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Cloud.\u003C/p\u003E\n","\u003Cp\u003EIt's a typical data integration scenario: there are multiple, related data sources, and you need to generate some joined-up insights. You will build ETL pipelines that extract, load, transform and integrate the data, and you'll get a feeling for the typical development lifecycle with Matillion.\u003C/p\u003E\n","\u003Cp\u003EMatillion is the data pipeline platform that brings push-down ELT and AI to Snowflake, helping you to accelerate data pipelines for analytics and AI projects at scale.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/Matillion-Data-Productivity-Cloud.png\" alt=\"Matillion Data Productivity Cloud\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EWhat You Will Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EMatillion Data Productivity Cloud pipelines that extract, load, transform and integrate data\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You Will Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to use the Matillion Data Productivity Cloud Pipeline Designer\u003C/li\u003E\u003Cli\u003EHow to build and use Orchestration pipelines for data extraction, and for command-and-control operations such as looping and synchronization\u003C/li\u003E\u003Cli\u003EHow to build and use Transformation pipelines for data transformation and integration, including handling semi-structured data\u003C/li\u003E\u003Cli\u003EHow to use the Matillion Copilot to help build ETL pipelines\u003C/li\u003E\u003Cli\u003EHow to use git with Matillion in feature branching mode\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA web browser\u003C/li\u003E\u003Cli\u003EAn email address\u003C/li\u003E\u003Cli\u003EAccess to a \u003Ca href=\"https://hub.matillion.com/\"\u003EMatillion account\u003C/a\u003E (or free trial, set up in the next step of this lab)\u003C/li\u003E\u003Cli\u003EAccess to a \u003Ca href=\"https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides\"\u003ESnowflake account\u003C/a\u003E (or free trial, automatically created when you sign up for a free Matillion trial)\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreate your Matillion and Snowflake accounts\u003C/h2\u003E\n","\u003Cp\u003EIf you already have a Matillion and Snowflake account, just \u003Ca href=\"https://hub.matillion.com/\"\u003Elog in to the Matillion Data Productivity Cloud\u003C/a\u003E and go straight to the next step in this Quickstart.\u003C/p\u003E\n","\u003Cp\u003EOtherwise ...\u003C/p\u003E\n","\u003Ch3\u003EUse Snowflake Partner Connect\u003C/h3\u003E\n","\u003Cp\u003EIf you already have a Snowflake account, look for Matillion Data Productivity Cloud in \u003Ca href=\"https://docs.snowflake.com/en/user-guide/ecosystem-partner-connect#connecting-with-a-snowflake-partner\"\u003ESnowflake Partner Connect\u003C/a\u003E and follow the instructions on screen to create a Matillion free trial. Once you are logged into your Matillion Data Productivity Cloud account, skip to the next step in this Quickstart.\u003C/p\u003E\n","\u003Cp\u003EIf you don't already have a Snowflake account, or you prefer to start your own Matillion free trial, read on ...\u003C/p\u003E\n","\u003Ch3\u003EStart a Matillion free trial\u003C/h3\u003E\n","\u003Cp\u003EGo to the Matillion start page at \u003Ca href=\"https://hub.matillion.com/\"\u003Ehub.matillion.com\u003C/a\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESwitch to the \u003Cstrong\u003ESign Up\u003C/strong\u003E tab\u003C/li\u003E\u003Cli\u003EAuthenticate with Google or Microsoft, or enter your business email address and pick a complicated password\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EIf you used an email address, you'll receive an email with a link to verify your address. Follow the link to continue.\u003C/p\u003E\n","\u003Cp\u003ENext you will be transferred to the Matillion setup screens.\u003C/p\u003E\n","\u003Cp\u003EIn the setup screens:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EProvide some basic information about your goals, and let Matillion know how to address you\u003C/li\u003E\u003Cli\u003ESelect a cloud region\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ESet up your Snowflake account\u003C/h3\u003E\n","\u003Cp\u003EThe last setup screen invites you to choose a target platform.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EIf you \u003Cstrong\u003Ealready\u003C/strong\u003E have a Snowflake account:\n\u003Cul\u003E\u003Cli\u003EPick "Connect to Snowflake"\u003C/li\u003E\u003Cli\u003ELog in to your Snowflake account in the dialog that follows\u003C/li\u003E\u003Cli\u003EGo to the next step in this Quickstart\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003EIf you \u003Cstrong\u003Edon't\u003C/strong\u003E already have a Snowflake account, select "Use a Matillion trial warehouse" and Matillion will set up a free Snowflake trial on your behalf.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/Use-Trial-Warehouse.png\" alt=\"Use a Trial Warehouse\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIt will take a couple of minutes for Matillion to create a free, empty, trial Snowflake account and connect everything together.\nOnce that's done you'll end up in the Matillion Data Productivity Cloud's main ETL \u003Ca href=\"https://docs.matillion.com/data-productivity-cloud/designer/docs/designer-overview/\"\u003Epipeline designer interface\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003EFor reference \u003Ca href=\"https://www.youtube.com/watch?v=G2Rc7K_8pZw\"\u003Ehere's a video\u003C/a\u003E that takes you through all the steps above.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreate a Development branch\u003C/h2\u003E\n","\u003Cp\u003EMatillion development is tightly integrated with git, so when you are \u003Ca href=\"https://hub.matillion.com/\"\u003Elogged in to the Matillion Data Productivity Cloud\u003C/a\u003E and developing ETL pipelines, you're always working in the context of a git branch. This Quickstart will demonstrate the \u003Cstrong\u003Efeature branching\u003C/strong\u003E strategy.\u003C/p\u003E\n","\u003Cp\u003EStart at the management page of your Data Productivity Cloud \u003Ca href=\"https://docs.matillion.com/data-productivity-cloud/designer/docs/projects/\"\u003EProject\u003C/a\u003E, and press "Add New Branch".\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/Add-New-Branch.png\" alt=\"Add New Branch\"\u003E\u003C/p\u003E\n","\u003Cp\u003EName the new branch \u003Ccode\u003Edevelopment\u003C/code\u003E and have it branch from \u003Ccode\u003Emain\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/Create-a-Branch.png\" alt=\"Create a Development Branch\"\u003E\u003C/p\u003E\n","\u003Cp\u003ETowards the end of this Quickstart you'll learn how to merge this feature branch back into main.\u003C/p\u003E\n","\u003Cp\u003EMeanwhile, click on the new branch name to enter the \u003Ca href=\"https://docs.matillion.com/data-productivity-cloud/designer/docs/designer-ui-basics/\"\u003EPipeline Designer\u003C/a\u003E interface in the development branch.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreate Static Metadata\u003C/h2\u003E\n","\u003Cp\u003EThe first data to be loaded into Snowflake is a few fixed records of reference information, which will be used later. This kind of setup is sometimes known as "seed" data.\u003C/p\u003E\n","\u003Cp\u003EMatillion uses \u003Cstrong\u003ETransformation\u003C/strong\u003E pipelines for everything that involves running SQL on Snowflake. In the pipeline designer interface, create a new Transformation pipeline.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/New-Transformation-Pipeline.png\" alt=\"Create a New Transformation Pipeline\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIn the dialog that follows, name the new pipeline \u003Ccode\u003ECreate SHEET_NAMES\u003C/code\u003E and press the Add button.\u003C/p\u003E\n","\u003Cp\u003ENow you can start to add components to the blank canvas. For seed data you need a \u003Ca href=\"https://docs.matillion.com/data-productivity-cloud/designer/docs/fixed-flow/\"\u003EFixed Flow\u003C/a\u003E component. There are two ways to do this, both highlighted in the screenshot below:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EOpen the \u003Ccode\u003EComponents\u003C/code\u003E panel on the left, find the \u003Cstrong\u003EFixed Flow\u003C/strong\u003E, and drag it onto the canvas\u003C/li\u003E\u003Cli\u003EPress the \u003Ccode\u003E+\u003C/code\u003E sign in the middle of the canvas, which opens a similar Components dialog. This method has the advantage of automatically connecting the components together when you have more than one\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/Add-Fixed-Flow-Component.png\" alt=\"Add a Fixed Flow Component\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EAdd a Fixed Flow component\u003C/h3\u003E\n","\u003Cp\u003EWhenever a component on the canvas is selected, a panel opens showing its properties.\u003C/p\u003E\n","\u003Cp\u003ESet the \u003Cstrong\u003Ename\u003C/strong\u003E of the Fixed Flow component to \u003Ccode\u003EGenerate worksheet names\u003C/code\u003E using the pencil icon at the top of the properties panel. You can do this with any component in a pipeline, and it helps maintainers understand what's going on.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/Renaming-a-Component.gif\" alt=\"Renaming a Component\"\u003E\u003C/p\u003E\n","\u003Cp\u003EComplete the properties of the new Fixed Flow by setting:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EColumns\u003C/strong\u003E\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EName\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EDatatype\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWORKSHEET_NAME\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EVARCHAR\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003E\u003Cstrong\u003EValues\u003C/strong\u003E\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWORKSHEET_NAME\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EITEMS\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EORDERS\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EORDER_ITEMS\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EBe careful with the exact names here, making sure they are in uppercase exactly as shown. Copy-and-paste is best.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EAfter you have completed the properties, the border of the component should change to green as Matillion validates it successfully.\u003C/p\u003E\n","\u003Cp\u003EOpen the \u003Cstrong\u003ESample data\u003C/strong\u003E panel at the bottom of the screen, and press the \u003Ccode\u003ESample data\u003C/code\u003E button to view the data. Check that three rows appear.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EYou can use the Sample data panel with almost all transformation components. It's a great way to keep in touch with the data while you are developing a transformation pipeline\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/Data-Sample-Fixed-Flow.png\" alt=\"Data Sample Fixed Flow\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EAdd a Rewrite Table component\u003C/h3\u003E\n","\u003Cp\u003ENow it's time to save those fixed three rows of data to a real Snowflake table. Follow the same steps to add another component: a \u003Ca href=\"https://docs.matillion.com/data-productivity-cloud/designer/docs/rewrite-table/\"\u003ERewrite Table\u003C/a\u003E this time.\u003C/p\u003E\n","\u003Cp\u003EIf you used the \u003Ccode\u003E+\u003C/code\u003E icon to add the new component, the two will be connected together already. If you dragged it in from the panel on the left, you'll have to manually link them together by dragging an arrow from the little circle nub on the Fixed Flow to the Rewrite Table. You can reposition components on the canvas at any time by dragging them with your mouse.\u003C/p\u003E\n","\u003Cp\u003EComplete the properties of the new Rewrite Table with these values:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EName\u003C/strong\u003E - \u003Ccode\u003ECreate SHEET_NAMES\u003C/code\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETarget Table\u003C/strong\u003E - \u003Ccode\u003EGW_SHEET_NAMES\u003C/code\u003E\u003C/p\u003E\n","\u003Cp\u003EYour transformation pipeline should look something like this:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/Run-Create-Seed-Data.png\" alt=\"Run Create Seed Data\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ERun the pipeline\u003C/h3\u003E\n","\u003Cp\u003EPress the \u003Ccode\u003ERun\u003C/code\u003E button at the top of the canvas, to manually run the pipeline. It will create a new Snowflake table named \u003Ccode\u003EGW_SHEET_NAMES\u003C/code\u003E containing those three rows. The Task History panel will open, and you should see the table name and the rowcount among the details. You can close the Task History panel afterwards.\u003C/p\u003E\n","\u003Cp\u003ENow you have some reference data in Snowflake, it's time for the next step.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ELoad Sales data from Excel\u003C/h2\u003E\n","\u003Cp\u003EIn this step you will start to work with some real data.\u003C/p\u003E\n","\u003Cp\u003EIn the Matillion pipeline designer interface, create a new pipeline - the same as you did in the previous step but an \u003Cstrong\u003EOrchestration\u003C/strong\u003E pipeline this time. In addition to extracting and loading data, this pipeline will also control the overall flow of data for this Quickstart, so give it a generic name: \u003Ccode\u003EGreenWave Technologies Demo\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003ENow you can start to add components to the canvas.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EEvery Orchestration pipeline must have a \u003Ca href=\"https://docs.matillion.com/data-productivity-cloud/designer/docs/start/\"\u003EStart\u003C/a\u003E component. One is added automatically whenever you create a new Orchestration pipeline.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EAdd the Create SHEET_NAMES pipeline\u003C/h3\u003E\n","\u003Cp\u003EMatillion Orchestration pipelines fulfill a command-and-control role. In this Quickstart the first thing is to make sure the static metadata is always available.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EExpand the Pipelines panel on the left of the screen\u003C/li\u003E\u003Cli\u003EDrag the Transformation pipeline \u003Ccode\u003ECreate SHEET_NAMES\u003C/code\u003E you created in the previous step onto the canvas\u003C/li\u003E\u003Cli\u003ELink it after the Start component\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThe three rows of static metadata are actually the names of worksheets in an Excel spreadsheet. This Quickstart will use data from all of them, so this pipeline needs a way to iterate through them.\u003C/p\u003E\n","\u003Ch3\u003EAdd a Variable\u003C/h3\u003E\n","\u003Cp\u003EIteration loops require a variable to loop over. Open the variables panel on the left, and add a new Pipeline variable of type \u003Cstrong\u003EText\u003C/strong\u003E.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EName it \u003Ccode\u003Eworksheet_name\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EMake it \u003Ccode\u003EPrivate\u003C/code\u003E to this pipeline\u003C/li\u003E\u003Cli\u003ESet the Behavior to \u003Ccode\u003ECopied\u003C/code\u003E, which means that any loops using it will be able to run in parallel. This is known as concurrent iteration.\u003C/li\u003E\u003Cli\u003EAdd a default value of \u003Ccode\u003EITEMS\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EIt's good practice to give every variable a default value. Choose a default that helps components validate cleanly.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EYour variable should look like this:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/Pipeline-Variable-Setup.png\" alt=\"Pipeline Variable Setup\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EAdd a Table Iterator\u003C/h3\u003E\n","\u003Cp\u003EThere are many different ways to loop in Matillion. The component which loops through the rows in a table is the \u003Ca href=\"https://docs.matillion.com/data-productivity-cloud/designer/docs/table-iterator/\"\u003ETable Iterator\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003EUse the components panel on the left (or the \u003Ccode\u003E+\u003C/code\u003E after the transformation pipeline component) to find and add a Table Iterator.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESet the Target Table to \u003Ccode\u003EGW_SHEET_NAMES\u003C/code\u003E. It should be present in the dropdown provided you ran the transformation pipeline earlier to create the table\u003C/li\u003E\u003Cli\u003ESet the Concurrency mode to \u003Ccode\u003EConcurrent\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EIn the Column Mapping, add a row that maps the \u003Ccode\u003EWORKSHEET_NAME\u003C/code\u003E column to the \u003Ccode\u003Eworksheet_name\u003C/code\u003E variable\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ENow the iterator will loop through the (three) rows in the reference table in parallel, setting the variable from the chosen column in each case.\u003C/p\u003E\n","\u003Cp\u003ELink the Table Iterator so it rus after the transformation pipeline component. You'll notice that \u003Cstrong\u003Ethree\u003C/strong\u003E output connector nubs are available: green, grey and red, and you can link using any of them. At runtime the \u003Cstrong\u003Egreen\u003C/strong\u003E path at the top is only followed if the component finished successfully, and that's the one you'll use in most cases. \u003Cstrong\u003EGrey\u003C/strong\u003E is for unconditional, where you don't care if it was successful or not. \u003Cstrong\u003ERed\u003C/strong\u003E is the failure branch, which is how to implement error handling.\u003C/p\u003E\n","\u003Cp\u003EYour pipeline should look like this:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/Iterator-Setup.png\" alt=\"Iterator Setup\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EAdd an Excel Query\u003C/h3\u003E\n","\u003Cp\u003ENow add a new \u003Ca href=\"https://docs.matillion.com/data-productivity-cloud/designer/docs/excel-query/\"\u003EExcel Query\u003C/a\u003E component, without linking it to anything yet. Place it slightly below the iterator on the canvas.\u003C/p\u003E\n","\u003Cp\u003EWith the iterator selected, drag its \u003Cstrong\u003Eblue\u003C/strong\u003E connector nub onto the Excel Query component. The two components should nest together, as shown in the "before" and "after" screenshot below.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/Connected-Iterator.png\" alt=\"Connected Iterator\"\u003E\u003C/p\u003E\n","\u003Cp\u003ESelect the Excel Query and set its properties:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EStorage Type: Amazon S3 Storage\u003C/li\u003E\u003Cli\u003EStorage URL: \u003Ccode\u003Edevrel.matillion.com/solutions/GreenWave/store_lite.xlsx\u003C/code\u003E (don't worry if you see an error message about listing buckets)\u003C/li\u003E\u003Cli\u003EBasic Advanced Mode: Advanced\u003C/li\u003E\u003Cli\u003EContains Header Row: Yes\u003C/li\u003E\u003Cli\u003ESQL Query: \u003Ccode\u003ESELECT * FROM ${worksheet_name}\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ETarget Table: \u003Ccode\u003EGW_${worksheet_name}\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENotice how the variable is used in the settings. During the extract phase, it defines the source worksheet name. During the load phase it defines part of the Snowflake table name.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EThe pipeline should validate cleanly now.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERun\u003C/strong\u003E the pipeline now, and it will create three new Snowflake tables: \u003Ccode\u003EGW_ITEMS\u003C/code\u003E, \u003Ccode\u003EGW_ORDERS\u003C/code\u003E and \u003Ccode\u003EGW_ORDER_ITEMS\u003C/code\u003E, with structure and data taken from the spreadsheet.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ELoad Customer data from JSON\u003C/h2\u003E\n","\u003Cp\u003EIn this step, you will add a new, semi-structured data source to your \u003Ccode\u003EGreenWave Technologies Demo\u003C/code\u003E Orchestration pipeline.\u003C/p\u003E\n","\u003Cp\u003EFind and drag two new components onto the canvas:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EA \u003Ca href=\"https://docs.matillion.com/data-productivity-cloud/designer/docs/create-table/\"\u003ECreate Table\u003C/a\u003E, linked to the \u003Ccode\u003EStart\u003C/code\u003E, and followed by:\u003C/li\u003E\u003Cli\u003EAn \u003Ca href=\"https://docs.matillion.com/data-productivity-cloud/designer/docs/s3-load/\"\u003ES3 Load\u003C/a\u003E component\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAzure\u003C/strong\u003E or \u003Cstrong\u003EGCP\u003C/strong\u003E users:\nInstead of an S3 Load, use a \u003Ca href=\"https://docs.matillion.com/data-productivity-cloud/designer/docs/data-transfer/\"\u003EData Transfer\u003C/a\u003E component first to copy the source file into your own cloud storage.\nThe source file is publicly available at \u003Ccode\u003Ehttps://s3.eu-west-1.amazonaws.com/devrel.matillion.com/solutions/GreenWave/customer_accounts.json\u003C/code\u003E .. you don't need an AWS account to access it.\u003C/p\u003E\n","\u003Cp\u003EAfterwards add either an \u003Ca href=\"https://docs.matillion.com/data-productivity-cloud/designer/docs/azure-blob-storage-load/\"\u003EAzure Blob\u003C/a\u003E or a \u003Ca href=\"https://docs.matillion.com/data-productivity-cloud/designer/docs/google-cloud-storage-load/\"\u003EGoogle Cloud\u003C/a\u003E Storage Load component instead of S3.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EAdjust the positioning of the components so they end up looking something like this:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/DDL-and-Load-Customer-JSON.png\" alt=\"DDL and Load Customer JSON\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENotice how there are \u003Cstrong\u003Etwo\u003C/strong\u003E components linked from the \u003Ccode\u003EStart\u003C/code\u003E component. That means two threads will execute in parallel whenever the pipeline runs. Also remember the Excel queries will themselves run inside a concurrent loop. And of course, all this is running against Snowflake which introduces its \u003Cem\u003Eown\u003C/em\u003E parallelism. All this multi threading means the pipeline will finish faster!\u003C/p\u003E\n","\u003Ch3\u003EDDL for semi-structured data\u003C/h3\u003E\n","\u003Cp\u003EAdjust the properties of the Create Table component like this:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EName: \u003Ccode\u003EReplace GW_CUSTOMER_ACCOUNTS\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ECreate/Replace: \u003Ccode\u003EReplace\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ENew Table Name: \u003Ccode\u003EGW_CUSTOMER_ACCOUNTS\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EColumns: Add just one, named \u003Ccode\u003EDATA_VALUE\u003C/code\u003E and of type \u003Ccode\u003EVARIANT\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EIt should validate cleanly now, and have a green border. Run just this component, to create the table, by clicking the small "Run only component" icon that appears when you select it.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/Run-Only-Component.png\" alt=\"Run Only Component\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ELoading semi-structured data\u003C/h3\u003E\n","\u003Cp\u003ENow the \u003Ccode\u003EGW_CUSTOMER_ACCOUNTS\u003C/code\u003E table exists, you can load data into it. Adjust the properties of the S3 Load component like this:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EName: \u003Ccode\u003ELoad Customer 360 Data\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ES3 Object Prefix: \u003Ccode\u003Edevrel.matillion.com\u003C/code\u003E (don't worry if you see a "Something went wrong" message)\u003C/li\u003E\u003Cli\u003EPattern: \u003Ccode\u003Esolutions/GreenWave/customer_accounts.json\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ETarget Table: \u003Ccode\u003EGW_CUSTOMER_ACCOUNTS\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ELoad Columns: \u003Ccode\u003EDATA_VALUE\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EFile Type: \u003Ccode\u003EJSON\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAzure\u003C/strong\u003E or \u003Cstrong\u003EGCP\u003C/strong\u003E users: adjust the cloud storage location settings to wherever you made the Data Transfer copy the file\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EThe component should validate cleanly, so once again run just this component, to load the table.\u003C/p\u003E\n","\u003Cp\u003EExpand the Task History to view the Task Details, and check that the row count is 1. All the customer records are in one big semi-structured JSON object. In the next step you'll see how to flatten and relationalize it.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EFlatten and relationalize semi-structured data\u003C/h2\u003E\n","\u003Cp\u003EIn the pipeline designer interface, create another new Transformation pipeline, named \u003Ccode\u003ECalculate Profit and Revenue\u003C/code\u003E this time.\u003C/p\u003E\n","\u003Cp\u003EFind the \u003Ca href=\"https://docs.matillion.com/data-productivity-cloud/designer/docs/table-input/\"\u003ETable Input\u003C/a\u003E in the components panel, and drag it onto the canvas. To help finding it, press the \u003Cstrong\u003ERead\u003C/strong\u003E components button to narrow the search.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESet the Target Table to \u003Ccode\u003EGW_CUSTOMER_ACCOUNTS\u003C/code\u003E, from the dropdown list. Update the component name to the same value\u003C/li\u003E\u003Cli\u003EIn the Column Names property, move \u003Ccode\u003EDATA_VALUE\u003C/code\u003E across to the \u003Cstrong\u003ESelected Column Names\u003C/strong\u003E list\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EGive it a moment to validate, and for the border to turn green. Use the \u003Cstrong\u003ESample data\u003C/strong\u003E panel at the bottom to view the single row of data.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/View-Semi-Structured-Data.png\" alt=\"View Semi-Structured Data\"\u003E\u003C/p\u003E\n","\u003Cp\u003EMatillion has several components that work with semi-structured data. The right one for this task is an \u003Ca href=\"https://docs.matillion.com/data-productivity-cloud/designer/docs/extract-nested-data/\"\u003EExtract Nested Data\u003C/a\u003E. Drag it onto the canvas after the Table Input so they link together.\u003C/p\u003E\n","\u003Cp\u003EIn the new component:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESet \u003Cstrong\u003EInclude Input Columns\u003C/strong\u003E to \u003Ccode\u003ENo\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EIn the \u003Cstrong\u003EColumns\u003C/strong\u003E property, press \u003Ccode\u003EAutofill\u003C/code\u003E, then expand the structure and select only \u003Ccode\u003Ecustomer_rid\u003C/code\u003E and \u003Ccode\u003Eaccount_name\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/Extract-Nested-Structure.png\" alt=\"Extract Nested Structure\"\u003E\u003C/p\u003E\n","\u003Cp\u003ERun a data sample against this component and check that the data has been flattened and relationalized. You should see 10,000 records, each with a \u003Ccode\u003Ecustomer_rid\u003C/code\u003E identifier and an \u003Ccode\u003Eaccount_name\u003C/code\u003E string.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EThis transformation work is happening entirely \u003Cstrong\u003Einside\u003C/strong\u003E Snowflake, which means it's very fast, efficient and cost-effective. If you're curious, open the SQL tab next to the component properties to see how it works.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003ETo prepare for the next step, rename the Extract Nested Data component to \u003Ccode\u003EACC\u003C/code\u003E - short for "accounts".\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EData Integration\u003C/h2\u003E\n","\u003Cp\u003EAt this stage four tables have been extracted and loaded: \u003Ccode\u003EGW_ORDERS\u003C/code\u003E, \u003Ccode\u003EGW_ITEMS\u003C/code\u003E and \u003Ccode\u003EGW_ORDER_ITEMS\u003C/code\u003E from Excel, and the newly flattened \u003Ccode\u003EGW_CUSTOMER_ACCOUNTS\u003C/code\u003E from the JSON file. They are related like this:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/Greenwave-ERD.png\" alt=\"Greenwave ERD\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ENotice that, in reality, the data was originally siloed and relatively inaccessible. Not only physically separated, but in the case of the customer data, in a difficult-to-read semi-structured format too.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EContinuing with the \u003Ccode\u003ECalculate Profit and Revenue\u003C/code\u003E transformation pipeline. The flattened customer accounts table is already in place, so:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EAdd \u003Cstrong\u003Ethree\u003C/strong\u003E more Table Input components: one for \u003Ccode\u003EGW_ORDERS\u003C/code\u003E, one for \u003Ccode\u003EGW_ITEMS\u003C/code\u003E and one for \u003Ccode\u003EGW_ORDER_ITEMS\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EChoose \u003Cstrong\u003Eall\u003C/strong\u003E the columns in every case\u003C/li\u003E\u003Cli\u003EFor readability it's best to align them vertically on the canvas\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ESome minor datatype fixes are needed before integrating, so add a \u003Ca href=\"https://docs.matillion.com/data-productivity-cloud/designer/docs/convert-type/\"\u003EConvert Type\u003C/a\u003E component linked after \u003Ccode\u003EGW_ORDERS\u003C/code\u003E, naming it \u003Ccode\u003EORDER\u003C/code\u003E and with the following conversions:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/Order-Datatype-Conversions.png\" alt=\"Order Datatype Conversions\"\u003E\u003C/p\u003E\n","\u003Cp\u003ERepeat for \u003Ccode\u003EGW_ORDER_ITEMS\u003C/code\u003E, naming it \u003Ccode\u003EORDER_ITEM\u003C/code\u003E with these conversions:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EColumn\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EType\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EITEM_RID\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENUMBER\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EORDER_RID\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENUMBER\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERID\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENUMBER\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EQUANTITY\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENUMBER\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003E... and for \u003Ccode\u003EGW_ITEMS\u003C/code\u003E, naming it \u003Ccode\u003EITEM_REF\u003C/code\u003E with these conversions:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EColumn\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EType\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERID\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENUMBER\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECOST\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENUMBER\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPRICE\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENUMBER\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003ENow for the data integration, find and drag on a \u003Ca href=\"https://docs.matillion.com/data-productivity-cloud/designer/docs/join/\"\u003EJoin\u003C/a\u003E component. Link all \u003Cstrong\u003Efour\u003C/strong\u003E inputs to it, and configure it as follows:\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EIt's important to complete the properties top downwards, as some of them cascade onto others further down the list\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cul\u003E\u003Cli\u003EName it \u003Ccode\u003EIntegrate\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EMain Table: choose \u003Ccode\u003EORDER_ITEM\u003C/code\u003E from the dropdown. This is the table with the lowest granularity\u003C/li\u003E\u003Cli\u003EMain Table Alias: \u003Ccode\u003EOI\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EJoins: add the other three tables like this:\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/Greenwave-Joins.png\" alt=\"Greenwave Joins\"\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EJoin Expressions: following the entity relationship diagram above\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EOI_Inner_O\u003C/strong\u003E: \u003Ccode\u003E"OI"."ORDER_RID" = "O"."RID"\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EOI_Inner_I\u003C/strong\u003E: \u003Ccode\u003E"OI"."ITEM_RID" = "I"."RID"\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EOI_Inner_A\u003C/strong\u003E: \u003Ccode\u003E"O"."CUSTOMER_RID" = "A"."customer_rid"\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EColumn Mappings:\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESource Column\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ETarget Column\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EO.ORDER_DATE\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EORDER_DATE\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EOI.QUANTITY\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EQUANTITY\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EI.NAME\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EITEM_NAME\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EI.PRICE\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPRICE\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EI.COST\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECOST\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EA.account_name\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EACCOUNT_NAME\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EOnce this is configured and validates successfully, open the \u003Cstrong\u003ESample data\u003C/strong\u003E panel and check the results. You should find the same number of records (999) as there were in \u003Ccode\u003EGW_ORDER_ITEMS\u003C/code\u003E, with these columns:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/Integrate-Data-Sample.png\" alt=\"Integrate Data Sample\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENow all the data is in place to start adding the derived columns including profit and revenue.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECalculate Profit and Revenue\u003C/h2\u003E\n","\u003Cp\u003EMatillion has several ways to add derived fields, and the most commonly used is the \u003Ca href=\"https://docs.matillion.com/data-productivity-cloud/designer/docs/calculator/\"\u003ECalculator\u003C/a\u003E component.\u003C/p\u003E\n","\u003Cp\u003EContinuing with the \u003Ccode\u003ECalculate Profit and Revenue\u003C/code\u003E transformation pipeline, add a Calculator after the Join integration you just completed.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/Add-Calculator-Component.png\" alt=\"Add Calculator Component\"\u003E\u003C/p\u003E\n","\u003Cp\u003EOne Calculator component can add multiple derived fields. Leave \u003Cstrong\u003EInclude Input Columns\u003C/strong\u003E set to \u003Ccode\u003EYes\u003C/code\u003E and follow \u003Cstrong\u003ECalculations\u003C/strong\u003E to enter the settings. Add three calculations, giving each one a name and a SQL expression:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EPROFIT - \u003Ccode\u003E(("PRICE" - "COST") * "QUANTITY")\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EREVENUE - \u003Ccode\u003E("PRICE" * "QUANTITY")\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EORDER_MONTH - \u003Ccode\u003ETRUNCATE("ORDER_DATE", 'mm')\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EIt should end up looking like this:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/Calculator-Component.gif\" alt=\"Calculator Component\"\u003E\u003C/p\u003E\n","\u003Cp\u003EUse the \u003Cstrong\u003ESample data\u003C/strong\u003E panel to check that the three new columns have been added to every row. You might need to use the scrollbar to view the new columns after all the existing ones.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EAggregating with Copilot assistance\u003C/h2\u003E\n","\u003Cp\u003EThe profit and revenue data is still at order item level, but the reporting need is at a higher granularity. So it must be aggregated as a final step before presentation.\u003C/p\u003E\n","\u003Cp\u003EYou can use the Matillion Designer Copilot to help with this, telling it to aggregate to the desired granularity, and which fields need to be summed.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EHighlight the Calculator component\u003C/li\u003E\u003Cli\u003EExpand the Copilot panel\u003C/li\u003E\u003Cli\u003EType the following into the "Enter a prompt here" box: \u003Ccode\u003EAggregate the sum of PROFIT and REVENUE by ITEM_NAME, ACCOUNT_NAME and ORDER_MONTH\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EPress the Send Prompt icon\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/Run-Copilot.png\" alt=\"Run Copilot\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe Copilot should add an \u003Ca href=\"https://docs.matillion.com/data-productivity-cloud/designer/docs/aggregate/\"\u003EAggregate\u003C/a\u003E component attached to the Calculator, with the specified groupings and aggregations. Sample the data from this Aggregate component to verify that the number of rows is smaller, and the profit and revenue are being summed.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EThe Matillion Designer Copilot uses generative AI, so the results are not always identical and deterministic. If you don't immediately get the configuration you need, just delete the component it created, adjust the prompt, and retry\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EAll the row sampling you've seen so far has been on the fly, with nothing actually saved to the database. In order to distribute this data for easy visualization, the last step is to save it into a new table. This needs a write component, so in the components panel look for a \u003Ca href=\"https://docs.matillion.com/data-productivity-cloud/designer/docs/rewrite-table/\"\u003ERewrite Table\u003C/a\u003E and drop it into place connected after the aggregation. It will do a Create Table As Select at runtime.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESet the \u003Cstrong\u003ETarget Table\u003C/strong\u003E to \u003Ccode\u003EGW_PROFIT_BY_ACCOUNT\u003C/code\u003E and rename the component to \u003Ccode\u003ERewrite GW_PROFIT_BY_ACCOUNT\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/Rewrite-GW_PROFIT_BY_ACCOUNT.png\" alt=\"Rewrite GW_PROFIT_BY_ACCOUNT\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe data transformation and integration is complete! The last piece of development work is to make sure it runs at the right time.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EOrchestrating the ETL pipelines\u003C/h2\u003E\n","\u003Cp\u003ETo get the transformation and integration to run at the right time, first go right back to the \u003Ccode\u003EGreenWave Technologies Demo\u003C/code\u003E orchestration pipeline.\u003C/p\u003E\n","\u003Cp\u003ERemember the data loading was happening in multiple threads? We need a way to synchronize these, and wait for \u003Cstrong\u003Eall\u003C/strong\u003E the data loading to finish before moving on to data transformation and integration. This is done with an \u003Ca href=\"https://docs.matillion.com/data-productivity-cloud/designer/docs/and/\"\u003EAnd\u003C/a\u003E component. Locate it in the components panel, drag it into place to the right of everything else, and link it to the success branch of the two load components like this:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/And-Wait-for-Loads.png\" alt=\"And Component Wait for Loads\"\u003E\u003C/p\u003E\n","\u003Cp\u003ETo help future maintainers understand what's happening, update its name to \u003Ccode\u003EWait for loads\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003ENow open the Pipelines panel, and drag \u003Ccode\u003ECalculate Profit and Revenue\u003C/code\u003E into place on the canvas after the \u003Ccode\u003EAnd\u003C/code\u003E. Link it using the unconditional connector.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/Add-Transformation-Pipeline.gif\" alt=\"Add Transformation Pipeline\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThis means the transformation pipeline will always run at the right time: after all the data loading has finished, and only if it was all successful.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EMerge the Development branch\u003C/h2\u003E\n","\u003Cp\u003EAll this pipeline work has been happening in the \u003Ccode\u003Edevelopment\u003C/code\u003E git branch. Now it's completed, it's time to merge this feature branch back into main.\u003C/p\u003E\n","\u003Cp\u003EUsing the git menu, first \u003Cstrong\u003Epull remote changes\u003C/strong\u003E into this feature branch. If there are any conflicts from development work that's been going on in parallel, now is the time to know about them in case any adjustments are needed. No conflicts are expected in this case.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/Pull-Remote-Changes.png\" alt=\"Pull Remote Changes\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENext, using the same menu:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ECommit\u003C/strong\u003E the feature branch, with a helpful commit message\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPush local changes\u003C/strong\u003E to the remote.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EUse the "back" icon top left of screen to quit the pipeline designer canvas, returning to your project menu. This time, follow the \u003Cstrong\u003Emain\u003C/strong\u003E branch name to enter the \u003Ca href=\"https://docs.matillion.com/data-productivity-cloud/designer/docs/designer-ui-basics/\"\u003EPipeline Designer\u003C/a\u003E interface in the main branch.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/Back-To-Main.png\" alt=\"Back To Main\"\u003E\u003C/p\u003E\n","\u003Cp\u003EYou should see no pipelines listed at the moment, because all the work has been done in the feature branch. Use the git menu again:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EPull remote changes\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EMerge\u003C/strong\u003E from the development branch into main. There should be no conflicts.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ENow all the pipelines are in the main branch. You can delete the feature branch, and return right back to the start of the development cycle, ready to work on something new.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion And Resources\u003C/h2\u003E\n","\u003Cp\u003ECongratulations on completing this lab!\u003C/p\u003E\n","\u003Cp\u003EYou've successfully used the Matillion Data Productivity Cloud to extract data, load it into Snowflake, and then transform and integrate it using SQL generated by Matillion's code-optional pipeline designer.\u003C/p\u003E\n","\u003Ch3\u003EConclusion\u003C/h3\u003E\n","\u003Cp\u003EYou have experienced how Matillion's close integration with Snowflake benefits teams with diverse skill sets. The Matillion/Snowflake combination enables everyone to quickly and efficiently gain insights from their data, even when it originates from different silos.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to use the Matillion Data Productivity Cloud Pipeline Designer\u003C/li\u003E\u003Cli\u003EBuilding Orchestration pipelines for data extraction, and for command-and-control operations\u003C/li\u003E\u003Cli\u003ECreating Transformation pipelines for data transformation and integration, including flattening semi-structured data\u003C/li\u003E\u003Cli\u003EMaking use of the Matillion Copilot to assist in building ETL pipelines\u003C/li\u003E\u003Cli\u003EGit feature branching with Matillion\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ENext Steps and Related Resources\u003C/h3\u003E\n","\u003Cp\u003EContinue to use your free trial of Matillion and Snowflake: load and process your own sample or real data, investigating some of the more advanced platform features that are not covered in this lab.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ERead the definitive Snowflake guide to \u003Ca href=\"/test-driving-snowflake-the-definitive-guide-to-maximizing-your-free-trial/\"\u003Emaximizing your free trial\u003C/a\u003E\u003C/li\u003E\u003Cli\u003EVisit the \u003Ca href=\"https://www.matillion.com/\"\u003EMatillion\u003C/a\u003E \u003Ca href=\"https://www.matillion.com/tour\"\u003Eproduct tour\u003C/a\u003E and interactive demo experience\u003C/li\u003E\u003Cli\u003ELearn how to \u003Ca href=\"https://www.matillion.com/ai\"\u003Ebring the power of AI to data engineering\u003C/a\u003E\u003C/li\u003E\u003Cli\u003ETake a training course from the \u003Ca href=\"https://academy.matillion.com/trainings\"\u003EMatillion Academy\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EA full \u003Ca href=\"https://www.youtube.com/watch?v=FGaf-6TcGYU\"\u003Evideo demonstration\u003C/a\u003E of this lab is also available.\u003C/p\u003E"],"title":"Getting Started with Matillion Data Productivity Cloud",":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Overview \n\nThis Quickstart will show you how to load and integrate enterprise data into Snowflake quickly and efficiently using the Matillion Data Productivity Cloud.\n\nIt's a typical data integration scenario: there are multiple, related data sources, and you need to generate some joined-up insights. You will build ETL pipelines that extract, load, transform and integrate the data, and you'll get a feeling for the typical development lifecycle with Matillion.\n\nMatillion is the data pipeline platform that brings push-down ELT and AI to Snowflake, helping you to accelerate data pipelines for analytics and AI projects at scale.\n\n![Matillion Data Productivity Cloud](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/Matillion-Data-Productivity-Cloud.png)\n\n### What You Will Build\n- Matillion Data Productivity Cloud pipelines that extract, load, transform and integrate data\n\n### What You Will Learn\n- How to use the Matillion Data Productivity Cloud Pipeline Designer\n- How to build and use Orchestration pipelines for data extraction, and for command-and-control operations such as looping and synchronization\n- How to build and use Transformation pipelines for data transformation and integration, including handling semi-structured data\n- How to use the Matillion Copilot to help build ETL pipelines\n- How to use git with Matillion in feature branching mode\n\n### Prerequisites\n- A web browser\n- An email address\n- Access to a [Matillion account](https://hub.matillion.com/) (or free trial, set up in the next step of this lab)\n- Access to a [Snowflake account](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides) (or free trial, automatically created when you sign up for a free Matillion trial)\n\n\u003C!-- ------------------------ --\u003E\n## Create your Matillion and Snowflake accounts\n\nIf you already have a Matillion and Snowflake account, just [log in to the Matillion Data Productivity Cloud](https://hub.matillion.com/) and go straight to the next step in this Quickstart.\n\nOtherwise ...\n\n### Use Snowflake Partner Connect\n\nIf you already have a Snowflake account, look for Matillion Data Productivity Cloud in [Snowflake Partner Connect](https://docs.snowflake.com/en/user-guide/ecosystem-partner-connect#connecting-with-a-snowflake-partner) and follow the instructions on screen to create a Matillion free trial. Once you are logged into your Matillion Data Productivity Cloud account, skip to the next step in this Quickstart.\n\nIf you don't already have a Snowflake account, or you prefer to start your own Matillion free trial, read on ...\n\n### Start a Matillion free trial\n\nGo to the Matillion start page at [hub.matillion.com](https://hub.matillion.com/)\n- Switch to the **Sign Up** tab\n- Authenticate with Google or Microsoft, or enter your business email address and pick a complicated password\n\nIf you used an email address, you'll receive an email with a link to verify your address. Follow the link to continue.\n\nNext you will be transferred to the Matillion setup screens.\n\nIn the setup screens:\n- Provide some basic information about your goals, and let Matillion know how to address you\n- Select a cloud region\n\n### Set up your Snowflake account\n\nThe last setup screen invites you to choose a target platform.\n\n- If you **already** have a Snowflake account:\n    - Pick \"Connect to Snowflake\"\n    - Log in to your Snowflake account in the dialog that follows\n    - Go to the next step in this Quickstart\n- If you **don't** already have a Snowflake account, select \"Use a Matillion trial warehouse\" and Matillion will set up a free Snowflake trial on your behalf.\n\n![Use a Trial Warehouse](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/Use-Trial-Warehouse.png)\n\nIt will take a couple of minutes for Matillion to create a free, empty, trial Snowflake account and connect everything together.\nOnce that's done you'll end up in the Matillion Data Productivity Cloud's main ETL [pipeline designer interface](https://docs.matillion.com/data-productivity-cloud/designer/docs/designer-overview/).\n\nFor reference [here's a video](https://www.youtube.com/watch?v=G2Rc7K_8pZw) that takes you through all the steps above.\n\n\n\n\u003C!-- ------------------------ --\u003E\n## Create a Development branch\n\nMatillion development is tightly integrated with git, so when you are [logged in to the Matillion Data Productivity Cloud](https://hub.matillion.com/) and developing ETL pipelines, you're always working in the context of a git branch. This Quickstart will demonstrate the **feature branching** strategy.\n\nStart at the management page of your Data Productivity Cloud [Project](https://docs.matillion.com/data-productivity-cloud/designer/docs/projects/), and press \"Add New Branch\".\n\n![Add New Branch](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/Add-New-Branch.png)\n\nName the new branch `development` and have it branch from `main`.\n\n![Create a Development Branch](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/Create-a-Branch.png)\n\nTowards the end of this Quickstart you'll learn how to merge this feature branch back into main.\n\nMeanwhile, click on the new branch name to enter the [Pipeline Designer](https://docs.matillion.com/data-productivity-cloud/designer/docs/designer-ui-basics/) interface in the development branch.\n\n\u003C!-- ------------------------ --\u003E\n## Create Static Metadata\n\nThe first data to be loaded into Snowflake is a few fixed records of reference information, which will be used later. This kind of setup is sometimes known as \"seed\" data.\n\nMatillion uses **Transformation** pipelines for everything that involves running SQL on Snowflake. In the pipeline designer interface, create a new Transformation pipeline.\n\n![Create a New Transformation Pipeline](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/New-Transformation-Pipeline.png)\n\nIn the dialog that follows, name the new pipeline `Create SHEET_NAMES` and press the Add button.\n\nNow you can start to add components to the blank canvas. For seed data you need a [Fixed Flow](https://docs.matillion.com/data-productivity-cloud/designer/docs/fixed-flow/) component. There are two ways to do this, both highlighted in the screenshot below:\n- Open the `Components` panel on the left, find the **Fixed Flow**, and drag it onto the canvas\n- Press the `+` sign in the middle of the canvas, which opens a similar Components dialog. This method has the advantage of automatically connecting the components together when you have more than one\n\n![Add a Fixed Flow Component](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/Add-Fixed-Flow-Component.png)\n\n### Add a Fixed Flow component\n\nWhenever a component on the canvas is selected, a panel opens showing its properties.\n\nSet the **name** of the Fixed Flow component to `Generate worksheet names` using the pencil icon at the top of the properties panel. You can do this with any component in a pipeline, and it helps maintainers understand what's going on.\n\n![Renaming a Component](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/Renaming-a-Component.gif)\n\nComplete the properties of the new Fixed Flow by setting:\n\n**Columns**\n\n| Name | Datatype |\n| --- | --- |\n| WORKSHEET_NAME | VARCHAR |\n\n**Values**\n\n| WORKSHEET_NAME |\n| --- |\n| ITEMS |\n| ORDERS |\n| ORDER_ITEMS |\n\n\u003E \n\u003E\n\u003E  Be careful with the exact names here, making sure they are in uppercase exactly as shown. Copy-and-paste is best.\n\nAfter you have completed the properties, the border of the component should change to green as Matillion validates it successfully.\n\nOpen the **Sample data** panel at the bottom of the screen, and press the `Sample data` button to view the data. Check that three rows appear.\n\n\u003E \n\u003E\n\u003E  You can use the Sample data panel with almost all transformation components. It's a great way to keep in touch with the data while you are developing a transformation pipeline\n\n![Data Sample Fixed Flow](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/Data-Sample-Fixed-Flow.png)\n\n### Add a Rewrite Table component\n\nNow it's time to save those fixed three rows of data to a real Snowflake table. Follow the same steps to add another component: a [Rewrite Table](https://docs.matillion.com/data-productivity-cloud/designer/docs/rewrite-table/) this time.\n\nIf you used the `+` icon to add the new component, the two will be connected together already. If you dragged it in from the panel on the left, you'll have to manually link them together by dragging an arrow from the little circle nub on the Fixed Flow to the Rewrite Table. You can reposition components on the canvas at any time by dragging them with your mouse.\n\nComplete the properties of the new Rewrite Table with these values:\n\n**Name** - `Create SHEET_NAMES`\n\n**Target Table** - `GW_SHEET_NAMES`\n\nYour transformation pipeline should look something like this:\n\n![Run Create Seed Data](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/Run-Create-Seed-Data.png)\n\n### Run the pipeline\n\nPress the `Run` button at the top of the canvas, to manually run the pipeline. It will create a new Snowflake table named `GW_SHEET_NAMES` containing those three rows. The Task History panel will open, and you should see the table name and the rowcount among the details. You can close the Task History panel afterwards.\n\nNow you have some reference data in Snowflake, it's time for the next step.\n\n\u003C!-- ------------------------ --\u003E\n## Load Sales data from Excel\n\nIn this step you will start to work with some real data.\n\nIn the Matillion pipeline designer interface, create a new pipeline - the same as you did in the previous step but an **Orchestration** pipeline this time. In addition to extracting and loading data, this pipeline will also control the overall flow of data for this Quickstart, so give it a generic name: `GreenWave Technologies Demo`.\n\nNow you can start to add components to the canvas. \n\n\u003E \n\u003E \n\u003E  Every Orchestration pipeline must have a [Start](https://docs.matillion.com/data-productivity-cloud/designer/docs/start/) component. One is added automatically whenever you create a new Orchestration pipeline.\n\n### Add the Create SHEET_NAMES pipeline\n\nMatillion Orchestration pipelines fulfill a command-and-control role. In this Quickstart the first thing is to make sure the static metadata is always available.\n\n- Expand the Pipelines panel on the left of the screen\n- Drag the Transformation pipeline `Create SHEET_NAMES` you created in the previous step onto the canvas\n- Link it after the Start component\n\nThe three rows of static metadata are actually the names of worksheets in an Excel spreadsheet. This Quickstart will use data from all of them, so this pipeline needs a way to iterate through them.\n\n### Add a Variable\n\nIteration loops require a variable to loop over. Open the variables panel on the left, and add a new Pipeline variable of type **Text**.\n\n- Name it `worksheet_name`\n- Make it `Private` to this pipeline\n- Set the Behavior to `Copied`, which means that any loops using it will be able to run in parallel. This is known as concurrent iteration.\n- Add a default value of `ITEMS`\n\n\u003E \n\u003E \n\u003E  It's good practice to give every variable a default value. Choose a default that helps components validate cleanly.\n\nYour variable should look like this:\n\n![Pipeline Variable Setup](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/Pipeline-Variable-Setup.png)\n\n### Add a Table Iterator\n\nThere are many different ways to loop in Matillion. The component which loops through the rows in a table is the [Table Iterator](https://docs.matillion.com/data-productivity-cloud/designer/docs/table-iterator/).\n\nUse the components panel on the left (or the `+` after the transformation pipeline component) to find and add a Table Iterator.\n\n- Set the Target Table to `GW_SHEET_NAMES`. It should be present in the dropdown provided you ran the transformation pipeline earlier to create the table\n- Set the Concurrency mode to `Concurrent`\n- In the Column Mapping, add a row that maps the `WORKSHEET_NAME` column to the `worksheet_name` variable\n\nNow the iterator will loop through the (three) rows in the reference table in parallel, setting the variable from the chosen column in each case.\n\nLink the Table Iterator so it rus after the transformation pipeline component. You'll notice that **three** output connector nubs are available: green, grey and red, and you can link using any of them. At runtime the **green** path at the top is only followed if the component finished successfully, and that's the one you'll use in most cases. **Grey** is for unconditional, where you don't care if it was successful or not. **Red** is the failure branch, which is how to implement error handling.\n\nYour pipeline should look like this:\n\n![Iterator Setup](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/Iterator-Setup.png)\n\n### Add an Excel Query\n\nNow add a new [Excel Query](https://docs.matillion.com/data-productivity-cloud/designer/docs/excel-query/) component, without linking it to anything yet. Place it slightly below the iterator on the canvas.\n\nWith the iterator selected, drag its **blue** connector nub onto the Excel Query component. The two components should nest together, as shown in the \"before\" and \"after\" screenshot below.\n\n![Connected Iterator](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/Connected-Iterator.png)\n\nSelect the Excel Query and set its properties:\n\n- Storage Type: Amazon S3 Storage\n- Storage URL: `devrel.matillion.com/solutions/GreenWave/store_lite.xlsx` (don't worry if you see an error message about listing buckets)\n- Basic Advanced Mode: Advanced\n- Contains Header Row: Yes\n- SQL Query: `SELECT * FROM ${worksheet_name}`\n- Target Table: `GW_${worksheet_name}`\n\n\u003E \n\u003E \n\u003E  Notice how the variable is used in the settings. During the extract phase, it defines the source worksheet name. During the load phase it defines part of the Snowflake table name.\n\nThe pipeline should validate cleanly now.\n\n**Run** the pipeline now, and it will create three new Snowflake tables: `GW_ITEMS`, `GW_ORDERS` and `GW_ORDER_ITEMS`, with structure and data taken from the spreadsheet.\n\n\u003C!-- ------------------------ --\u003E\n## Load Customer data from JSON\n\nIn this step, you will add a new, semi-structured data source to your `GreenWave Technologies Demo` Orchestration pipeline.\n\nFind and drag two new components onto the canvas:\n- A [Create Table](https://docs.matillion.com/data-productivity-cloud/designer/docs/create-table/), linked to the `Start`, and followed by:\n- An [S3 Load](https://docs.matillion.com/data-productivity-cloud/designer/docs/s3-load/) component\n\n\u003E \n\u003E \n\u003E  **Azure** or **GCP** users:\n\u003E  Instead of an S3 Load, use a [Data Transfer](https://docs.matillion.com/data-productivity-cloud/designer/docs/data-transfer/) component first to copy the source file into your own cloud storage.\n\u003E  The source file is publicly available at `https://s3.eu-west-1.amazonaws.com/devrel.matillion.com/solutions/GreenWave/customer_accounts.json` .. you don't need an AWS account to access it.\n\u003E\n\u003E  Afterwards add either an [Azure Blob](https://docs.matillion.com/data-productivity-cloud/designer/docs/azure-blob-storage-load/) or a [Google Cloud](https://docs.matillion.com/data-productivity-cloud/designer/docs/google-cloud-storage-load/) Storage Load component instead of S3.\n\nAdjust the positioning of the components so they end up looking something like this:\n\n![DDL and Load Customer JSON](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/DDL-and-Load-Customer-JSON.png)\n\nNotice how there are **two** components linked from the `Start` component. That means two threads will execute in parallel whenever the pipeline runs. Also remember the Excel queries will themselves run inside a concurrent loop. And of course, all this is running against Snowflake which introduces its *own* parallelism. All this multi threading means the pipeline will finish faster!\n\n### DDL for semi-structured data\n\nAdjust the properties of the Create Table component like this:\n\n- Name: `Replace GW_CUSTOMER_ACCOUNTS`\n- Create/Replace: `Replace`\n- New Table Name: `GW_CUSTOMER_ACCOUNTS`\n- Columns: Add just one, named `DATA_VALUE` and of type `VARIANT`\n\nIt should validate cleanly now, and have a green border. Run just this component, to create the table, by clicking the small \"Run only component\" icon that appears when you select it.\n\n![Run Only Component](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/Run-Only-Component.png)\n\n### Loading semi-structured data\n\nNow the `GW_CUSTOMER_ACCOUNTS` table exists, you can load data into it. Adjust the properties of the S3 Load component like this:\n\n- Name: `Load Customer 360 Data`\n- S3 Object Prefix: `devrel.matillion.com` (don't worry if you see a \"Something went wrong\" message)\n- Pattern: `solutions/GreenWave/customer_accounts.json`\n- Target Table: `GW_CUSTOMER_ACCOUNTS`\n- Load Columns: `DATA_VALUE`\n- File Type: `JSON`\n\n\u003E \n\u003E \n\u003E  **Azure** or **GCP** users: adjust the cloud storage location settings to wherever you made the Data Transfer copy the file\n\nThe component should validate cleanly, so once again run just this component, to load the table.\n\nExpand the Task History to view the Task Details, and check that the row count is 1. All the customer records are in one big semi-structured JSON object. In the next step you'll see how to flatten and relationalize it.\n\n\u003C!-- ------------------------ --\u003E\n## Flatten and relationalize semi-structured data\n\nIn the pipeline designer interface, create another new Transformation pipeline, named `Calculate Profit and Revenue` this time.\n\nFind the [Table Input](https://docs.matillion.com/data-productivity-cloud/designer/docs/table-input/) in the components panel, and drag it onto the canvas. To help finding it, press the **Read** components button to narrow the search.\n\n- Set the Target Table to `GW_CUSTOMER_ACCOUNTS`, from the dropdown list. Update the component name to the same value\n- In the Column Names property, move `DATA_VALUE` across to the **Selected Column Names** list\n\nGive it a moment to validate, and for the border to turn green. Use the **Sample data** panel at the bottom to view the single row of data.\n\n![View Semi-Structured Data](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/View-Semi-Structured-Data.png)\n\nMatillion has several components that work with semi-structured data. The right one for this task is an [Extract Nested Data](https://docs.matillion.com/data-productivity-cloud/designer/docs/extract-nested-data/). Drag it onto the canvas after the Table Input so they link together.\n\nIn the new component:\n\n- Set **Include Input Columns** to `No`\n- In the **Columns** property, press `Autofill`, then expand the structure and select only `customer_rid` and `account_name`\n\n![Extract Nested Structure](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/Extract-Nested-Structure.png)\n\nRun a data sample against this component and check that the data has been flattened and relationalized. You should see 10,000 records, each with a `customer_rid` identifier and an `account_name` string.\n\n\u003E \n\u003E \n\u003E  This transformation work is happening entirely **inside** Snowflake, which means it's very fast, efficient and cost-effective. If you're curious, open the SQL tab next to the component properties to see how it works.\n\nTo prepare for the next step, rename the Extract Nested Data component to `ACC` - short for \"accounts\".\n\n\u003C!-- ------------------------ --\u003E\n## Data Integration\n\nAt this stage four tables have been extracted and loaded: `GW_ORDERS`, `GW_ITEMS` and `GW_ORDER_ITEMS` from Excel, and the newly flattened `GW_CUSTOMER_ACCOUNTS` from the JSON file. They are related like this:\n\n![Greenwave ERD](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/Greenwave-ERD.png)\n\n\u003E \n\u003E \n\u003E  Notice that, in reality, the data was originally siloed and relatively inaccessible. Not only physically separated, but in the case of the customer data, in a difficult-to-read semi-structured format too.\n\nContinuing with the `Calculate Profit and Revenue` transformation pipeline. The flattened customer accounts table is already in place, so:\n\n- Add **three** more Table Input components: one for `GW_ORDERS`, one for `GW_ITEMS` and one for `GW_ORDER_ITEMS`\n- Choose **all** the columns in every case\n- For readability it's best to align them vertically on the canvas\n\nSome minor datatype fixes are needed before integrating, so add a [Convert Type](https://docs.matillion.com/data-productivity-cloud/designer/docs/convert-type/) component linked after `GW_ORDERS`, naming it `ORDER` and with the following conversions:\n\n![Order Datatype Conversions](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/Order-Datatype-Conversions.png)\n\nRepeat for `GW_ORDER_ITEMS`, naming it `ORDER_ITEM` with these conversions:\n\n| Column | Type |\n| --- | --- |\n| ITEM_RID | NUMBER |\n| ORDER_RID | NUMBER |\n| RID | NUMBER |\n| QUANTITY | NUMBER |\n\n... and for `GW_ITEMS`, naming it `ITEM_REF` with these conversions:\n\n| Column | Type |\n| --- | --- |\n| RID | NUMBER |\n| COST | NUMBER |\n| PRICE | NUMBER |\n\nNow for the data integration, find and drag on a [Join](https://docs.matillion.com/data-productivity-cloud/designer/docs/join/) component. Link all **four** inputs to it, and configure it as follows:\n\n\u003E \n\u003E\n\u003E  It's important to complete the properties top downwards, as some of them cascade onto others further down the list\n\n- Name it `Integrate`\n- Main Table: choose `ORDER_ITEM` from the dropdown. This is the table with the lowest granularity\n- Main Table Alias: `OI`\n- Joins: add the other three tables like this:\n\n![Greenwave Joins](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/Greenwave-Joins.png)\n\n- Join Expressions: following the entity relationship diagram above\n  - **OI_Inner_O**: `\"OI\".\"ORDER_RID\" = \"O\".\"RID\"`\n  - **OI_Inner_I**: `\"OI\".\"ITEM_RID\" = \"I\".\"RID\"`\n  - **OI_Inner_A**: `\"O\".\"CUSTOMER_RID\" = \"A\".\"customer_rid\"`\n\n- Column Mappings:\n\n| Source Column | Target Column |\n| --- | --- |\n| O.ORDER_DATE | ORDER_DATE |\n| OI.QUANTITY | QUANTITY |\n| I.NAME | ITEM_NAME |\n| I.PRICE | PRICE |\n| I.COST | COST |\n| A.account_name | ACCOUNT_NAME |\n\nOnce this is configured and validates successfully, open the **Sample data** panel and check the results. You should find the same number of records (999) as there were in `GW_ORDER_ITEMS`, with these columns:\n\n![Integrate Data Sample](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/Integrate-Data-Sample.png)\n\nNow all the data is in place to start adding the derived columns including profit and revenue.\n\n\u003C!-- ------------------------ --\u003E\n## Calculate Profit and Revenue\n\nMatillion has several ways to add derived fields, and the most commonly used is the [Calculator](https://docs.matillion.com/data-productivity-cloud/designer/docs/calculator/) component.\n\nContinuing with the `Calculate Profit and Revenue` transformation pipeline, add a Calculator after the Join integration you just completed.\n\n![Add Calculator Component](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/Add-Calculator-Component.png)\n\nOne Calculator component can add multiple derived fields. Leave **Include Input Columns** set to `Yes` and follow **Calculations** to enter the settings. Add three calculations, giving each one a name and a SQL expression:\n\n- PROFIT - `((\"PRICE\" - \"COST\") * \"QUANTITY\")`\n- REVENUE - `(\"PRICE\" * \"QUANTITY\")`\n- ORDER_MONTH - `TRUNCATE(\"ORDER_DATE\", 'mm')`\n\nIt should end up looking like this:\n\n![Calculator Component](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/Calculator-Component.gif)\n\nUse the **Sample data** panel to check that the three new columns have been added to every row. You might need to use the scrollbar to view the new columns after all the existing ones.\n\n\u003C!-- ------------------------ --\u003E\n## Aggregating with Copilot assistance\n\nThe profit and revenue data is still at order item level, but the reporting need is at a higher granularity. So it must be aggregated as a final step before presentation.\n\nYou can use the Matillion Designer Copilot to help with this, telling it to aggregate to the desired granularity, and which fields need to be summed.\n\n- Highlight the Calculator component\n- Expand the Copilot panel\n- Type the following into the \"Enter a prompt here\" box: `Aggregate the sum of PROFIT and REVENUE by ITEM_NAME, ACCOUNT_NAME and ORDER_MONTH`\n- Press the Send Prompt icon\n\n![Run Copilot](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/Run-Copilot.png)\n\nThe Copilot should add an [Aggregate](https://docs.matillion.com/data-productivity-cloud/designer/docs/aggregate/) component attached to the Calculator, with the specified groupings and aggregations. Sample the data from this Aggregate component to verify that the number of rows is smaller, and the profit and revenue are being summed.\n\n\u003E \n\u003E \n\u003E  The Matillion Designer Copilot uses generative AI, so the results are not always identical and deterministic. If you don't immediately get the configuration you need, just delete the component it created, adjust the prompt, and retry \n\nAll the row sampling you've seen so far has been on the fly, with nothing actually saved to the database. In order to distribute this data for easy visualization, the last step is to save it into a new table. This needs a write component, so in the components panel look for a [Rewrite Table](https://docs.matillion.com/data-productivity-cloud/designer/docs/rewrite-table/) and drop it into place connected after the aggregation. It will do a Create Table As Select at runtime.\n\n- Set the **Target Table** to `GW_PROFIT_BY_ACCOUNT` and rename the component to `Rewrite GW_PROFIT_BY_ACCOUNT`\n\n![Rewrite GW_PROFIT_BY_ACCOUNT](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/Rewrite-GW_PROFIT_BY_ACCOUNT.png)\n\nThe data transformation and integration is complete! The last piece of development work is to make sure it runs at the right time.\n\n\u003C!-- ------------------------ --\u003E\n## Orchestrating the ETL pipelines\n\nTo get the transformation and integration to run at the right time, first go right back to the `GreenWave Technologies Demo` orchestration pipeline.\n\nRemember the data loading was happening in multiple threads? We need a way to synchronize these, and wait for **all** the data loading to finish before moving on to data transformation and integration. This is done with an [And](https://docs.matillion.com/data-productivity-cloud/designer/docs/and/) component. Locate it in the components panel, drag it into place to the right of everything else, and link it to the success branch of the two load components like this:\n\n![And Component Wait for Loads](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/And-Wait-for-Loads.png)\n\nTo help future maintainers understand what's happening, update its name to `Wait for loads`.\n\nNow open the Pipelines panel, and drag `Calculate Profit and Revenue` into place on the canvas after the `And`. Link it using the unconditional connector.\n\n![Add Transformation Pipeline](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/Add-Transformation-Pipeline.gif)\n\nThis means the transformation pipeline will always run at the right time: after all the data loading has finished, and only if it was all successful.\n\n\u003C!-- ------------------------ --\u003E\n## Merge the Development branch\n\nAll this pipeline work has been happening in the `development` git branch. Now it's completed, it's time to merge this feature branch back into main.\n\nUsing the git menu, first **pull remote changes** into this feature branch. If there are any conflicts from development work that's been going on in parallel, now is the time to know about them in case any adjustments are needed. No conflicts are expected in this case.\n\n![Pull Remote Changes](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/Pull-Remote-Changes.png)\n\nNext, using the same menu:\n\n- **Commit** the feature branch, with a helpful commit message\n- **Push local changes** to the remote.\n\nUse the \"back\" icon top left of screen to quit the pipeline designer canvas, returning to your project menu. This time, follow the **main** branch name to enter the [Pipeline Designer](https://docs.matillion.com/data-productivity-cloud/designer/docs/designer-ui-basics/) interface in the main branch.\n\n![Back To Main](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/load-integrate-data-matillion-data-productivity-cloud/Back-To-Main.png)\n\nYou should see no pipelines listed at the moment, because all the work has been done in the feature branch. Use the git menu again:\n\n- **Pull remote changes**\n- **Merge** from the development branch into main. There should be no conflicts.\n\nNow all the pipelines are in the main branch. You can delete the feature branch, and return right back to the start of the development cycle, ready to work on something new.\n\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion And Resources\n\nCongratulations on completing this lab!\n\nYou've successfully used the Matillion Data Productivity Cloud to extract data, load it into Snowflake, and then transform and integrate it using SQL generated by Matillion's code-optional pipeline designer.\n\n### Conclusion\n\nYou have experienced how Matillion's close integration with Snowflake benefits teams with diverse skill sets. The Matillion/Snowflake combination enables everyone to quickly and efficiently gain insights from their data, even when it originates from different silos.\n\n### What You Learned\n- How to use the Matillion Data Productivity Cloud Pipeline Designer\n- Building Orchestration pipelines for data extraction, and for command-and-control operations\n- Creating Transformation pipelines for data transformation and integration, including flattening semi-structured data\n- Making use of the Matillion Copilot to assist in building ETL pipelines\n- Git feature branching with Matillion\n\n### Next Steps and Related Resources\n\nContinue to use your free trial of Matillion and Snowflake: load and process your own sample or real data, investigating some of the more advanced platform features that are not covered in this lab.\n\n- Read the definitive Snowflake guide to [maximizing your free trial](/test-driving-snowflake-the-definitive-guide-to-maximizing-your-free-trial/)\n- Visit the [Matillion](https://www.matillion.com/) [product tour](https://www.matillion.com/tour) and interactive demo experience\n- Learn how to [bring the power of AI to data engineering](https://www.matillion.com/ai)\n- Take a training course from the [Matillion Academy](https://academy.matillion.com/trainings)\n\nA full [video demonstration](https://www.youtube.com/watch?v=FGaf-6TcGYU) of this lab is also available.\n\n",":type":"text/x-markdown","multiValue":false},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image",":type":"text/plain","multiValue":false}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"isDeveloperGuidesPage":false,"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-0311de30fc","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-f69095eed5",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-88b2de3807","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2025-12-20",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-7481e4ffc8","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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your Matillion and Snowflake accounts\u003C/h2\u003E","\u003Ch2\u003ECreate a Development branch\u003C/h2\u003E","\u003Ch2\u003ECreate Static Metadata\u003C/h2\u003E","\u003Ch2\u003ELoad Sales data from Excel\u003C/h2\u003E","\u003Ch2\u003ELoad Customer data from JSON\u003C/h2\u003E","\u003Ch2\u003EFlatten and relationalize semi-structured data\u003C/h2\u003E","\u003Ch2\u003EData Integration\u003C/h2\u003E","\u003Ch2\u003ECalculate Profit and Revenue\u003C/h2\u003E","\u003Ch2\u003EAggregating with Copilot assistance\u003C/h2\u003E","\u003Ch2\u003EOrchestrating the ETL pipelines\u003C/h2\u003E","\u003Ch2\u003EMerge the Development branch\u003C/h2\u003E","\u003Ch2\u003EConclusion And 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