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pre[class*=language-]{background-color:rgba(var(--ui-12-rgb),.5);color:var(--text-01);text-shadow:none;padding:var(--spacing-00);border-radius:var(--spacing-00);font-size:smaller}","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"},"responsivegrid":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"quickstart_hero":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"columnCount":12,":items":{"quickstart_hero":{"id":"quickstart-hero-93485c1181","quickstartHeroAuthor":"Snowflake","quickstartHeroBreadcrumbs":[{"title":"Automating Data Pipelines to Drive Marketing Analytics with Snowflake & Fivetran","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers/guides/vhol-fivetran","currentPage":true},{"title":"Guides","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers/guides","currentPage":false},{"title":"Snowflake for Developers","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers","currentPage":false}],"isDeveloperGuidesPage":false,"quickstartHeroTitle":{"lines":["Automating Data Pipelines to Drive Marketing Analytics with Snowflake & Fivetran"],"type":"heading2",":type":"snowflake-site/components/title-v2"},"quickstartHeroForkRepoLink":{"id":"button-65eb046273","showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/vhol-fivetran"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Fork 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business, so does the complexity involved in traditional approaches and architecture. Snowflake and Fivetran have partnered to bring you the Data Cloud and the most automated data integration solution which helps customers simplify data pipelines for all your businesses so you can focus on your data and analytics instead of infrastructure management and maintenance. In this virtual hands-on lab, you will follow a step-by-step guide to perform marketing analytics for Google Ads data by using Fivetran, Snowflake, and dbt. Let’s get started.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n","\u003Cp\u003ETo participate in the virtual hands-on lab, attendees need the following:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EGithub account - Participants will need to create, or already have, an account on Github. Other git-based source control hosting sites will work fine (Gitlab, Bitbucket), but the instructions will assume Github. An account on Github is free: \u003Ca href=\"https://github.com/join/\"\u003Elink\u003C/a\u003E.  See appendix 1 for step-by-step instructions.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ESnowflake Free Trial - Registrants of the virtual hands-on lab need to sign up for a \u003Ca href=\"https://signup.snowflake.com/?utm_cta=quickstarts_\"\u003Efree trial\u003C/a\u003E.  Please sign up using an email address that hasn’t been used previously. See appendix 2 for step-by-step instructions.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EOPTIONAL: Log in to your Google account, if you have one.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EOPTIONAL: Google Ads account credentials.\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ESample Google Ads data will be provided if you do not have a Google Ads account.\u003C/p\u003E\n","\u003Ch3\u003EWhat You'll Need During the Lab\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ca href=\"https://github.com/fivetran/snowflake_fivetran_vhol/blob/main/LAB_ASSETS/GSHEETS_LINKS.md\"\u003ESample Google Ads data\u003C/a\u003E - a public Google Sheet with three workbooks. Each workbook will become a table in our sample Google Ads data schema. The above link takes you to a page full of Google Sheets links -- all of these sheets are copies of the same data, you can use any link on that page.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ca href=\"https://github.com/fivetran/snowflake_fivetran_vhol\"\u003Edbt Project Github Repository\u003C/a\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/fivetran/snowflake_fivetran_vhol/raw/main/LAB_ASSETS/vhol_script.sql.zip\"\u003ESQL Script file\u003C/a\u003E - Participants will load \u003Ca href=\"https://github.com/fivetran/snowflake_fivetran_vhol/raw/main/LAB_ASSETS/vhol_script.sql.zip\"\u003Ethis file\u003C/a\u003E into a Snowflake worksheet when prompted during the lab.  Save this file where you can easily find it during the lab.\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EHow to begin a Fivetran trial through Snowflake's Partner Connect\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EHow to load Google Ads data into Snowflake using Fivetran\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EHow to run a dbt project via Fivetran to generate models\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EHow to create analyst sandboxes in Snowflake using clones\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EHow to leverage data in Snowflake's Data Marketplace\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA sophisticated marketing data analytics pipeline leveraging Fivetran, dbt, and Snowflake\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003ESnowflake Configuration\u003C/h2\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003ELogin to your Snowflake trial account.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image124.png\" alt=\"Snowflake Log In Screen\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EUI Tour (SE will walk through this live). For post-workshop participants, click \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-manager.html#quick-tour-of-the-web-interface\"\u003Ehere\u003C/a\u003E for a quick tour of the UI.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image29.png\" alt=\"Snowflake Worksheets\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ELet’s change our role and enable notifications. We need to work in the ACCOUNTADMIN role for this lab and notifications are how Snowflake will alert you when resource monitor thresholds have been crossed. Click on your User Name in the upper right-hand corner.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n<!--- ![Change role - step 1](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image27.png)  -->\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image27.png\" alt=\"assets/image27.png\"\u003E\u003C/p\u003E\n\u003Col start=\"4\"\u003E\u003Cli\u003EYou’ll get a popup with 4 items; click on Switch Role.\u003C/li\u003E\u003C/ol\u003E\n<!--- ![Change role - step 2](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image22.png)  -->\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image22.png\" alt=\"assets/image22.png\"\u003E\u003C/p\u003E\n\u003Col start=\"5\"\u003E\u003Cli\u003ESelect \u003Cstrong\u003EACCOUNTADMIN\u003C/strong\u003E.\u003C/li\u003E\u003C/ol\u003E\n<!--- ![Change role - step 3](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image62.png)  -->\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image62.png\" alt=\"assets/image62.png\"\u003E\u003C/p\u003E\n\u003Col start=\"6\"\u003E\u003Cli\u003EThe UI will refresh and you should see ACCOUNTADMIN under your username. If you don’t, go back to step 5.\u003C/li\u003E\u003C/ol\u003E\n<!--- ![Change role - step 4](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image30.png)  -->\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image30.png\" alt=\"assets/image30.png\"\u003E\u003C/p\u003E\n\u003Col start=\"7\"\u003E\u003Cli\u003EClick on your username again and you’ll get the same popup with 4 items; click on Preferences.\u003C/li\u003E\u003C/ol\u003E\n<!---![Preferences - step 1](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image59.png)  -->\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image59.png\" alt=\"assets/image59.png\"\u003E\u003C/p\u003E\n\u003Col start=\"8\"\u003E\u003Cli\u003E\n","\u003Cp\u003EClick on \u003Cstrong\u003ENotifications\u003C/strong\u003E.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image137.png\" alt=\"Preferences - step 2\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ESelect \u003Cstrong\u003EAll\u003C/strong\u003E, which will send notifications to your email address and this UI (in the Notifications tile in the upper right).\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image100.png\" alt=\"Preferences - step 3\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ENow let’s create your Fivetran account.  Click on the \u003Cstrong\u003EPartner Connect\u003C/strong\u003E tile at the top of the UI...\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n<!--- ![Create Fivetran Account - step 1](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image89.png)  -->\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image89.png\" alt=\"assets/image89.png\"\u003E\u003C/p\u003E\n\u003Col start=\"11\"\u003E\u003Cli\u003E\n","\u003Cp\u003E...and then click on the Fivetran tile inside Snowflake Partner Connect. (if you aren’t in the ACCOUNTADMIN role you’ll receive a warning.  Go back and complete steps 6-9.)\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image134.png\" alt=\"Create Fivetran Account - step 2\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick on \u003Cstrong\u003EConnect\u003C/strong\u003E.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image122.png\" alt=\"Create Fivetran Account - step 2\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick on \u003Cstrong\u003EActivate\u003C/strong\u003E in the pop-up that appears. This will open a new browser tab and take you to Fivetran where you will enter a password for the trial account.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image61.png\" alt=\"Create Fivetran Account - step 3\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EFivetran - Configure Source\u003C/h2\u003E\n<!-- ![Fivetran](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image79.png) --> \n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image79.png\" alt=\"assets/image79.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIn the next three sections we will create an automated data pipeline, with an extract-load (ELT) architecture::\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EExtract and Load:\n\u003Cul\u003E\u003Cli\u003EComplete Fivetran Account Setup\u003C/li\u003E\u003Cli\u003EUpload Sample Adwords Data using the Fivetran Google Sheets Connector\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003ETransform:\n\u003Cul\u003E\u003Cli\u003ECreate our first dbt project\u003C/li\u003E\u003Cli\u003ESetup Fivetran dbt Transformations\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003ELet’s get started!\u003C/p\u003E\n","\u003Ch3\u003EComplete Fivetran Account Setup\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003ECreate a password.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image107.png\" alt=\"Fivetran - Password\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EThat is it! Hang out on this screen until the next section.\nTo log into Fivetran in the future, you can navigate to \u003Ca href=\"https://fivetran.com/dashboard\"\u003Ehttps://fivetran.com/dashboard\u003C/a\u003E. Your email is the same as your Snowflake email (at the time you connected with Partner Connect) and your password is whatever you entered in Step 1 of this section.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image3.png\" alt=\"Fivetran - Source\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch2\u003EUpload Sample Adwords Data With Google Sheets\u003C/h2\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote\u003C/strong\u003E: The \u003Cstrong\u003ESetup Fivetran dbt Transformations\u003C/strong\u003E section assumes you have uploaded the sample data as outlined in this section. If you used your own Google Ads data, that is fine; you will just need to edit the dbt_project.yml file as discussed in the next section.\u003C/p\u003E\n","\u003Cp\u003EFor your convenience we have included sample Google Adwords data in the form of a Google Sheets containing three workbooks. Each workbook corresponds to a table to be synced to the Snowflake warehouse. This gives us the opportunity to explore another Fivetran feature, the Google Sheets connector. Google Sheets is, in fact, the most popular connector used by Fivetran customers. In the next section, we will:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EUse the Google Sheets connector to create a new Schema and Tables\u003C/li\u003E\u003Cli\u003EWe will create one Google Sheets connector per table.\u003C/li\u003E\u003Cli\u003EWe will use any one of the Google Sheet links found in this document: \u003Ca href=\"https://github.com/fivetran/snowflake_fivetran_vhol/blob/main/LAB_ASSETS/GSHEETS_LINKS.md\"\u003Elink\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EGoogle Sheets is the first connector in the list! Click on \u003Cstrong\u003EGoogle Sheets\u003C/strong\u003E.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image57.png\" alt=\"Fivetran - Source2\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EYou should see this screen.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image21.png\" alt=\"Fivetran - Google Sheet\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EPlease use the following values for the two fields:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EDestination schema: \u003Cstrong\u003Egoogle_ads_demo\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EDestination table: \u003Cstrong\u003Efinal_url_performance\u003C/strong\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image132.png\" alt=\"Fivetran - Google Sheet2\"\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EScroll down the page a bit, and paste the URL you just copied into the Sheet URL field. (\u003Ca href=\"https://github.com/fivetran/snowflake_fivetran_vhol/blob/main/LAB_ASSETS/GSHEETS_LINKS.md\"\u003EFollow these instructions to get a Google Sheets URL\u003C/a\u003E)\nThen, click the \u003Cstrong\u003EFIND SHEET\u003C/strong\u003E button. This step will take a few moments.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image118.png\" alt=\"Fivetran - Google Sheet3\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EIn the \u003Cstrong\u003ENamed Range\u003C/strong\u003E field, choose \u003Cstrong\u003Efinal_url_performance\u003C/strong\u003E.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image68.png\" alt=\"Fivetran - Google Sheet4\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003ESAVE & TEST\u003C/strong\u003E.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image18.png\" alt=\"Fivetran - Google Sheet5\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EWhen you see \u003Cstrong\u003EAll connection tests passed!\u003C/strong\u003E, click the \u003Cstrong\u003EVIEW CONNECTOR\u003C/strong\u003E button.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image42.png\" alt=\"Fivetran - Google Sheet6\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003EStart Initial Sync\u003C/strong\u003E. This will start the data sync into the warehouse! Now any updates to these sheets will be automatically synced to Snowflake.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image77.png\" alt=\"Fivetran - Google Sheet7\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EGreat! The Google Sheets connector is now syncing. Let’s setup the next table. Click the \u003Cstrong\u003EConnectors\u003C/strong\u003E menu.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image86.png\" alt=\"Fivetran - Google Sheet8\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick the \u003Cstrong\u003E+ Connector\u003C/strong\u003E button.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image36.png\" alt=\"Fivetran - Google Sheet9\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EPlease use the following values for the two fields:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EDestination schema: \u003Cstrong\u003Egoogle_ads_demo\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EDestination table: \u003Cstrong\u003Ecriteria_performance\u003C/strong\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image6.png\" alt=\"Fivetran - Google Sheet10\"\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EScroll down the page a bit, and paste the URL you just copied into the Sheet URL field. (Use the same Sheets URL as step 4, or \u003Ca href=\"https://github.com/fivetran/snowflake_fivetran_vhol/blob/main/LAB_ASSETS/GSHEETS_LINKS.md\"\u003Efollow these instructions to get a new Google Sheets URL\u003C/a\u003E.)\nThen, click the \u003Cstrong\u003EFIND SHEET\u003C/strong\u003E button. This step will take a few moments.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image126.png\" alt=\"Fivetran - Google Sheet11\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EIn the \u003Cstrong\u003ENamed Range\u003C/strong\u003E field, choose \u003Cstrong\u003Ecriteria_performance\u003C/strong\u003E. Click \u003Cstrong\u003ESAVE & TEST\u003C/strong\u003E.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image52.png\" alt=\"Fivetran - Google Sheet12\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EWhen the connection tests complete, click \u003Cstrong\u003EVIEW CONNECTOR\u003C/strong\u003E.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image42.png\" alt=\"Fivetran - Google Sheet13\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003EStart Initial Sync\u003C/strong\u003E. Then click \u003Cstrong\u003EConnectors\u003C/strong\u003E on the left-hand menu.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image38.png\" alt=\"Fivetran - Google Sheet14\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003E+ Connector\u003C/strong\u003E.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image108.png\" alt=\"Fivetran - Google Sheet15\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EPlease use the following values for the two fields:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EDestination schema: \u003Cstrong\u003Egoogle_ads_demo\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EDestination table: \u003Cstrong\u003Eclick_performance\u003C/strong\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image121.png\" alt=\"Fivetran - Google Sheet16\"\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EScroll down the page a bit, and paste the URL you just copied into the Sheet URL field. (Use the same Sheets URL as step 4, or \u003Ca href=\"https://github.com/fivetran/snowflake_fivetran_vhol/blob/main/LAB_ASSETS/GSHEETS_LINKS.md\"\u003Efollow these instructions to get a new Google Sheets URL\u003C/a\u003E.)\nThen, click the \u003Cstrong\u003EFIND SHEET\u003C/strong\u003E button. This step will take a few moments.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image95.png\" alt=\"Fivetran - Google Sheet17\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EIn the \u003Cstrong\u003ENamed Range\u003C/strong\u003E field, choose \u003Cstrong\u003Eclick_performance\u003C/strong\u003E. Click \u003Cstrong\u003ESAVE & TEST\u003C/strong\u003E.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image64.png\" alt=\"Fivetran - Google Sheet18\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EWhen the connection tests complete, click \u003Cstrong\u003EVIEW CONNECTOR\u003C/strong\u003E.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image42.png\" alt=\"Fivetran - Google Sheet19\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003EStart Initial Sync\u003C/strong\u003E. Then click \u003Cstrong\u003EConnectors\u003C/strong\u003E on the left-hand menu.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image117.png\" alt=\"Fivetran - Google Sheet20\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ECongratulations! You can see your 3 Google Sheets connectors running, each creating one of our three sample data tables. Click on \u003Cstrong\u003EGoogle Sheets (3)\u003C/strong\u003E.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image75.png\" alt=\"Fivetran - Google Sheet21\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EThis view shows you all of your Google Sheets connectors. Some of your connectors may still be syncing, that is fine; we will have time for them to complete while we perform the next part of the lab.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image48.png\" alt=\"Fivetran - Google Sheet22\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIn Snowflake (optional): When the sync is complete, you can see your schema and tables in Snowflake’s Worksheet view. You should be able to see the data in the tables by clicking into the \u003Cstrong\u003EPC_FIVETRAN_DB\u003C/strong\u003E database, into the \u003Cstrong\u003EGOOGLE_ADS_DEMO\u003C/strong\u003E schema, then clicking on one of the tables and then clicking \u003Cstrong\u003EPreview Data\u003C/strong\u003E.\u003C/p\u003E\n","\u003Ch2\u003EFork Sample dbt Project on Github\u003C/h2\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote\u003C/strong\u003E: The \u003Cstrong\u003ESetup Fivetran dbt Transformations\u003C/strong\u003E section assumes you have uploaded the sample data as outlined in the previous section. If you use your own Google Ads data, that is fine; you will just need to edit the dbt_project.yml file as discussed in this section.\u003C/p\u003E\n","\u003Cp\u003EFor your convenience, we have created a sample dbt Github project that is already configured to work with Fivetran’s \u003Ca href=\"https://hub.getdbt.com/fivetran/google_ads_source/latest/\"\u003Edbt_google_ads_source\u003C/a\u003E and \u003Ca href=\"https://hub.getdbt.com/fivetran/google_ads/latest/\"\u003Edbt_google_ads\u003C/a\u003E packages. You can find the sample repository at the following link:\nhttps://github.com/fivetran/snowflake_fivetran_vhol\u003C/p\u003E\n","\u003Cp\u003EThis section assumes that you have created a Github account and are logged into that account. In this section we are going to:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EFork the sample github repository.\u003C/li\u003E\u003Cli\u003EExplore the files in the project.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EWhen you click \u003Ca href=\"https://github.com/fivetran/snowflake_fivetran_vhol\"\u003Ehttps://github.com/fivetran/snowflake_fivetran_vhol\u003C/a\u003E and you are logged in, you should see the following screen. Notice the user / owner is \u003Cstrong\u003Efivetran\u003C/strong\u003E and the repository is named \u003Cstrong\u003Esnowflake_fivetran_vhol\u003C/strong\u003E.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EOn the upper right (but below your user icon) is a \u003Cstrong\u003EFork\u003C/strong\u003E button. Click it!\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image10.png\" alt=\"Fork repo - 1\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EIf you have access to multiple github accounts, you will have the option to select into which account to fork this repository. Choose your personal user account.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image116.png\" alt=\"Fork repo - 2\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EThat's it! Notice in the screenshot above the user / owner is \u003Cstrong\u003Ecrw\u003C/strong\u003E and the repository is (still) named \u003Cstrong\u003Esnowflake_fivetran_vhol\u003C/strong\u003E - but now that repository is under your personal Github account. I can now manipulate this repository however I like, without impacting the sample repository.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image105.png\" alt=\"Fork repo - 3\"\u003E\u003C/p\u003E\n","\u003Cp\u003ECongrats! You have successfully created your first dbt project! That was easy!\n\u003Cstrong\u003EThe next section is OPTIONAL and not required for the workshop.\u003C/strong\u003E It is included for your information only.\u003C/p\u003E\n","\u003Cp\u003EThere are three very important configuration files to be aware, all at the top level of the file tree.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003Edbt_project.yml\u003C/strong\u003E - general dbt project settings.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003Epackages.yml\u003C/strong\u003E - include packages that bring functionality to your dbt project, like those provided by Fivetran to enhance your data!\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003Edeployment.yml\u003C/strong\u003E - a Fivetran-specific configuration file that controls how frequently the dbt jobs run.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Ca href=\"https://github.com/crw/snowflake_fivetran_vhol/blob/main/dbt_project.yml\"\u003Edbt_project.yml\u003C/a\u003E:\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image24.png\" alt=\"Fork repo - 4\"\u003E\u003Cbr\u003E\nVarious configuration settings are found in this file. Please see \u003Ca href=\"https://docs.getdbt.com/reference/dbt_project.yml\"\u003Edbt’s documentation\u003C/a\u003E for more information on these settings.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDid You Know?\u003C/strong\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Chr\u003E\n","\u003Cp\u003EAs previously mentioned, \u003Cstrong\u003Eif you want to use your own Google Ads data\u003C/strong\u003E, you will need to edit \u003Cstrong\u003Edbt_project\u003C/strong\u003E.yml. You can edit files directly in Github using the pencil icon at the top of the file view (mid-upper right, to the right of the Blame button). All you need to do is remove or comment out the lines that say:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E    vars\n        google_ad_schema: 'GOOGLE_ADS_DEMO'\n\u003C/code\u003E\u003C/pre\u003E\n\u003Chr\u003E\n","\u003Cp\u003EThat’s it! Remove those lines and you are good to go. This assumes, of course, that you loaded your own Google Ads data in the previous section! If you have an \u003Cstrong\u003EADWORDS\u003C/strong\u003E schema in your Snowflake warehouse, you successfully loaded Google Ads data.\u003C/p\u003E\n","\u003Cp\u003E\u003Ca href=\"https://github.com/crw/snowflake_fivetran_vhol/blob/main/packages.yml\"\u003Epackages.yml\u003C/a\u003E:\u003Cbr\u003E\nThis is where you can include outside packages to run! See \u003Ca href=\"https://hub.getdbt.com/\"\u003Ehub.getdbt.com\u003C/a\u003E for a complete list and installation instructions. As of this writing, Fivetran has created over 17 packages, including packages for Netsuite, Salesforce, Twitter Ads, Marketo, Zendesk, Mailchimp, and more!\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image32.png\" alt=\"Fork repo - 5\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Ca href=\"https://github.com/crw/snowflake_fivetran_vhol/blob/main/deployment.yml\"\u003Edeployment.yml\u003C/a\u003E:\u003Cbr\u003E\nThis is a Fivetran-specific file that configures what jobs will run when, leveraging a \u003Ca href=\"https://crontab.guru/\"\u003Ecrontab-style syntax\u003C/a\u003E for scheduling. Documentation can be found in the file itself. Remember, as described above, you can edit files directly in Github!\u003C/p\u003E\n","\u003Ch2\u003ESet Up Fivetran dbt Transformations\u003C/h2\u003E\n","\u003Cp\u003EIn this section we will take the dbt project we created in the previous section, and run it via Fivetran to generate models! We will be showing how Fivetran is used to orchestrate dbt jobs, and the first step is to connect to your dbt project in Github. There are a lot of steps in this section, but for the most part they are not too complicated. Let’s get started!\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote: dbt Transformations in Fivetran are in beta\u003C/strong\u003E as of this writing. Depending how you signed up for your Fivetran trial, you may or may not see the option to “Try dbt Transformations”. If you do not see this option, please reach out to us and we will try to get you enabled to try this feature.\u003C/p\u003E\n","\u003Cp\u003EIf you see this screen, please reach out to Fivetran sales -- we can enable dbt Transformations beta for your account.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image12.png\" alt=\"Transformations - 1\"\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EIf you have not already, \u003Cstrong\u003Eswitch back to your Fivetran tab in your web browser\u003C/strong\u003E.\nFirst click on the \u003Cstrong\u003ETransformations\u003C/strong\u003E section of your Fivetran interface. You should see the following interface. We will be clicking the button labeled \u003Cstrong\u003ETry dbt Transformations\u003C/strong\u003E.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image51.png\" alt=\"Transformations - 2\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003EEnable dbt Transformations\u003C/strong\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image72.png\" alt=\"Transformations - 3\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003EI am ready to connect my Git repo\u003C/strong\u003E.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image104.png\" alt=\"Transformations - 4\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ENow we will configure the connection to Github. Next to \u003Cstrong\u003EPublic Key\u003C/strong\u003E, click \u003Cstrong\u003ECopy to Clipboard\u003C/strong\u003E (the papers icon).\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image92.png\" alt=\"Transformations - 5\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EOn the Github repository we created above (your_username/snowflake_fivetran_vhol) click on \u003Cstrong\u003ESettings\u003C/strong\u003E (farthest-right of the tabs under the repository name.)\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image49.png\" alt=\"Transformations - 6\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EOn the left-hand navigation, click on \u003Cstrong\u003EDeploy Keys\u003C/strong\u003E (lower half, under Integrations, above Secrets).\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image102.png\" alt=\"Transformations - 7\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003EAdd deploy key\u003C/strong\u003E, on the mid-upper-right hand side of the screen.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image98.png\" alt=\"Transformations - 8\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EGive the deploy key a memorable, distinct title, like \u003Cstrong\u003EFivetran dbt access\u003C/strong\u003E.\nPaste the previously-copied deploy key in the \u003Cstrong\u003EKey\u003C/strong\u003E field.\n\u003Cstrong\u003EDO NOT\u003C/strong\u003E check “Allow write access” - currently Fivetran only requires read access to the repository.\nClick \u003Cstrong\u003EAdd key\u003C/strong\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image128.png\" alt=\"Transformations - 9\"\u003E\u003Cbr\u003E\nPhew! Done! If at any time you want to revoke Fivetran’s access to this repository, come back to this screen and delete the deploy key we just created.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EBefore leaving Github, return to the \u003Cstrong\u003ECode\u003C/strong\u003E tab.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image111.png\" alt=\"Transformations - 10\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ENow click the green \u003Cstrong\u003ECode\u003C/strong\u003E button. Make sure the \u003Cstrong\u003ESSH\u003C/strong\u003E tab is selected, and copy the:\n\u003Cstrong\u003Egit@github.com:your_username/snowflake_fivetran_vhol.git\u003C/strong\u003E URL. We will need it on the next step.\n\u003Cstrong\u003ENote\u003C/strong\u003E: it is safe to ignore errors about not having an SSH key configured here. The Deploy Key we configured will give Fivetran access to this repository.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image93.png\" alt=\"Transformations - 10\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EPaste the value into the \u003Cstrong\u003ERepository URL\u003C/strong\u003E field.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image13.png\" alt=\"Transformations - 10\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EFor \u003Cstrong\u003EDefault Schema Name\u003C/strong\u003E field, enter \u003Cstrong\u003EGOOGLE_ADS_DBT\u003C/strong\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image2.png\" alt=\"Transformations - 10\"\u003E\u003C/p\u003E\n","\u003Cp\u003EAs of this writing, you need to perform the next actions to continue.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote\u003C/strong\u003E: Github has changed their default branch name to “main”. This change was made very recently, and Fivetran has not yet caught up to the new naming convention. For the time being, for this guide, we will need to manually change the branch name.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n<!--- FDP: Wish the above could be done as a positive box -->\n\u003Col start=\"13\"\u003E\u003Cli\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003EShow Advanced Options\u003C/strong\u003E.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image67.png\" alt=\"Transformations - 10\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EChange the \u003Cstrong\u003EGit branch\u003C/strong\u003E field’s value to \u003Cstrong\u003Emain\u003C/strong\u003E.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image133.png\" alt=\"Transformations - 10\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003ESAVE\u003C/strong\u003E. Then when you see the green success message, click \u003Cstrong\u003ETRANSFORMATIONS\u003C/strong\u003E.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image1.png\" alt=\"Transformations - 10\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EThat’s it! Your dbt project  is now configured to run and be orchestrated with Fivetran. In the upper right corner, there is a toggle switch, \u003Cstrong\u003EActivate dbt Transformations\u003C/strong\u003E. Click this switch to start your transformations running.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image33.png\" alt=\"Transformations - 10\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ENow if left to their own devices, your transformations will run on the schedules listed below. As a reminder, these schedules are configured in the \u003Ca href=\"https://github.com/crw/snowflake_fivetran_vhol/blob/main/deployment.yml\"\u003Edeployment.yml\u003C/a\u003E file, as discussed in the previous section. But let’s run one right away! Click on the \u003Cstrong\u003Eweekdays\u003C/strong\u003E line item.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image109.png\" alt=\"Transformations - 10\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EIn the upper right corner, click \u003Cstrong\u003ERun now\u003C/strong\u003E (the circular arrow icon.)\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image43.png\" alt=\"Transformations - 10\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EThe dbt job runs successfully! Click on the line item with the green checkmark next to it.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image66.png\" alt=\"Transformations - 10\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EHere we can see the results of running the dbt job. Click on the down arrow (caret) next to run models.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image50.png\" alt=\"Transformations - 10\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EThis exposes the results of running the dbt job. If you scroll to the bottom of the page, you can see the job ran successfully.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image74.png\" alt=\"Transformations - 10\"\u003E\u003C/p\u003E\n","\u003Cp\u003ECongratulations! You have successfully run your first dbt job!\u003C/p\u003E\n","\u003Cp\u003EIn Snowflake (optional): We can see the results of all of this hard work in the Snowflake Worksheets view. On the left hand column, you can see the GOOGLE_ADS_DBT schema which has the results of running our dbt job: fresh models, produced simply by including Fivetran’s open source dbt_google_ads package! We are that much closer to exposing the true value of this data!\u003C/p\u003E\n","\u003Cp\u003EThe tables created by these models do the following:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EGOOGLE_ADS__CRITERIA_AD_ADAPTER\u003C/strong\u003E\n\u003Cul\u003E\u003Cli\u003EEach record represents the daily ad performance of each criteria in each ad group and campaign.\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EGOOGLE_ADS__URL_AD_ADAPTER\u003C/strong\u003E\n\u003Cul\u003E\u003Cli\u003EEach record represents the daily ad performance of each URL in each ad group, including information about the used UTM parameters.\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EGOOGLE_ADS__CLICK_PERFORMANCE\u003C/strong\u003E\n\u003Cul\u003E\u003Cli\u003EEach record represents a click, with a corresponding Google click ID (gclid).\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDid You Know?\u003C/strong\u003E\nAll of the code that produces these models is open source! Fivetran officially supports over 20 open source dbt packages that model data delivered by Fivetran, typically to provide commonly required aggregations. You can see all of the available packages at \u003Ca href=\"https://hub.getdbt.com/\"\u003Ehttps://hub.getdbt.com/\u003C/a\u003E - scroll down to the Fivetran section.\u003C/p\u003E\n","\u003Cp\u003EThe two packages we use in this lab are the Google Ads packages:\n\u003Ca href=\"https://hub.getdbt.com/fivetran/google_ads_source/latest/\"\u003Egoogle_ads_source\u003C/a\u003E \u003Ca href=\"https://github.com/fivetran/dbt_google_ads_source\"\u003Esource code on github\u003C/a\u003E\n\u003Ca href=\"https://hub.getdbt.com/fivetran/google_ads/latest/\"\u003Egoogle_ads\u003C/a\u003E \u003Ca href=\"https://github.com/fivetran/dbt_google_ads\"\u003Esource code on github\u003C/a\u003E\u003C/p\u003E\n","\u003Cp\u003EYou can save considerable development time by leveraging Fivetran’s open source dbt packages with data delivered by Fivetran. Now that you have a running dbt project, feel free to try including more packages!\u003C/p\u003E\n\u003Chr\u003E\n","\u003Cp\u003EThe summary so far: Maintenance-free data pipelines and powerful in-warehouse transformations\u003C/p\u003E\n","\u003Cp\u003EIn the previous sections, we learned the following skills:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EHow to setup a Fivetran connector (in this case, a Google Sheets connector)\u003C/li\u003E\u003Cli\u003EHow to create a dbt project by forking the snowflake_fivetran_vhol sample repository\u003C/li\u003E\u003Cli\u003EHow to enable dbt Transformations in Fivetran by connecting our repository\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EIn doing so, we have set up a complete, end-to-end modern data pipeline focusing on the advantages of the Extract-Load-Transform (ELT) architectural approach. Data loading is extremely simple to configure and comes with the benefit of hands-free maintenance, forever. dbt is a powerful transformation tool that comes with many open source Fivetran modeling packages out-of-the-box. Moreover, Fivetran manages the execution of those transformation jobs, and the jobs run natively in the Snowflake warehouse, exposing all of the pßower of Snowflake’s query engine to your transformations.\u003C/p\u003E\n","\u003Ch2\u003ESnowflake - Part Two\u003C/h2\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EBack in the Snowflake UI, let’s take a quick look at the query history to see all the work that Fivetran/dbt performed.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image53.png\" alt=\"Snowflake Part 2 - 1\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick on \u003Cstrong\u003EAdd a filter\u003C/strong\u003E.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image20.png\" alt=\"Snowflake Part 2 - 2\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EChange the left box to User and select PC_FIVETRAN_USER in the right box.  All of the Fivetran/dbt queries executed as this user.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image69.png\" alt=\"Snowflake Part 2 - 3\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EIn the table below you can see all of these queries. Scroll down. Click on a SQL Text value and you’ll see a pop-up of the specific SQL statement. You can read about this UI screen \u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-history.html\"\u003Ehere\u003C/a\u003E.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image65.png\" alt=\"Snowflake Part 2 - 4\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick on the \u003Cstrong\u003EWorksheets\u003C/strong\u003E tile at the top.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image81.png\" alt=\"Snowflake Part 2 - 5\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EYour screen will look like this.  This is where we will do most of our remaining work.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image58.png\" alt=\"Snowflake Part 2 - 6\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ENow load the script (vhol_script.sql) by clicking on the button and then selecting in the pop-up.  Navigate your computer and find the script.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image135.png\" alt=\"Snowflake Part 2 - 7\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EThe script should appear in your worksheet like this.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image94.png\" alt=\"Snowflake Part 2 - 8\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ENotice on the left-hand side is a database browser with a database called PC_FIVETRAN_DB.  This is the database that you loaded with Fivetran.  Click on it and you will see the schemas.  GOOGLE_ADS_DEMO is where the data initially loaded and GOOGLE_ADS_DBT is the schema created by the dbt process.  Click on GOOGLE_ADS_DBT and you’ll see the tables and views that live within.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image37.png\" alt=\"Snowflake Part 2 - 9\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ELet’s start our work performing DBA functions.   We need to set the context we want to use within the worksheet.  In each worksheet I can have a different role,  warehouse, database, and schema.  Each worksheet is independent of the others (like Snowflake compute!).  My worksheet role can also be different from the role I have in the upper right-hand corner under my name.  <br><br>\u003C/p\u003E\n","\u003Cp\u003ELet’s execute the SQL in script section \u003Cstrong\u003EA\u003C/strong\u003E to set our worksheet context.  To do so, highlight the two lines (\u003Cem\u003Euse role..., use schema...\u003C/em\u003E) and click the \u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image136.png\" alt=\"assets/image136.png\"\u003E button.  <br><br>\u003C/p\u003E\n","\u003Cp\u003E\u003Cem\u003E\u003Cstrong\u003EPro Tip: You may get a pop-up asking if you want to run both statements.  If you don’t want this warning every time, click in the check box and click OK.\u003C/strong\u003E\u003C/em\u003E\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode\u003E/*-----\n--A--\n-----\n--Let's set our worksheet role and db/schema*/\nuse role ACCOUNTADMIN;\nuse schema PC_FIVETRAN_DB.GOOGLE_ADS_DBT;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode\u003ENotice that your worksheet context now has a default role, database, and schema but no warehouse (yet).  \n![Snowflake Part 2 - 11](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image80.png)  \n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"11\"\u003E\u003Cli\u003E\n","\u003Cp\u003ELet’s pretend that the data we loaded and transformed with Fivetran is our production data.  We only want it to be changed by our Fivetran process.  Since our analysts will want to experiment with and change the data,  we’ll \u003Cstrong\u003ECLONE\u003C/strong\u003E the entire PC_FIVETRAN_DB database so they can have their own sandbox.  This is a logical copy; it will consume no additional space unless the data in the clone starts to change.  This is a fantastic tool for creating as many sandboxes as you need.  It also works well for making time-specific snapshots.  <br><br>\u003C/p\u003E\n","\u003Cp\u003E\u003Cem\u003E\u003Cstrong\u003ECloning reduces time to value  - full production clones are fast and support more accurate analytical results because they are rich and complete data sets.\u003C/strong\u003E\u003C/em\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ERun the \u003Cstrong\u003ECreate Database\u003C/strong\u003E SQL and the \u003Cstrong\u003EUse Schema\u003C/strong\u003E commands in section \u003Cstrong\u003EB\u003C/strong\u003E to make a clone, then refresh the database browser with the (small) \u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image130.png\" alt=\"Refresh\"\u003E button:\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode\u003E/*-----\n--B--\n-----\n--Make a new DEV db and schema and then clone the 3 key tables for isolated analysis and experimentation*/\ncreate database GOOGLE_ADS_DEV clone PC_FIVETRAN_DB;\nuse schema GOOGLE_ADS_DEV.GOOGLE_ADS_DBT;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode\u003ENow your analysts have GOOGLE_ADS_DEV - a complete clone of PC_FIVETRAN_DB.  \n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"13\"\u003E\u003Cli\u003ENext we need to create a warehouse (compute) for the analysts. For this workshop we’ll create an Extra Small - the smallest unit of Snowflake compute - with an auto suspend time of 2 minutes and auto resume enabled.  Auto resume means it will start up when a request is made.  Run the SQL in section \u003Cstrong\u003EC\u003C/strong\u003E to create the warehouse.  Notice that this warehouse is now in your worksheet context.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode\u003E/*-----\n --C--\n -----\n --Create a virtual warehouse (compute) for the Marketing Analyst team to do their work*/\ncreate or replace warehouse MKT_ANALYSIS with warehouse_size = 'XSMALL' auto_suspend = 120 --seconds\n  auto_resume = TRUE;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode\u003E_**Snowflake compute is unique.  It’s (1) easy to define and manage; (2) fast to activate and suspend; (3) self-suspending; (4) self-healing; (5) isolated from the storage layer; (6) isolated  from other compute; (7) quick to scale up/down and out/in; (8) non-disruptive when any of this is happening.**_\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"14\"\u003E\u003Cli\u003EFor our last act as DBA we’ll create a \u003Cstrong\u003Eresource monitor\u003C/strong\u003E to track warehouse consumption.  Resource monitors are convenient tools for setting time-based consumption thresholds on compute at the warehouse or account level.  You can set alerts for various thresholds and even prevent a warehouse from running once it reaches a credit value that you set.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode\u003E/*-----\n --D--\n -----\n --Create a resource monitor to track credit usage of the marketing virtual warehouse*/\ncreate or replace RESOURCE MONITOR "MARKETING_ANALYSIS" with CREDIT_QUOTA = 20,\n  frequency = 'DAILY',\n  start_timestamp = 'IMMEDIATELY',\n  end_timestamp = null triggers \n  ON 100 PERCENT DO SUSPEND \n  ON 5 PERCENT do NOTIFY \n  ON 10 PERCENT do NOTIFY \n  ON 50 PERCENT do NOTIFY \n  ON 90 PERCENT do NOTIFY;\nalter WAREHOUSE "MKT_ANALYSIS"\nset RESOURCE_MONITOR = "MARKETING_ANALYSIS";\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode\u003ERun the two SQL statements in section **D** to create a resource monitor that has a daily 20 credit limit, sends an alert at 5%, 10%, 50%, and 99% thresholds, and suspends the warehouse at 100% (20 credits).  The second statement associates the resource monitor to the warehouse.   \n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"15\"\u003E\u003Cli\u003ENow we’ll switch to ‘Analyst mode’. In real life this would be a different person with a different userid and a different Role.  An analyst might write a query like the one in section \u003Cstrong\u003EE\u003C/strong\u003E of the script to answer the question “What was the best performing cranberry sauce campaign?” Go ahead and run it.  We’re working with tiny data sets in the lab but in reality we might run over millions or billions of rows.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode\u003E/*-----\n --E--\n -----\n --What was the best performing cranberry sauce campaign?*/\nselect ad_group_name,\n  campaign_name,\n  sum(spend) as TOT_SPEND,\n  sum(clicks) as TOT_CLICKS,\n  sum(impressions) as TOT_IMPRESSIONS,\n  sum(impressions) / sum(spend) as IMPRESSIONS_PER_DOLLAR,\n  sum(spend) / sum(clicks) as cost_per_click\nfrom GOOGLE_ADS__URL_AD_ADAPTER\nwhere ad_group_name = 'cranberry sauce'\ngroup by ad_group_name,\n  campaign_name\norder by 6 desc;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"16\"\u003E\u003Cli\u003E\n","\u003Cp\u003EThese are interesting results, but you have a hypothesis:  clicks increase with snowy weather.  Let’s go to the Snowflake Marketplace and find what we need.  The Data Marketplace lives in the new UI called Snowsight (currently in Preview mode but feel free to test drive after the lab).  Click on Preview App at the top of the UI.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image11.png\" alt=\"Snowflake Part 2 - 16\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003ESign in to continue\u003C/strong\u003E.  You will need to use the same user and pw that you used to login to your Snowflake account the first time.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image17.png\" alt=\"Snowflake Part 2 - 17\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EYou’re now in the new UI - Snowsight.  It’s pretty cool - with charting and dashboards and context-sensitivity - but today we’re just focused on getting to the Data Marketplace.  Click on \u003Cstrong\u003EData\u003C/strong\u003E…\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image23.png\" alt=\"Snowflake Part 2 - 18\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E...and then \u003Cstrong\u003EMarketplace\u003C/strong\u003E…\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image55.png\" alt=\"Snowflake Part 2 - 19\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E...and now you’re in!  Over 100 providers have made datasets available for you to enrich your data.  Today we’re going to grab a sample of weather data.  Click the \u003Cstrong\u003EReady to Query\u003C/strong\u003E checkbox…\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image82.png\" alt=\"Snowflake Part 2 - 20\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EAnd then find the \u003Cstrong\u003EWeather Source\u003C/strong\u003E tile.  Once you find it, click on it.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image84.png\" alt=\"Snowflake Part 2 - 21\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EHere you’ll find a description of the data, example queries, and other useful information.  Let’s get this data into our Snowflake account.  You’ll be amazed at how fast and easy this is.  Click \u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image40.png\" alt=\"assets/image40.png\"\u003E.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image56.png\" alt=\"Snowflake Part 2 - 22\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EIn the pop-up, rename the database to WEATHERSOURCE (\u003Cstrong\u003Eimportant!\u003C/strong\u003E),  check the “I accept…” box and then \u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image26.png\" alt=\"assets/image26.png\"\u003E.  No need to add additional roles (though you might in real-life).\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image28.png\" alt=\"Snowflake Part 2 - 24\"\u003E\u003C/p\u003E\n","\u003Cp\u003EWhat is happening here?  Weather Source has granted access to this data from their Snowflake account to yours.  You’re creating a new database in your account for this data to live - but the best part is that no data is going to move between accounts!  When you query you’ll really be querying the data that lives in the Weather Source account.  If they change the data you’ll automatically see those changes.  No need to define schemas, move data,  or create a data pipeline either. Isn’t that slick?\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ENow click \u003Cstrong\u003EDone\u003C/strong\u003E. You can close this browser tab and go back to the other Snowflake UI that you started in.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image31.png\" alt=\"Snowflake Part 2 - 25\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ERefresh the database browser and notice you have a new shared database, ready to query and join with your data. Click on it and you’ll see views under the PUBLIC schema. We’ll use one of these next.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image25.png\" alt=\"Snowflake Part 2 - 26\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EWith production-sized data sets we are likely to have very large tables and complex queries; therefore, we might want to increase the amount of compute we’re using to perform this analysis.  In Snowflake this is fast and easy.  Run the statement in section \u003Cstrong\u003EG\u003C/strong\u003E.  Notice how fast it scales up. No disruption. No shuffling of data.  We’ve changed from an XS (one node cluster) to an XL (16 node cluster) in seconds.  Ready to go!\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode\u003E/*-----\n--G--\n----- \n--With production-sized data sets we might want to join and query tables with billions of rows\n--In these cases it's easy and productive to scale up our virtual warehouse so these queries run faster*/\nalter warehouse mkt_analysis set warehouse_size = 'XLARGE';\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"27\"\u003E\u003Cli\u003ESo, let’s join that weather data to our Google Ads data and see if there is a correlation between clicks and snowfall.  Run the correlation query in section \u003Cstrong\u003EH\u003C/strong\u003E. Remember that we’re actually joining data across two accounts, but the experience and performance is seamless and fast.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode\u003E/*-----\n --H--\n -----\n --Is there any correlation between clicks and snowfall for any ad group?*/\nselect ad_group_name,\n  abs(corr(clicks, w.tot_snowfall_in)) as clicks_snow_corr\nfrom GOOGLE_ADS__URL_AD_ADAPTER ga,\n  "WEATHERSOURCE"."PUBLIC"."HISTORY_DAY" w\nwhere ga.date_day = w.date_valid_std\ngroup by 1\norder by 2 desc;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode\u003EIt’s a weak correlation so maybe our hypothesis isn’t worth exploring further. But our ability to quickly source Marketplace data and mash it with our own data has saved us lots of time and we can iterate further.\n![Snowflake Part 2 - 29](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image125.png)  \n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"28\"\u003E\u003Cli\u003EBecause this combination may yet have value, let’s create a view that combines the correlations with the URL data so others can take a look more easily.  Run the SQL in section \u003Cstrong\u003EI\u003C/strong\u003E.  One creates the view and the other queries it.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode\u003E/*-----\n --I--\n -----\n --Let's create new view with the correlations built in to the URL_AD_ADAPTER data*/\ncreate or replace view GOOGLE_ADS__URL_AD_ADAPTER_CORR as (\n    with corr as (\n      select ad_group_name,\n        abs(corr(clicks, w.tot_snowfall_in)) as clicks_snow_corr\n      from GOOGLE_ADS__URL_AD_ADAPTER ga,\n        "WEATHERSOURCE"."PUBLIC"."HISTORY_DAY" w\n      where ga.date_day = w.date_valid_std\n      group by 1\n    ),\n    base as (\n      select *\n      from GOOGLE_ADS__URL_AD_ADAPTER\n    )\n    select b.*,\n      c.clicks_snow_corr\n    from base b,\n      corr c\n    where b.ad_group_name = c.ad_group_name\n  );\nselect *\nfrom GOOGLE_ADS__URL_AD_ADAPTER_CORR;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"29\"\u003E\u003Cli\u003ENow that our heavy lifting is done, let’s scale down the warehouse size. No need to pay for more compute than we need. Run the SQL in section \u003Cstrong\u003EJ\u003C/strong\u003E.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode\u003E/*-----\n--J--\n-----\n--Once we're done with our complex queries we scale the warehouse back down*/\nalter warehouse mkt_analysis set warehouse_size = 'XSMALL';\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"30\"\u003E\u003Cli\u003EFor our last trick let’s imagine we’ve accidentally dropped the table we’re working with.  No one has ever done that!  Run the DROP statement in section \u003Cstrong\u003EK\u003C/strong\u003E and then refresh the database browser.  Notice the table is gone.  Now run the UNDROP and refresh.  It’s back! This is the power of time travel.  You can restore lost data for up to 90 days of history.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode\u003E/*-----\n--K--\n-----\n--Let's look at one last great feature of Snowflake.  Have you ever accidentally dropped a table?  Or a database?  \n--Let's 'accidentally' drop a table.*/\ndrop table GOOGLE_ADS__URL_AD_ADAPTER; \n\n/*--Fortunately, in Snowflake, this is an easy fix.*/\nundrop table GOOGLE_ADS__URL_AD_ADAPTER;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode\u003E: _Time Travel also enables you to query and replace tables as of a particular point in time or prior to a specific SQL statement._  \n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"31\"\u003E\u003Cli\u003E\n","\u003Cp\u003EOne last thing. How is our Snowflake consumption looking?  It’s easy to understand.  We’ll look in two places. First, you might have a Notification, so click on that tile.  When you have notifications enabled and resource monitors built, you’ll get notifications.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image123.png\" alt=\"Snowflake Part 2 - 32\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ESee anything that looks like this?  These notifications come from the resource monitor we set up earlier in the lab.  You’ll also get emails with the same information.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image101.png\" alt=\"Snowflake Part 2 - 33\"\u003E\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image101.png\" alt=\"Snowflake Part 2 - 33\"\u003E<!-- .element style="width: 25%; border: 0; background: None; box-shadow: None" -->\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image101.png\" alt=\"assets/image101.png\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ENow, click on the \u003Cstrong\u003EAccount\u003C/strong\u003E tile.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image16.png\" alt=\"Snowflake Part 2 - 34\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EThen click on \u003Cstrong\u003EUsage\u003C/strong\u003E.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image91.png\" alt=\"Snowflake Part 2 - 35\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EHere you can see monthly/daily credit usage…\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image19.png\" alt=\"Snowflake Part 2 - 36\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E...and monthly/daily storage usage (you won’t see much for storage yet since our data is small).  In addition to these screens you can query the Snowflake metadata layer for the same info and build reports in your favorite BI tool.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image35.png\" alt=\"Snowflake Part 2 - 37\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cstrong\u003EThis concludes the lab.  We hope you learned a lot and are interested in exploring Fivetran and Snowflake for your use cases.  Please feel free to ask questions.\u003C/strong\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion & Next Steps\u003C/h2\u003E\n","\u003Cp\u003ECongratulations on completing this lab on automating data pipelines to drive marketing analytics! You’ve mastered the Fivetran and Snowflake basics and are ready to apply these fundamentals to your own data. Be sure to reference this guide if you ever need a refresher.\u003C/p\u003E\n","\u003Cp\u003EWe encourage you to continue with your free trials by loading your own sample or production data and by using some of the more advanced capabilities of Fivetran and Snowflake not covered in this lab.\u003C/p\u003E\n","\u003Ch3\u003EAdditional Resources:\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ERead the \u003Ca href=\"/test-driving-snowflake-the-definitive-guide-to-maximizing-your-free-trial/\"\u003EDefinitive Guide to Maximizing Your Free Trial\u003C/a\u003E document\u003C/li\u003E\u003Cli\u003EAttend a \u003Ca href=\"/about/events/\"\u003ESnowflake virtual or in-person event\u003C/a\u003E to learn more about our capabilities and customers\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://community.snowflake.com/s/topic/0TO0Z000000wmFQWAY/getting-started-with-snowflake\"\u003EJoin the Snowflake community\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://community.snowflake.com/s/article/Getting-Access-to-Snowflake-University\"\u003ESign up for Snowflake University\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/free-trial-contact-sales/\"\u003EContact our Sales Team\u003C/a\u003E to learn more\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat we've covered:\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EHow to begin a Fivetran trial through Snowflake's Partner Connect\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EHow to load Google Ads data into Snowflake using Fivetran\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EHow to run a dbt project via Fivetran to generate models\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EHow to create analyst sandboxes in Snowflake using clones\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EHow to leverage data in Snowflake's Data Marketplace\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003EAppendix I: Github Account Creation\u003C/h2\u003E\n","\u003Cp\u003EEnter a username, email address, and password.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image120.png\" alt=\"Appendix I - 1\"\u003E\u003C/p\u003E\n","\u003Cp\u003ESolve the puzzle and continue!\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image114.png\" alt=\"Appendix I - 2\"\u003E\u003C/p\u003E\n","\u003Cp\u003EYou can skip down to the bottom of this screen, or take the time to choose your interests.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image99.png\" alt=\"Appendix I - 3\"\u003E\u003C/p\u003E\n","\u003Cp\u003EChoose \u003Cstrong\u003EComplete setup\u003C/strong\u003E.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image8.png\" alt=\"Appendix I - 4\"\u003E\u003C/p\u003E\n","\u003Cp\u003EHead to your email application and verify your email address.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image131.png\" alt=\"Appendix I - 5\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThat’s it! You now have a Github account.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image106.png\" alt=\"Appendix I - 6\"\u003E\u003C/p\u003E\n","\u003Ch2\u003EAppendix II: Snowflake Account Creation\u003C/h2\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EGo to \u003Ca href=\"https://signup.snowflake.com/\"\u003Ehttps://signup.snowflake.com/\u003C/a\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EEnter your personal information.  You need to use a valid email address that you can access and haven’t used previously.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image14.png\" alt=\"Appendix II - 1\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EWhen you sign up for Snowflake it auto-populates to \u003Cstrong\u003EEnterprise\u003C/strong\u003E. This is the best choice for this lab. Choose the cloud/region closest to you and click the "GET STARTED" button.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image115.png\" alt=\"Appendix II - 2\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EYour account will provision in 1-2 minutes and you will receive an email activation link like this to create a user.  Click on the \u003Cstrong\u003ECLICK TO ACTIVATE\u003C/strong\u003E link.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image76.png\" alt=\"Appendix II - 3\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ENext, you'll see this page your browser. Specify your username and password. Be sure to store your credentials in a safe location.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image47.png\" alt=\"Appendix II - 4\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EYou will be logged in to your new account and it will look like this.  You will also receive another email with your username and account URL.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image5.png\" alt=\"Appendix II - 5\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003ECongratulations, you are ready to work in Snowflake!\u003C/h3\u003E\n","\u003Ch2\u003EAppendix III: Fivetran - Configure Google Adwords Connector\u003C/h2\u003E\n","\u003Cp\u003ELog into Fivetran and you should see the following screen, which shows our selection of source connectors. Use the type-down or the drop-down menu to find the \u003Ca href=\"https://fivetran.com/docs/applications/google-ads\"\u003EGoogle Ads\u003C/a\u003E connector. If you have already setup your first connector, you may need to go to the Connectors tab in the Fivetran web application, and choose \u003Cstrong\u003E+ Connector\u003C/strong\u003E.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image44.png\" alt=\"Appendix III - 1\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe Fivetran Google Ads connector setup screen is shown below, before we begin to modify it. On the left are the configuration controls. On the right are inline, helpful setup instructions.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image78.png\" alt=\"Appendix III - 2\"\u003E\u003C/p\u003E\n","\u003Cp\u003EObtain your customer ID per the instructions on the right.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image54.png\" alt=\"Appendix III - 3\"\u003E\u003C/p\u003E\n","\u003Cp\u003EAnd enter it in the field on the left. Click \u003Cstrong\u003EAUTHORIZE\u003C/strong\u003E to sign in to Google.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image60.png\" alt=\"Appendix III - 4\"\u003E\u003C/p\u003E\n","\u003Cp\u003EChoose the account with which you wish to authenticate.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image139.png\" alt=\"Appendix III - 5\"\u003E\u003C/p\u003E\n","\u003Cp\u003EAuthorize Fivetran to access your Google Ads data by clicking \u003Cstrong\u003EAllow\u003C/strong\u003E.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image96.png\" alt=\"Appendix III - 5\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENow we need to configure three reports. These three reports will give us the raw data that we will use to generate our models later. Each report will require:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EA Destination Table Name\u003C/li\u003E\u003Cli\u003EA Report Type\u003C/li\u003E\u003Cli\u003EA selection of Fields\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EFor each report, we will use Configuration Type: Use Custom Report.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDid You Know?\u003C/strong\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Chr\u003E\n","\u003Cp\u003EYou can find the configuration requirements for these three reports in the documentation of the Fivetran dbt package for Google Ads we will be using later, \u003Ca href=\"https://hub.getdbt.com/fivetran/google_ads_source/latest/\"\u003Edbt_google_ads_source\u003C/a\u003E. You can always find a list of all of Fivetranour available packages on \u003Ca href=\"https://hub.getdbt.com/\"\u003Edbt Hub\u003C/a\u003E and in our \u003Ca href=\"https://fivetran.com/docs/transformations/dbt\"\u003Edocumentation\u003C/a\u003E.\u003C/p\u003E\n\u003Chr\u003E\n","\u003Cp\u003EAds connectors, also known as Marketing connectors,  are different from most other Fivetran connectors in that, rather than replicate a set schema, you configure a series of reports. Each report becomes a table in the destination schema. This is necessary as the raw data that is returned from Ads connectors changes depending on the Fields chosen in the report. After the Hands-on Lab, you can always return to the connector setup and add more reports.\u003C/p\u003E\n","\u003Cp\u003EFor the first report, use the following configuration:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EDestination Table Name: final_url_performance\u003C/li\u003E\u003Cli\u003EReport Type: FINAL_URL_REPORT\u003C/li\u003E\u003Cli\u003EFields:\n\u003Cul\u003E\u003Cli\u003EAccountDescriptiveName\u003C/li\u003E\u003Cli\u003EAdGroupId\u003C/li\u003E\u003Cli\u003EAdGroupName\u003C/li\u003E\u003Cli\u003EAdGroupStatus\u003C/li\u003E\u003Cli\u003ECampaignId\u003C/li\u003E\u003Cli\u003ECampaignName\u003C/li\u003E\u003Cli\u003ECampaignStatus\u003C/li\u003E\u003Cli\u003EClicks\u003C/li\u003E\u003Cli\u003ECost\u003C/li\u003E\u003Cli\u003EDate\u003C/li\u003E\u003Cli\u003EEffectiveFinalUrl\u003C/li\u003E\u003Cli\u003EExternalCustomerId\u003C/li\u003E\u003Cli\u003EImpressions\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image39.png\" alt=\"Appendix III - 7\"\u003E\u003C/p\u003E\n","\u003Cp\u003EFor the second report, use the following configuration:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EDestination Table Name: criteria_performance\u003C/li\u003E\u003Cli\u003EReport Type: CRITERIA_PERFORMANCE_REPORT\u003C/li\u003E\u003Cli\u003EFields:\n\u003Cul\u003E\u003Cli\u003EAccountDescriptiveName\u003C/li\u003E\u003Cli\u003EAdGroupId\u003C/li\u003E\u003Cli\u003EAdGroupName\u003C/li\u003E\u003Cli\u003EAdGroupStatus\u003C/li\u003E\u003Cli\u003ECampaignId\u003C/li\u003E\u003Cli\u003ECampaignName\u003C/li\u003E\u003Cli\u003ECampaignStatus\u003C/li\u003E\u003Cli\u003EClicks\u003C/li\u003E\u003Cli\u003ECost\u003C/li\u003E\u003Cli\u003ECriteria\u003C/li\u003E\u003Cli\u003ECriteriaDestinationUrl\u003C/li\u003E\u003Cli\u003ECriteriaType\u003C/li\u003E\u003Cli\u003EDate\u003C/li\u003E\u003Cli\u003EExternalCustomerId\u003C/li\u003E\u003Cli\u003EId\u003C/li\u003E\u003Cli\u003EImpressions\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image73.png\" alt=\"Appendix III - 8\"\u003E\u003C/p\u003E\n","\u003Cp\u003EFor the third report, use the following configuration:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EDestination Table Name: click_performance\u003C/li\u003E\u003Cli\u003EReport Type: CLICK_PERFORMANCE_REPORT\u003C/li\u003E\u003Cli\u003EFields:\n\u003Cul\u003E\u003Cli\u003EAccountDescriptiveName\u003C/li\u003E\u003Cli\u003EAdGroupId\u003C/li\u003E\u003Cli\u003EAdGroupName\u003C/li\u003E\u003Cli\u003EAdGroupStatus\u003C/li\u003E\u003Cli\u003ECampaignId\u003C/li\u003E\u003Cli\u003ECampaignName\u003C/li\u003E\u003Cli\u003ECampaignStatus\u003C/li\u003E\u003Cli\u003EClicks\u003C/li\u003E\u003Cli\u003ECriteriaId\u003C/li\u003E\u003Cli\u003EDate\u003C/li\u003E\u003Cli\u003EExternalCustomerId\u003C/li\u003E\u003Cli\u003EGclId\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image46.png\" alt=\"Appendix III - 9\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThat’s it for setting up reports! To review, your connector configuration screen should look like the screen shot below:\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image83.png\" alt=\"Appendix III - 10\"\u003E\u003C/p\u003E\n","\u003Cp\u003EGreat! Now click \u003Cstrong\u003ESAVE & TEST\u003C/strong\u003E to save your configuration.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image9.png\" alt=\"Appendix III - 11\"\u003E\u003C/p\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003ECONTINUE\u003C/strong\u003E to see the next screen, where you can \u003Cstrong\u003EStart Initial Sync\u003C/strong\u003E to start the data flowing!\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image113.png\" alt=\"Appendix III - 12\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDid You Know?\u003C/strong\u003E\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Chr\u003E\n","\u003Cp\u003EFivetran does more than just sync Ad connector reports. Fivetran has over 170 source connectors that you can use to keep your data in sync in Snowflake.  With Fivetran’s Database (e.g.,x. Oracle, PostgreSQL) and Application (e.g., ex Salesforce, Marketo, NetSuite) connectors, your data is automatically kept up-to-date without any intervention on your part. Any schema changes to your source data are automatically updated in Snowflake with our Automatic Schema Migration technology. Never miss or lose data, and never worry about updating your data pipelines again.\u003C/p\u003E\n\u003Chr\u003E\n","\u003Cp\u003EMoreover, Fivetran keeps your data up-to-date on your schedule, with recurring syncs on the timetable you specify. Keep reading for more information.\u003C/p\u003E\n","\u003Cp\u003EBefore we start the first sync, let’s take a look at the configuration Fivetran provides for connectors.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image7.png\" alt=\"Appendix III - 13\"\u003E\u003C/p\u003E\n","\u003Cp\u003EFirst let’s look at the \u003Cstrong\u003ESchema\u003C/strong\u003E tab. Here, depending on the connector type, you can configure up to three settings:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EBlock a table from syncing to Snowflake.\u003C/li\u003E\u003Cli\u003EBlock a column from syncing to Snowflake.\u003C/li\u003E\u003Cli\u003EHash a column’s values before they are stored in Snowflake.\n\u003Cul\u003E\u003Cli\u003EUsed to protect Personally Identifiable Information from being accessed by the users of the data, while maintaining the ability to SQL JOIN on those values.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image85.png\" alt=\"Appendix III - 14\"\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EWith Ads connectors, you can enable or disable a particular report from syncing.\u003C/p\u003E\n","\u003Cp\u003ENext let’s visit the \u003Cstrong\u003ESetup\u003C/strong\u003E tab. There is one very important configuration tool here, and that is the Sync Frequency. This can be set to as frequently as every 5 minutes, to as infrequently as every 24 hours. Fivetran starts and stops the Snowflake warehouse used for data loading on demand, and is extremely efficient at data loading, so you only pay for the time Fivetran is actually loading data.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image129.png\" alt=\"Appendix III - 15\"\u003E\u003C/p\u003E\n","\u003Cp\u003EOK! That is it! The Schema setup in the \u003Cstrong\u003ESchema\u003C/strong\u003E tab, and the Sync Frequency setting in the \u003Cstrong\u003ESetup\u003C/strong\u003E tab are the only configuration details you need to manage, and even those can be left on the defaults for flawless operation! With that visited, let’s go back to the \u003Cstrong\u003EStatus\u003C/strong\u003E tab and start the sync!\u003C/p\u003E\n","\u003Cp\u003EOnce the \u003Cstrong\u003EStart Initial Sync\u003C/strong\u003E button is clicked, you should see your sync start up.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image15.png\" alt=\"Appendix III - 16\"\u003E\u003C/p\u003E\n","\u003Cp\u003EWhile the sync is running, we can take a quick detour into the \u003Cstrong\u003ELogs\u003C/strong\u003E tab. This is a real-time view into the data loading process. Fivetran offers ways to export these logs into your preferred log aggregation service. We even have a free connector called the \u003Ca href=\"https://fivetran.com/docs/logs/fivetran-log\"\u003EFivetran Log\u003C/a\u003E connector, that exports log data, schema operation metadata, and Fivetran credit consumption data!\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image70.png\" alt=\"Appendix III - 17\"\u003E\u003C/p\u003E\n","\u003Cp\u003EFor this demo, I chose to only sync 3 months of historical data, so the initial sync finished relatively quickly at two and a half minutes. When your sync has completed, you will see its status on the timeline, as per below.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image63.png\" alt=\"Appendix III - 13\"\u003E\u003C/p\u003E"],"description":"","title":"Automating Data Pipelines to Drive Marketing Analytics with Snowflake & Fivetran","elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\r\n## Overview \r\n\r\nWith the growth of your data and business, so does the complexity involved in traditional approaches and architecture. Snowflake and Fivetran have partnered to bring you the Data Cloud and the most automated data integration solution which helps customers simplify data pipelines for all your businesses so you can focus on your data and analytics instead of infrastructure management and maintenance. In this virtual hands-on lab, you will follow a step-by-step guide to perform marketing analytics for Google Ads data by using Fivetran, Snowflake, and dbt. Let’s get started. \r\n\r\n### Prerequisites\r\nTo participate in the virtual hands-on lab, attendees need the following:\r\n\r\n* Github account - Participants will need to create, or already have, an account on Github. Other git-based source control hosting sites will work fine (Gitlab, Bitbucket), but the instructions will assume Github. An account on Github is free: [link](https://github.com/join/).  See appendix 1 for step-by-step instructions. \r\n\r\n* Snowflake Free Trial - Registrants of the virtual hands-on lab need to sign up for a [free trial](https://signup.snowflake.com/?utm_cta=quickstarts_).  Please sign up using an email address that hasn’t been used previously. See appendix 2 for step-by-step instructions. \r\n\r\n* OPTIONAL: Log in to your Google account, if you have one.\r\n\r\n* OPTIONAL: Google Ads account credentials. \r\n\r\nSample Google Ads data will be provided if you do not have a Google Ads account.\r\n\r\n### What You'll Need During the Lab\r\n \r\n* [Sample Google Ads data](https://github.com/fivetran/snowflake_fivetran_vhol/blob/main/LAB_ASSETS/GSHEETS_LINKS.md) - a public Google Sheet with three workbooks. Each workbook will become a table in our sample Google Ads data schema. The above link takes you to a page full of Google Sheets links -- all of these sheets are copies of the same data, you can use any link on that page.\r\n\r\n* [dbt Project Github Repository](https://github.com/fivetran/snowflake_fivetran_vhol)\r\n\r\n    * [SQL Script file](https://github.com/fivetran/snowflake_fivetran_vhol/raw/main/LAB_ASSETS/vhol_script.sql.zip) - Participants will load [this file](https://github.com/fivetran/snowflake_fivetran_vhol/raw/main/LAB_ASSETS/vhol_script.sql.zip) into a Snowflake worksheet when prompted during the lab.  Save this file where you can easily find it during the lab.\r\n\r\n### What You'll Learn\r\n\r\n* How to begin a Fivetran trial through Snowflake's Partner Connect\r\n\r\n* How to load Google Ads data into Snowflake using Fivetran\r\n\r\n* How to run a dbt project via Fivetran to generate models\r\n\r\n* How to create analyst sandboxes in Snowflake using clones\r\n\r\n* How to leverage data in Snowflake's Data Marketplace \r\n\r\n### What You'll Build\r\n* A sophisticated marketing data analytics pipeline leveraging Fivetran, dbt, and Snowflake\r\n\r\n## Snowflake Configuration \r\n\r\n1. Login to your Snowflake trial account.  \r\n![Snowflake Log In Screen](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image124.png)  \r\n\r\n2. UI Tour (SE will walk through this live). For post-workshop participants, click [here](https://docs.snowflake.com/en/user-guide/snowflake-manager.html#quick-tour-of-the-web-interface) for a quick tour of the UI.  \r\n![Snowflake Worksheets](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image29.png)  \r\n\r\n3. Let’s change our role and enable notifications. We need to work in the ACCOUNTADMIN role for this lab and notifications are how Snowflake will alert you when resource monitor thresholds have been crossed. Click on your User Name in the upper right-hand corner.  \r\n\u003C!--- ![Change role - step 1](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image27.png)  --\u003E\r\n![assets/image27.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image27.png)\r\n\r\n4. You’ll get a popup with 4 items; click on Switch Role.  \r\n\u003C!--- ![Change role - step 2](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image22.png)  --\u003E\r\n![assets/image22.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image22.png)\r\n\r\n5. Select **ACCOUNTADMIN**.  \r\n\u003C!--- ![Change role - step 3](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image62.png)  --\u003E\r\n![assets/image62.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image62.png)\r\n\r\n6. The UI will refresh and you should see ACCOUNTADMIN under your username. If you don’t, go back to step 5.  \r\n\u003C!--- ![Change role - step 4](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image30.png)  --\u003E\r\n![assets/image30.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image30.png)\r\n\r\n7. Click on your username again and you’ll get the same popup with 4 items; click on Preferences.  \r\n\u003C!---![Preferences - step 1](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image59.png)  --\u003E\r\n![assets/image59.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image59.png)\r\n\r\n8. Click on **Notifications**.  \r\n![Preferences - step 2](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image137.png)  \r\n\r\n9. Select **All**, which will send notifications to your email address and this UI (in the Notifications tile in the upper right).  \r\n![Preferences - step 3](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image100.png)  \r\n\r\n10. Now let’s create your Fivetran account.  Click on the **Partner Connect** tile at the top of the UI...\r\n\u003C!--- ![Create Fivetran Account - step 1](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image89.png)  --\u003E\r\n![assets/image89.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image89.png)\r\n\r\n11. ...and then click on the Fivetran tile inside Snowflake Partner Connect. (if you aren’t in the ACCOUNTADMIN role you’ll receive a warning.  Go back and complete steps 6-9.)  \r\n![Create Fivetran Account - step 2](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image134.png)  \r\n\r\n12. Click on **Connect**.  \r\n![Create Fivetran Account - step 2](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image122.png)  \r\n\r\n13. Click on **Activate** in the pop-up that appears. This will open a new browser tab and take you to Fivetran where you will enter a password for the trial account. \r\n![Create Fivetran Account - step 3](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image61.png)  \r\n\r\n\u003C!-- ------------------------ --\u003E\r\n\r\n## Fivetran - Configure Source  \r\n\r\n\u003C!-- ![Fivetran](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image79.png) --\u003E \r\n![assets/image79.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image79.png)\r\n\r\nIn the next three sections we will create an automated data pipeline, with an extract-load (ELT) architecture::\r\n1. Extract and Load:\r\n    * Complete Fivetran Account Setup\r\n    * Upload Sample Adwords Data using the Fivetran Google Sheets Connector\r\n2. Transform:\r\n    * Create our first dbt project\r\n    * Setup Fivetran dbt Transformations\r\n\r\nLet’s get started!\r\n\r\n### Complete Fivetran Account Setup\r\n1. Create a password.  \r\n![Fivetran - Password](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image107.png)  \r\n\r\n2. That is it! Hang out on this screen until the next section. \r\nTo log into Fivetran in the future, you can navigate to [https://fivetran.com/dashboard](https://fivetran.com/dashboard). Your email is the same as your Snowflake email (at the time you connected with Partner Connect) and your password is whatever you entered in Step 1 of this section.\r\n![Fivetran - Source](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image3.png)  \r\n\r\n## Upload Sample Adwords Data With Google Sheets\r\n\r\n**Note**: The **Setup Fivetran dbt Transformations** section assumes you have uploaded the sample data as outlined in this section. If you used your own Google Ads data, that is fine; you will just need to edit the dbt_project.yml file as discussed in the next section.\r\n\r\nFor your convenience we have included sample Google Adwords data in the form of a Google Sheets containing three workbooks. Each workbook corresponds to a table to be synced to the Snowflake warehouse. This gives us the opportunity to explore another Fivetran feature, the Google Sheets connector. Google Sheets is, in fact, the most popular connector used by Fivetran customers. In the next section, we will:\r\n* Use the Google Sheets connector to create a new Schema and Tables\r\n* We will create one Google Sheets connector per table. \r\n* We will use any one of the Google Sheet links found in this document: [link](https://github.com/fivetran/snowflake_fivetran_vhol/blob/main/LAB_ASSETS/GSHEETS_LINKS.md)\r\n\r\n\r\n1. Google Sheets is the first connector in the list! Click on **Google Sheets**.\r\n![Fivetran - Source2](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image57.png)  \r\n\r\n2. You should see this screen.  \r\n![Fivetran - Google Sheet](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image21.png)  \r\n\r\n3. Please use the following values for the two fields:\r\n    * Destination schema: **google_ads_demo**\r\n    * Destination table: **final_url_performance**\r\n![Fivetran - Google Sheet2](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image132.png)  \r\n\r\n4. Scroll down the page a bit, and paste the URL you just copied into the Sheet URL field. ([Follow these instructions to get a Google Sheets URL](https://github.com/fivetran/snowflake_fivetran_vhol/blob/main/LAB_ASSETS/GSHEETS_LINKS.md))\r\nThen, click the **FIND SHEET** button. This step will take a few moments. \r\n![Fivetran - Google Sheet3](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image118.png)  \r\n\r\n5. In the **Named Range** field, choose **final_url_performance**.\r\n![Fivetran - Google Sheet4](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image68.png)  \r\n\r\n6. Click **SAVE & TEST**.\r\n![Fivetran - Google Sheet5](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image18.png)  \r\n\r\n7. When you see **All connection tests passed!**, click the **VIEW CONNECTOR** button.\r\n![Fivetran - Google Sheet6](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image42.png)  \r\n\r\n8. Click **Start Initial Sync**. This will start the data sync into the warehouse! Now any updates to these sheets will be automatically synced to Snowflake.\r\n![Fivetran - Google Sheet7](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image77.png)  \r\n\r\n9. Great! The Google Sheets connector is now syncing. Let’s setup the next table. Click the **Connectors** menu.  \r\n![Fivetran - Google Sheet8](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image86.png)  \r\n\r\n10. Click the **+ Connector** button.  \r\n![Fivetran - Google Sheet9](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image36.png)  \r\n\r\n11. Please use the following values for the two fields:\r\n    * Destination schema: **google_ads_demo**\r\n    * Destination table: **criteria_performance**\r\n![Fivetran - Google Sheet10](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image6.png)  \r\n\r\n12. Scroll down the page a bit, and paste the URL you just copied into the Sheet URL field. (Use the same Sheets URL as step 4, or [follow these instructions to get a new Google Sheets URL](https://github.com/fivetran/snowflake_fivetran_vhol/blob/main/LAB_ASSETS/GSHEETS_LINKS.md).)\r\nThen, click the **FIND SHEET** button. This step will take a few moments. \r\n![Fivetran - Google Sheet11](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image126.png)  \r\n\r\n13. In the **Named Range** field, choose **criteria_performance**. Click **SAVE & TEST**.\r\n![Fivetran - Google Sheet12](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image52.png)  \r\n\r\n14. When the connection tests complete, click **VIEW CONNECTOR**.\r\n![Fivetran - Google Sheet13](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image42.png)  \r\n\r\n15. Click **Start Initial Sync**. Then click **Connectors** on the left-hand menu.\r\n![Fivetran - Google Sheet14](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image38.png)  \r\n\r\n16. Click **+ Connector**.  \r\n![Fivetran - Google Sheet15](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image108.png)  \r\n\r\n17. Please use the following values for the two fields:\r\n    * Destination schema: **google_ads_demo**\r\n    * Destination table: **click_performance**\r\n![Fivetran - Google Sheet16](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image121.png)  \r\n\r\n18. Scroll down the page a bit, and paste the URL you just copied into the Sheet URL field. (Use the same Sheets URL as step 4, or [follow these instructions to get a new Google Sheets URL](https://github.com/fivetran/snowflake_fivetran_vhol/blob/main/LAB_ASSETS/GSHEETS_LINKS.md).) \r\nThen, click the **FIND SHEET** button. This step will take a few moments.  \r\n![Fivetran - Google Sheet17](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image95.png)  \r\n\r\n20. In the **Named Range** field, choose **click_performance**. Click **SAVE & TEST**.  \r\n![Fivetran - Google Sheet18](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image64.png)  \r\n\r\n21. When the connection tests complete, click **VIEW CONNECTOR**.  \r\n![Fivetran - Google Sheet19](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image42.png)  \r\n\r\n22. Click **Start Initial Sync**. Then click **Connectors** on the left-hand menu.  \r\n![Fivetran - Google Sheet20](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image117.png)  \r\n\r\n23. Congratulations! You can see your 3 Google Sheets connectors running, each creating one of our three sample data tables. Click on **Google Sheets (3)**.  \r\n![Fivetran - Google Sheet21](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image75.png)  \r\n\r\nThis view shows you all of your Google Sheets connectors. Some of your connectors may still be syncing, that is fine; we will have time for them to complete while we perform the next part of the lab.  \r\n![Fivetran - Google Sheet22](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image48.png)  \r\n\r\nIn Snowflake (optional): When the sync is complete, you can see your schema and tables in Snowflake’s Worksheet view. You should be able to see the data in the tables by clicking into the **PC_FIVETRAN_DB** database, into the **GOOGLE_ADS_DEMO** schema, then clicking on one of the tables and then clicking **Preview Data**.\r\n\r\n## Fork Sample dbt Project on Github\r\n\r\n**Note**: The **Setup Fivetran dbt Transformations** section assumes you have uploaded the sample data as outlined in the previous section. If you use your own Google Ads data, that is fine; you will just need to edit the dbt_project.yml file as discussed in this section.  \r\n\r\nFor your convenience, we have created a sample dbt Github project that is already configured to work with Fivetran’s [dbt_google_ads_source](https://hub.getdbt.com/fivetran/google_ads_source/latest/) and [dbt_google_ads](https://hub.getdbt.com/fivetran/google_ads/latest/) packages. You can find the sample repository at the following link:\r\nhttps://github.com/fivetran/snowflake_fivetran_vhol\r\n\r\nThis section assumes that you have created a Github account and are logged into that account. In this section we are going to:\r\n* Fork the sample github repository.\r\n* Explore the files in the project.\r\n\r\nWhen you click [https://github.com/fivetran/snowflake_fivetran_vhol](https://github.com/fivetran/snowflake_fivetran_vhol) and you are logged in, you should see the following screen. Notice the user / owner is **fivetran** and the repository is named **snowflake_fivetran_vhol**.\r\n1. On the upper right (but below your user icon) is a **Fork** button. Click it!\r\n![Fork repo - 1](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image10.png)  \r\n  \r\n2. If you have access to multiple github accounts, you will have the option to select into which account to fork this repository. Choose your personal user account.  \r\n![Fork repo - 2](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image116.png)  \r\n  \r\nThat's it! Notice in the screenshot above the user / owner is **crw** and the repository is (still) named **snowflake_fivetran_vhol** - but now that repository is under your personal Github account. I can now manipulate this repository however I like, without impacting the sample repository.\r\n![Fork repo - 3](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image105.png)  \r\n  \r\nCongrats! You have successfully created your first dbt project! That was easy!\r\n**The next section is OPTIONAL and not required for the workshop.** It is included for your information only.  \r\n\r\nThere are three very important configuration files to be aware, all at the top level of the file tree.\r\n* **dbt_project.yml** - general dbt project settings.\r\n* **packages.yml** - include packages that bring functionality to your dbt project, like those provided by Fivetran to enhance your data!\r\n* **deployment.yml** - a Fivetran-specific configuration file that controls how frequently the dbt jobs run.  \r\n\r\n[dbt_project.yml](https://github.com/crw/snowflake_fivetran_vhol/blob/main/dbt_project.yml):\r\n![Fork repo - 4](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image24.png)  \r\nVarious configuration settings are found in this file. Please see [dbt’s documentation](https://docs.getdbt.com/reference/dbt_project.yml) for more information on these settings.\r\n\r\n\u003E aside positive\r\n\u003E \r\n\u003E  **Did You Know?**  \r\n*******************************************************************************  \r\nAs previously mentioned, **if you want to use your own Google Ads data**, you will need to edit **dbt_project**.yml. You can edit files directly in Github using the pencil icon at the top of the file view (mid-upper right, to the right of the Blame button). All you need to do is remove or comment out the lines that say:  \r\n```\r\n    vars\r\n        google_ad_schema: 'GOOGLE_ADS_DEMO'\r\n```\r\n*******************************************************************************  \r\nThat’s it! Remove those lines and you are good to go. This assumes, of course, that you loaded your own Google Ads data in the previous section! If you have an **ADWORDS** schema in your Snowflake warehouse, you successfully loaded Google Ads data.\r\n\r\n\r\n\r\n[packages.yml](https://github.com/crw/snowflake_fivetran_vhol/blob/main/packages.yml):  \r\nThis is where you can include outside packages to run! See [hub.getdbt.com](https://hub.getdbt.com/) for a complete list and installation instructions. As of this writing, Fivetran has created over 17 packages, including packages for Netsuite, Salesforce, Twitter Ads, Marketo, Zendesk, Mailchimp, and more!\r\n![Fork repo - 5](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image32.png)  \r\n\r\n[deployment.yml](https://github.com/crw/snowflake_fivetran_vhol/blob/main/deployment.yml):  \r\nThis is a Fivetran-specific file that configures what jobs will run when, leveraging a [crontab-style syntax](https://crontab.guru/) for scheduling. Documentation can be found in the file itself. Remember, as described above, you can edit files directly in Github!\r\n\r\n## Set Up Fivetran dbt Transformations\r\n\r\nIn this section we will take the dbt project we created in the previous section, and run it via Fivetran to generate models! We will be showing how Fivetran is used to orchestrate dbt jobs, and the first step is to connect to your dbt project in Github. There are a lot of steps in this section, but for the most part they are not too complicated. Let’s get started!\r\n\r\n**Note: dbt Transformations in Fivetran are in beta** as of this writing. Depending how you signed up for your Fivetran trial, you may or may not see the option to “Try dbt Transformations”. If you do not see this option, please reach out to us and we will try to get you enabled to try this feature.  \r\n\r\nIf you see this screen, please reach out to Fivetran sales -- we can enable dbt Transformations beta for your account.  \r\n![Transformations - 1](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image12.png)  \r\n\r\n1. If you have not already, **switch back to your Fivetran tab in your web browser**.\r\nFirst click on the **Transformations** section of your Fivetran interface. You should see the following interface. We will be clicking the button labeled **Try dbt Transformations**.\r\n![Transformations - 2](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image51.png)  \r\n\r\n2. Click **Enable dbt Transformations**\r\n![Transformations - 3](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image72.png)  \r\n\r\n3. Click **I am ready to connect my Git repo**.\r\n![Transformations - 4](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image104.png)  \r\n\r\n4. Now we will configure the connection to Github. Next to **Public Key**, click **Copy to Clipboard** (the papers icon).\r\n![Transformations - 5](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image92.png)  \r\n\r\n5. On the Github repository we created above (your_username/snowflake_fivetran_vhol) click on **Settings** (farthest-right of the tabs under the repository name.)\r\n![Transformations - 6](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image49.png)  \r\n\r\n6. On the left-hand navigation, click on **Deploy Keys** (lower half, under Integrations, above Secrets).\r\n![Transformations - 7](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image102.png)  \r\n\r\n7. Click **Add deploy key**, on the mid-upper-right hand side of the screen.\r\n![Transformations - 8](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image98.png)  \r\n\r\n8. Give the deploy key a memorable, distinct title, like **Fivetran dbt access**.\r\nPaste the previously-copied deploy key in the **Key** field.\r\n**DO NOT** check “Allow write access” - currently Fivetran only requires read access to the repository.\r\nClick **Add key**\r\n![Transformations - 9](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image128.png)  \r\nPhew! Done! If at any time you want to revoke Fivetran’s access to this repository, come back to this screen and delete the deploy key we just created.\r\n\r\n9. Before leaving Github, return to the **Code** tab.\r\n![Transformations - 10](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image111.png)  \r\n\r\n10. Now click the green **Code** button. Make sure the **SSH** tab is selected, and copy the:\r\n**git@github.com:your_username/snowflake_fivetran_vhol.git** URL. We will need it on the next step.\r\n**Note**: it is safe to ignore errors about not having an SSH key configured here. The Deploy Key we configured will give Fivetran access to this repository.\r\n![Transformations - 10](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image93.png)  \r\n\r\n11. Paste the value into the **Repository URL** field.\r\n![Transformations - 10](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image13.png)  \r\n\r\n12. For **Default Schema Name** field, enter **GOOGLE_ADS_DBT** \r\n![Transformations - 10](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image2.png)  \r\n\r\n    As of this writing, you need to perform the next actions to continue.  \r\n\r\n    **Note**: Github has changed their default branch name to “main”. This change was made very recently, and Fivetran has not yet caught up to the new naming convention. For the time being, for this guide, we will need to manually change the branch name.  \r\n\u003C!--- FDP: Wish the above could be done as a positive box --\u003E\r\n\r\n13. Click **Show Advanced Options**.\r\n![Transformations - 10](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image67.png)  \r\n\r\n14. Change the **Git branch** field’s value to **main**.  \r\n![Transformations - 10](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image133.png)  \r\n\r\n15. Click **SAVE**. Then when you see the green success message, click **TRANSFORMATIONS**.\r\n![Transformations - 10](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image1.png)  \r\n\r\n16. That’s it! Your dbt project  is now configured to run and be orchestrated with Fivetran. In the upper right corner, there is a toggle switch, **Activate dbt Transformations**. Click this switch to start your transformations running.\r\n![Transformations - 10](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image33.png)  \r\n\r\n17. Now if left to their own devices, your transformations will run on the schedules listed below. As a reminder, these schedules are configured in the [deployment.yml](https://github.com/crw/snowflake_fivetran_vhol/blob/main/deployment.yml) file, as discussed in the previous section. But let’s run one right away! Click on the **weekdays** line item.  \r\n![Transformations - 10](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image109.png)  \r\n\r\n18. In the upper right corner, click **Run now** (the circular arrow icon.)\r\n![Transformations - 10](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image43.png)  \r\n\r\n19. The dbt job runs successfully! Click on the line item with the green checkmark next to it.\r\n![Transformations - 10](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image66.png)  \r\n\r\n20. Here we can see the results of running the dbt job. Click on the down arrow (caret) next to run models.\r\n![Transformations - 10](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image50.png)  \r\n\r\nThis exposes the results of running the dbt job. If you scroll to the bottom of the page, you can see the job ran successfully.\r\n![Transformations - 10](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image74.png)  \r\n\r\nCongratulations! You have successfully run your first dbt job!  \r\n\r\nIn Snowflake (optional): We can see the results of all of this hard work in the Snowflake Worksheets view. On the left hand column, you can see the GOOGLE_ADS_DBT schema which has the results of running our dbt job: fresh models, produced simply by including Fivetran’s open source dbt_google_ads package! We are that much closer to exposing the true value of this data!\r\n\r\nThe tables created by these models do the following:\r\n* **GOOGLE_ADS__CRITERIA_AD_ADAPTER**\r\n    * Each record represents the daily ad performance of each criteria in each ad group and campaign.\r\n* **GOOGLE_ADS__URL_AD_ADAPTER**\r\n    * Each record represents the daily ad performance of each URL in each ad group, including information about the used UTM parameters.\r\n* **GOOGLE_ADS__CLICK_PERFORMANCE**\r\n    * Each record represents a click, with a corresponding Google click ID (gclid).\r\n\r\n**Did You Know?**\r\nAll of the code that produces these models is open source! Fivetran officially supports over 20 open source dbt packages that model data delivered by Fivetran, typically to provide commonly required aggregations. You can see all of the available packages at [https://hub.getdbt.com/](https://hub.getdbt.com/) - scroll down to the Fivetran section.\r\n\r\nThe two packages we use in this lab are the Google Ads packages:\r\n[google_ads_source](https://hub.getdbt.com/fivetran/google_ads_source/latest/) [source code on github](https://github.com/fivetran/dbt_google_ads_source)\r\n[google_ads](https://hub.getdbt.com/fivetran/google_ads/latest/) [source code on github](https://github.com/fivetran/dbt_google_ads)\r\n\r\nYou can save considerable development time by leveraging Fivetran’s open source dbt packages with data delivered by Fivetran. Now that you have a running dbt project, feel free to try including more packages!\r\n\r\n***\r\nThe summary so far: Maintenance-free data pipelines and powerful in-warehouse transformations\r\n\r\nIn the previous sections, we learned the following skills:\r\n* How to setup a Fivetran connector (in this case, a Google Sheets connector)\r\n* How to create a dbt project by forking the snowflake_fivetran_vhol sample repository\r\n* How to enable dbt Transformations in Fivetran by connecting our repository\r\n\r\nIn doing so, we have set up a complete, end-to-end modern data pipeline focusing on the advantages of the Extract-Load-Transform (ELT) architectural approach. Data loading is extremely simple to configure and comes with the benefit of hands-free maintenance, forever. dbt is a powerful transformation tool that comes with many open source Fivetran modeling packages out-of-the-box. Moreover, Fivetran manages the execution of those transformation jobs, and the jobs run natively in the Snowflake warehouse, exposing all of the pßower of Snowflake’s query engine to your transformations.\r\n\r\n## Snowflake - Part Two\r\n\r\n1. Back in the Snowflake UI, let’s take a quick look at the query history to see all the work that Fivetran/dbt performed.  \r\n![Snowflake Part 2 - 1](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image53.png)  \r\n\r\n2. Click on **Add a filter**.  \r\n![Snowflake Part 2 - 2](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image20.png)  \r\n\r\n3. Change the left box to User and select PC_FIVETRAN_USER in the right box.  All of the Fivetran/dbt queries executed as this user.  \r\n![Snowflake Part 2 - 3](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image69.png)  \r\n\r\n4. In the table below you can see all of these queries. Scroll down. Click on a SQL Text value and you’ll see a pop-up of the specific SQL statement. You can read about this UI screen [here](https://docs.snowflake.com/en/user-guide/ui-history.html).  \r\n![Snowflake Part 2 - 4](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image65.png)  \r\n\r\n5. Click on the **Worksheets** tile at the top.\r\n![Snowflake Part 2 - 5](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image81.png)  \r\n\r\n6. Your screen will look like this.  This is where we will do most of our remaining work.\r\n![Snowflake Part 2 - 6](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image58.png)  \r\n\r\n7. Now load the script (vhol_script.sql) by clicking on the button and then selecting in the pop-up.  Navigate your computer and find the script.\r\n![Snowflake Part 2 - 7](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image135.png)  \r\n\r\n8. The script should appear in your worksheet like this.\r\n![Snowflake Part 2 - 8](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image94.png)  \r\n\r\n9. Notice on the left-hand side is a database browser with a database called PC_FIVETRAN_DB.  This is the database that you loaded with Fivetran.  Click on it and you will see the schemas.  GOOGLE_ADS_DEMO is where the data initially loaded and GOOGLE_ADS_DBT is the schema created by the dbt process.  Click on GOOGLE_ADS_DBT and you’ll see the tables and views that live within.\r\n![Snowflake Part 2 - 9](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image37.png)  \r\n \r\n10. Let’s start our work performing DBA functions.   We need to set the context we want to use within the worksheet.  In each worksheet I can have a different role,  warehouse, database, and schema.  Each worksheet is independent of the others (like Snowflake compute!).  My worksheet role can also be different from the role I have in the upper right-hand corner under my name.  \u003Cbr\u003E\u003Cbr\u003E\r\n\r\n    Let’s execute the SQL in script section **A** to set our worksheet context.  To do so, highlight the two lines (_use role..., use schema..._) and click the ![assets/image136.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image136.png) button.  \u003Cbr\u003E\u003Cbr\u003E\r\n    \r\n    _**Pro Tip: You may get a pop-up asking if you want to run both statements.  If you don’t want this warning every time, click in the check box and click OK.**_\r\n\r\n```\r\n/*-----\r\n--A--\r\n-----\r\n--Let's set our worksheet role and db/schema*/\r\nuse role ACCOUNTADMIN;\r\nuse schema PC_FIVETRAN_DB.GOOGLE_ADS_DBT;\r\n```\r\n\r\n    Notice that your worksheet context now has a default role, database, and schema but no warehouse (yet).  \r\n    ![Snowflake Part 2 - 11](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image80.png)  \r\n\r\n11. Let’s pretend that the data we loaded and transformed with Fivetran is our production data.  We only want it to be changed by our Fivetran process.  Since our analysts will want to experiment with and change the data,  we’ll **CLONE** the entire PC_FIVETRAN_DB database so they can have their own sandbox.  This is a logical copy; it will consume no additional space unless the data in the clone starts to change.  This is a fantastic tool for creating as many sandboxes as you need.  It also works well for making time-specific snapshots.  \u003Cbr\u003E\u003Cbr\u003E\r\n\r\n    _**Cloning reduces time to value  - full production clones are fast and support more accurate analytical results because they are rich and complete data sets.**_\r\n    \r\n12. Run the **Create Database** SQL and the **Use Schema** commands in section **B** to make a clone, then refresh the database browser with the (small) ![Refresh](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image130.png) button:  \r\n```\r\n/*-----\r\n--B--\r\n-----\r\n--Make a new DEV db and schema and then clone the 3 key tables for isolated analysis and experimentation*/\r\ncreate database GOOGLE_ADS_DEV clone PC_FIVETRAN_DB;\r\nuse schema GOOGLE_ADS_DEV.GOOGLE_ADS_DBT;\r\n```\r\n\r\n    Now your analysts have GOOGLE_ADS_DEV - a complete clone of PC_FIVETRAN_DB.  \r\n\r\n13. Next we need to create a warehouse (compute) for the analysts. For this workshop we’ll create an Extra Small - the smallest unit of Snowflake compute - with an auto suspend time of 2 minutes and auto resume enabled.  Auto resume means it will start up when a request is made.  Run the SQL in section **C** to create the warehouse.  Notice that this warehouse is now in your worksheet context.  \r\n```\r\n/*-----\r\n --C--\r\n -----\r\n --Create a virtual warehouse (compute) for the Marketing Analyst team to do their work*/\r\ncreate or replace warehouse MKT_ANALYSIS with warehouse_size = 'XSMALL' auto_suspend = 120 --seconds\r\n  auto_resume = TRUE;\r\n```\r\n    \r\n    _**Snowflake compute is unique.  It’s (1) easy to define and manage; (2) fast to activate and suspend; (3) self-suspending; (4) self-healing; (5) isolated from the storage layer; (6) isolated  from other compute; (7) quick to scale up/down and out/in; (8) non-disruptive when any of this is happening.**_\r\n    \r\n14. For our last act as DBA we’ll create a **resource monitor** to track warehouse consumption.  Resource monitors are convenient tools for setting time-based consumption thresholds on compute at the warehouse or account level.  You can set alerts for various thresholds and even prevent a warehouse from running once it reaches a credit value that you set.\r\n```\r\n/*-----\r\n --D--\r\n -----\r\n --Create a resource monitor to track credit usage of the marketing virtual warehouse*/\r\ncreate or replace RESOURCE MONITOR \"MARKETING_ANALYSIS\" with CREDIT_QUOTA = 20,\r\n  frequency = 'DAILY',\r\n  start_timestamp = 'IMMEDIATELY',\r\n  end_timestamp = null triggers \r\n  ON 100 PERCENT DO SUSPEND \r\n  ON 5 PERCENT do NOTIFY \r\n  ON 10 PERCENT do NOTIFY \r\n  ON 50 PERCENT do NOTIFY \r\n  ON 90 PERCENT do NOTIFY;\r\nalter WAREHOUSE \"MKT_ANALYSIS\"\r\nset RESOURCE_MONITOR = \"MARKETING_ANALYSIS\";\r\n```\r\n\r\n    Run the two SQL statements in section **D** to create a resource monitor that has a daily 20 credit limit, sends an alert at 5%, 10%, 50%, and 99% thresholds, and suspends the warehouse at 100% (20 credits).  The second statement associates the resource monitor to the warehouse.   \r\n\r\n15. Now we’ll switch to ‘Analyst mode’. In real life this would be a different person with a different userid and a different Role.  An analyst might write a query like the one in section **E** of the script to answer the question “What was the best performing cranberry sauce campaign?” Go ahead and run it.  We’re working with tiny data sets in the lab but in reality we might run over millions or billions of rows.\r\n```\r\n/*-----\r\n --E--\r\n -----\r\n --What was the best performing cranberry sauce campaign?*/\r\nselect ad_group_name,\r\n  campaign_name,\r\n  sum(spend) as TOT_SPEND,\r\n  sum(clicks) as TOT_CLICKS,\r\n  sum(impressions) as TOT_IMPRESSIONS,\r\n  sum(impressions) / sum(spend) as IMPRESSIONS_PER_DOLLAR,\r\n  sum(spend) / sum(clicks) as cost_per_click\r\nfrom GOOGLE_ADS__URL_AD_ADAPTER\r\nwhere ad_group_name = 'cranberry sauce'\r\ngroup by ad_group_name,\r\n  campaign_name\r\norder by 6 desc;\r\n```\r\n\r\n16. These are interesting results, but you have a hypothesis:  clicks increase with snowy weather.  Let’s go to the Snowflake Marketplace and find what we need.  The Data Marketplace lives in the new UI called Snowsight (currently in Preview mode but feel free to test drive after the lab).  Click on Preview App at the top of the UI.\r\n![Snowflake Part 2 - 16](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image11.png)  \r\n\r\n17. Click **Sign in to continue**.  You will need to use the same user and pw that you used to login to your Snowflake account the first time.\r\n![Snowflake Part 2 - 17](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image17.png)  \r\n\r\n18. You’re now in the new UI - Snowsight.  It’s pretty cool - with charting and dashboards and context-sensitivity - but today we’re just focused on getting to the Data Marketplace.  Click on **Data**…\r\n![Snowflake Part 2 - 18](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image23.png)  \r\n\r\n19. ...and then **Marketplace**…  \r\n![Snowflake Part 2 - 19](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image55.png)  \r\n\r\n20. ...and now you’re in!  Over 100 providers have made datasets available for you to enrich your data.  Today we’re going to grab a sample of weather data.  Click the **Ready to Query** checkbox…  \r\n![Snowflake Part 2 - 20](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image82.png)  \r\n\r\n21. And then find the **Weather Source** tile.  Once you find it, click on it.  \r\n![Snowflake Part 2 - 21](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image84.png)  \r\n\r\n22. Here you’ll find a description of the data, example queries, and other useful information.  Let’s get this data into our Snowflake account.  You’ll be amazed at how fast and easy this is.  Click ![assets/image40.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image40.png).\r\n![Snowflake Part 2 - 22](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image56.png)  \r\n\r\n23. In the pop-up, rename the database to WEATHERSOURCE (**important!**),  check the “I accept…” box and then ![assets/image26.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image26.png).  No need to add additional roles (though you might in real-life).  \r\n![Snowflake Part 2 - 24](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image28.png)  \r\n\r\n    What is happening here?  Weather Source has granted access to this data from their Snowflake account to yours.  You’re creating a new database in your account for this data to live - but the best part is that no data is going to move between accounts!  When you query you’ll really be querying the data that lives in the Weather Source account.  If they change the data you’ll automatically see those changes.  No need to define schemas, move data,  or create a data pipeline either. Isn’t that slick?\r\n\r\n24. Now click **Done**. You can close this browser tab and go back to the other Snowflake UI that you started in.\r\n![Snowflake Part 2 - 25](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image31.png)  \r\n\r\n25. Refresh the database browser and notice you have a new shared database, ready to query and join with your data. Click on it and you’ll see views under the PUBLIC schema. We’ll use one of these next.  \r\n![Snowflake Part 2 - 26](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image25.png)  \r\n\r\n26. With production-sized data sets we are likely to have very large tables and complex queries; therefore, we might want to increase the amount of compute we’re using to perform this analysis.  In Snowflake this is fast and easy.  Run the statement in section **G**.  Notice how fast it scales up. No disruption. No shuffling of data.  We’ve changed from an XS (one node cluster) to an XL (16 node cluster) in seconds.  Ready to go!\r\n```\r\n/*-----\r\n--G--\r\n----- \r\n--With production-sized data sets we might want to join and query tables with billions of rows\r\n--In these cases it's easy and productive to scale up our virtual warehouse so these queries run faster*/\r\nalter warehouse mkt_analysis set warehouse_size = 'XLARGE';\r\n```\r\n\r\n27. So, let’s join that weather data to our Google Ads data and see if there is a correlation between clicks and snowfall.  Run the correlation query in section **H**. Remember that we’re actually joining data across two accounts, but the experience and performance is seamless and fast.  \r\n```\r\n/*-----\r\n --H--\r\n -----\r\n --Is there any correlation between clicks and snowfall for any ad group?*/\r\nselect ad_group_name,\r\n  abs(corr(clicks, w.tot_snowfall_in)) as clicks_snow_corr\r\nfrom GOOGLE_ADS__URL_AD_ADAPTER ga,\r\n  \"WEATHERSOURCE\".\"PUBLIC\".\"HISTORY_DAY\" w\r\nwhere ga.date_day = w.date_valid_std\r\ngroup by 1\r\norder by 2 desc;\r\n```\r\n\r\n    It’s a weak correlation so maybe our hypothesis isn’t worth exploring further. But our ability to quickly source Marketplace data and mash it with our own data has saved us lots of time and we can iterate further.\r\n    ![Snowflake Part 2 - 29](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image125.png)  \r\n\r\n28. Because this combination may yet have value, let’s create a view that combines the correlations with the URL data so others can take a look more easily.  Run the SQL in section **I**.  One creates the view and the other queries it.\r\n```\r\n/*-----\r\n --I--\r\n -----\r\n --Let's create new view with the correlations built in to the URL_AD_ADAPTER data*/\r\ncreate or replace view GOOGLE_ADS__URL_AD_ADAPTER_CORR as (\r\n    with corr as (\r\n      select ad_group_name,\r\n        abs(corr(clicks, w.tot_snowfall_in)) as clicks_snow_corr\r\n      from GOOGLE_ADS__URL_AD_ADAPTER ga,\r\n        \"WEATHERSOURCE\".\"PUBLIC\".\"HISTORY_DAY\" w\r\n      where ga.date_day = w.date_valid_std\r\n      group by 1\r\n    ),\r\n    base as (\r\n      select *\r\n      from GOOGLE_ADS__URL_AD_ADAPTER\r\n    )\r\n    select b.*,\r\n      c.clicks_snow_corr\r\n    from base b,\r\n      corr c\r\n    where b.ad_group_name = c.ad_group_name\r\n  );\r\nselect *\r\nfrom GOOGLE_ADS__URL_AD_ADAPTER_CORR;\r\n```\r\n\r\n29. Now that our heavy lifting is done, let’s scale down the warehouse size. No need to pay for more compute than we need. Run the SQL in section **J**.\r\n```\r\n/*-----\r\n--J--\r\n-----\r\n--Once we're done with our complex queries we scale the warehouse back down*/\r\nalter warehouse mkt_analysis set warehouse_size = 'XSMALL';\r\n```\r\n\r\n30. For our last trick let’s imagine we’ve accidentally dropped the table we’re working with.  No one has ever done that!  Run the DROP statement in section **K** and then refresh the database browser.  Notice the table is gone.  Now run the UNDROP and refresh.  It’s back! This is the power of time travel.  You can restore lost data for up to 90 days of history.  \r\n```\r\n/*-----\r\n--K--\r\n-----\r\n--Let's look at one last great feature of Snowflake.  Have you ever accidentally dropped a table?  Or a database?  \r\n--Let's 'accidentally' drop a table.*/\r\ndrop table GOOGLE_ADS__URL_AD_ADAPTER; \r\n\r\n/*--Fortunately, in Snowflake, this is an easy fix.*/\r\nundrop table GOOGLE_ADS__URL_AD_ADAPTER;\r\n```\r\n    \r\n    : _Time Travel also enables you to query and replace tables as of a particular point in time or prior to a specific SQL statement._  \r\n    \r\n31. One last thing. How is our Snowflake consumption looking?  It’s easy to understand.  We’ll look in two places. First, you might have a Notification, so click on that tile.  When you have notifications enabled and resource monitors built, you’ll get notifications.  \r\n![Snowflake Part 2 - 32](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image123.png)  \r\n\r\n32. See anything that looks like this?  These notifications come from the resource monitor we set up earlier in the lab.  You’ll also get emails with the same information.  \r\n![Snowflake Part 2 - 33](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image101.png)  \r\n![Snowflake Part 2 - 33](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image101.png)\u003C!-- .element style=\"width: 25%; border: 0; background: None; box-shadow: None\" --\u003E\r\n![assets/image101.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image101.png)\r\n\r\n33. Now, click on the **Account** tile.  \r\n![Snowflake Part 2 - 34](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image16.png)  \r\n\r\n34. Then click on **Usage**.  \r\n![Snowflake Part 2 - 35](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image91.png)  \r\n\r\n35. Here you can see monthly/daily credit usage…  \r\n![Snowflake Part 2 - 36](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image19.png)  \r\n\r\n36. ...and monthly/daily storage usage (you won’t see much for storage yet since our data is small).  In addition to these screens you can query the Snowflake metadata layer for the same info and build reports in your favorite BI tool.  \r\n![Snowflake Part 2 - 37](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image35.png)  \r\n\r\n**This concludes the lab.  We hope you learned a lot and are interested in exploring Fivetran and Snowflake for your use cases.  Please feel free to ask questions.**\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n\r\n## Conclusion & Next Steps\r\n\r\nCongratulations on completing this lab on automating data pipelines to drive marketing analytics! You’ve mastered the Fivetran and Snowflake basics and are ready to apply these fundamentals to your own data. Be sure to reference this guide if you ever need a refresher.\r\n\r\nWe encourage you to continue with your free trials by loading your own sample or production data and by using some of the more advanced capabilities of Fivetran and Snowflake not covered in this lab. \r\n### Additional Resources:\r\n\r\n- Read the [Definitive Guide to Maximizing Your Free Trial](/test-driving-snowflake-the-definitive-guide-to-maximizing-your-free-trial/) document\r\n- Attend a [Snowflake virtual or in-person event](/about/events/) to learn more about our capabilities and customers\r\n- [Join the Snowflake community](https://community.snowflake.com/s/topic/0TO0Z000000wmFQWAY/getting-started-with-snowflake)\r\n- [Sign up for Snowflake University](https://community.snowflake.com/s/article/Getting-Access-to-Snowflake-University)\r\n- [Contact our Sales Team](/free-trial-contact-sales/) to learn more\r\n\r\n### What we've covered:\r\n\r\n- How to begin a Fivetran trial through Snowflake's Partner Connect\r\n\r\n- How to load Google Ads data into Snowflake using Fivetran\r\n\r\n- How to run a dbt project via Fivetran to generate models\r\n\r\n- How to create analyst sandboxes in Snowflake using clones\r\n\r\n- How to leverage data in Snowflake's Data Marketplace \r\n\r\n## Appendix I: Github Account Creation\r\n\r\nEnter a username, email address, and password.\r\n![Appendix I - 1](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image120.png)  \r\n\r\nSolve the puzzle and continue!\r\n![Appendix I - 2](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image114.png)  \r\n\r\nYou can skip down to the bottom of this screen, or take the time to choose your interests.\r\n![Appendix I - 3](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image99.png)  \r\n\r\nChoose **Complete setup**.\r\n![Appendix I - 4](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image8.png)  \r\n\r\nHead to your email application and verify your email address.\r\n![Appendix I - 5](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image131.png)  \r\n\r\nThat’s it! You now have a Github account.\r\n![Appendix I - 6](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image106.png)  \r\n\r\n## Appendix II: Snowflake Account Creation\r\n\r\n1. Go to \u003Chttps://signup.snowflake.com/\u003E.  \r\n\r\n2. Enter your personal information.  You need to use a valid email address that you can access and haven’t used previously.\r\n![Appendix II - 1](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image14.png)  \r\n\r\n3. When you sign up for Snowflake it auto-populates to **Enterprise**. This is the best choice for this lab. Choose the cloud/region closest to you and click the \"GET STARTED\" button. \r\n![Appendix II - 2](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image115.png)  \r\n\r\n4. Your account will provision in 1-2 minutes and you will receive an email activation link like this to create a user.  Click on the **CLICK TO ACTIVATE** link.  \r\n![Appendix II - 3](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image76.png)  \r\n\r\n5. Next, you'll see this page your browser. Specify your username and password. Be sure to store your credentials in a safe location.  \r\n![Appendix II - 4](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image47.png)  \r\n\r\n6. You will be logged in to your new account and it will look like this.  You will also receive another email with your username and account URL.  \r\n![Appendix II - 5](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image5.png)  \r\n\r\n### Congratulations, you are ready to work in Snowflake!\r\n\r\n## Appendix III: Fivetran - Configure Google Adwords Connector\r\n\r\nLog into Fivetran and you should see the following screen, which shows our selection of source connectors. Use the type-down or the drop-down menu to find the [Google Ads](https://fivetran.com/docs/applications/google-ads) connector. If you have already setup your first connector, you may need to go to the Connectors tab in the Fivetran web application, and choose **+ Connector**.  \r\n![Appendix III - 1](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image44.png)  \r\n\r\nThe Fivetran Google Ads connector setup screen is shown below, before we begin to modify it. On the left are the configuration controls. On the right are inline, helpful setup instructions.  \r\n![Appendix III - 2](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image78.png)  \r\n\r\nObtain your customer ID per the instructions on the right.  \r\n![Appendix III - 3](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image54.png)  \r\n\r\nAnd enter it in the field on the left. Click **AUTHORIZE** to sign in to Google.  \r\n![Appendix III - 4](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image60.png)  \r\n\r\nChoose the account with which you wish to authenticate.  \r\n![Appendix III - 5](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image139.png)  \r\n\r\nAuthorize Fivetran to access your Google Ads data by clicking **Allow**.  \r\n![Appendix III - 5](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image96.png)  \r\n\r\nNow we need to configure three reports. These three reports will give us the raw data that we will use to generate our models later. Each report will require:\r\n* A Destination Table Name\r\n* A Report Type\r\n* A selection of Fields  \r\n\r\nFor each report, we will use Configuration Type: Use Custom Report.\r\n\r\n\u003E aside positive\r\n\u003E \r\n\u003E  **Did You Know?**  \r\n*******************************************************************************  \r\nYou can find the configuration requirements for these three reports in the documentation of the Fivetran dbt package for Google Ads we will be using later, [dbt_google_ads_source](https://hub.getdbt.com/fivetran/google_ads_source/latest/). You can always find a list of all of Fivetranour available packages on [dbt Hub](https://hub.getdbt.com/) and in our [documentation](https://fivetran.com/docs/transformations/dbt).  \r\n*******************************************************************************  \r\nAds connectors, also known as Marketing connectors,  are different from most other Fivetran connectors in that, rather than replicate a set schema, you configure a series of reports. Each report becomes a table in the destination schema. This is necessary as the raw data that is returned from Ads connectors changes depending on the Fields chosen in the report. After the Hands-on Lab, you can always return to the connector setup and add more reports.\r\n\r\nFor the first report, use the following configuration:\r\n* Destination Table Name: final_url_performance\r\n* Report Type: FINAL_URL_REPORT\r\n* Fields:\r\n    * AccountDescriptiveName\r\n    * AdGroupId\r\n    * AdGroupName\r\n    * AdGroupStatus\r\n    * CampaignId\r\n    * CampaignName\r\n    * CampaignStatus\r\n    * Clicks\r\n    * Cost\r\n    * Date\r\n    * EffectiveFinalUrl\r\n    * ExternalCustomerId\r\n    * Impressions  \r\n\r\n![Appendix III - 7](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image39.png)  \r\n\r\nFor the second report, use the following configuration:\r\n* Destination Table Name: criteria_performance\r\n* Report Type: CRITERIA_PERFORMANCE_REPORT\r\n* Fields:\r\n    * AccountDescriptiveName\r\n    * AdGroupId\r\n    * AdGroupName\r\n    * AdGroupStatus\r\n    * CampaignId\r\n    * CampaignName\r\n    * CampaignStatus\r\n    * Clicks\r\n    * Cost\r\n    * Criteria\r\n    * CriteriaDestinationUrl\r\n    * CriteriaType\r\n    * Date\r\n    * ExternalCustomerId\r\n    * Id\r\n    * Impressions  \r\n\r\n![Appendix III - 8](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image73.png)  \r\n\r\nFor the third report, use the following configuration:\r\n* Destination Table Name: click_performance\r\n* Report Type: CLICK_PERFORMANCE_REPORT\r\n* Fields:\r\n    * AccountDescriptiveName\r\n    * AdGroupId\r\n    * AdGroupName\r\n    * AdGroupStatus\r\n    * CampaignId\r\n    * CampaignName\r\n    * CampaignStatus\r\n    * Clicks\r\n    * CriteriaId\r\n    * Date\r\n    * ExternalCustomerId\r\n    * GclId  \r\n\r\n![Appendix III - 9](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image46.png)  \r\n\r\nThat’s it for setting up reports! To review, your connector configuration screen should look like the screen shot below:  \r\n![Appendix III - 10](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image83.png)  \r\n\r\nGreat! Now click **SAVE & TEST** to save your configuration.  \r\n![Appendix III - 11](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image9.png)  \r\n\r\nClick **CONTINUE** to see the next screen, where you can **Start Initial Sync** to start the data flowing!  \r\n![Appendix III - 12](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image113.png)  \r\n\r\n\u003E aside positive\r\n\u003E \r\n\u003E  **Did You Know?**  \r\n*******************************************************************************  \r\nFivetran does more than just sync Ad connector reports. Fivetran has over 170 source connectors that you can use to keep your data in sync in Snowflake.  With Fivetran’s Database (e.g.,x. Oracle, PostgreSQL) and Application (e.g., ex Salesforce, Marketo, NetSuite) connectors, your data is automatically kept up-to-date without any intervention on your part. Any schema changes to your source data are automatically updated in Snowflake with our Automatic Schema Migration technology. Never miss or lose data, and never worry about updating your data pipelines again.\r\n*******************************************************************************  \r\nMoreover, Fivetran keeps your data up-to-date on your schedule, with recurring syncs on the timetable you specify. Keep reading for more information.\r\n\r\nBefore we start the first sync, let’s take a look at the configuration Fivetran provides for connectors.\r\n![Appendix III - 13](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image7.png)  \r\n\r\nFirst let’s look at the **Schema** tab. Here, depending on the connector type, you can configure up to three settings:\r\n* Block a table from syncing to Snowflake.\r\n* Block a column from syncing to Snowflake.\r\n* Hash a column’s values before they are stored in Snowflake.\r\n    * Used to protect Personally Identifiable Information from being accessed by the users of the data, while maintaining the ability to SQL JOIN on those values.  \r\n![Appendix III - 14](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image85.png)  \r\n\r\nWith Ads connectors, you can enable or disable a particular report from syncing.\r\n\r\nNext let’s visit the **Setup** tab. There is one very important configuration tool here, and that is the Sync Frequency. This can be set to as frequently as every 5 minutes, to as infrequently as every 24 hours. Fivetran starts and stops the Snowflake warehouse used for data loading on demand, and is extremely efficient at data loading, so you only pay for the time Fivetran is actually loading data.  \r\n![Appendix III - 15](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image129.png)  \r\n\r\nOK! That is it! The Schema setup in the **Schema** tab, and the Sync Frequency setting in the **Setup** tab are the only configuration details you need to manage, and even those can be left on the defaults for flawless operation! With that visited, let’s go back to the **Status** tab and start the sync!\r\n\r\nOnce the **Start Initial Sync** button is clicked, you should see your sync start up.  \r\n![Appendix III - 16](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image15.png)  \r\n\r\nWhile the sync is running, we can take a quick detour into the **Logs** tab. This is a real-time view into the data loading process. Fivetran offers ways to export these logs into your preferred log aggregation service. We even have a free connector called the [Fivetran Log](https://fivetran.com/docs/logs/fivetran-log) connector, that exports log data, schema operation metadata, and Fivetran credit consumption data!  \r\n![Appendix III - 17](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image70.png)  \r\n\r\nFor this demo, I chose to only sync 3 months of historical data, so the initial sync finished relatively quickly at two and a half minutes. When your sync has completed, you will see its status on the timeline, as per below.  \r\n![Appendix III - 13](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-fivetran/image63.png)  ","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"isDeveloperGuidesPage":false,":items":{},":itemsOrder":[],":type":"snowflake-site/components/contentfragment","model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-d181227d6b","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-c135db8139",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-ed2d208ab7","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2024-07-08",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-a843cdba97","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. 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