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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}",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false},"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 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Kranzler","quickstartHeroForkRepoLink":{"id":"button-6fa7920861","showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/tasty-bytes-zero-to-snowflake-collaboration"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Fork Repo"},"quickstartHeroBreadcrumbs":[{"title":"Tasty Bytes - Zero to Snowflake - Collaboration","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers/guides/tasty-bytes-zero-to-snowflake-collaboration","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}]},"flexible_column_cont":{"id":"flexible-column-container-9167a383f5","propertiesId":"quickstart-template-main-flexible-container","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-1e052d65dc",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"contentfragment":{"id":"contentfragment-d884c9e32e","paragraphs":["<!-- ------------------------ -->\n","\u003Ch2\u003ECollaboration in Snowflake\u003C/h2\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration/collaboration_header.png\" alt=\"assets/collaboration_header.png\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003EWelcome to the Tasty Bytes - Zero to Snowflake Quickstart focused on Collaboration! Within this Quickstart we will highlight how you can enrich your first-party data with weather data via Snowflake Marketplace. Snowflake Marketplace is where you can find, try, and buy third party data and apps, all without having to set up any ETL, data pipelines, or integrations.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EBefore beginning, please make sure you have completed the \u003Ca href=\"/en/developers/guides/tasty-bytes-introduction/\"\u003E\u003Cstrong\u003EIntroduction to Tasty Bytes Quickstart\u003C/strong\u003E\u003C/a\u003E which provides a walkthrough on setting up a trial account and deploying the Tasty Bytes Foundation required to complete this Quickstart.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You Will Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to Access the Snowflake Marketplace\u003C/li\u003E\u003Cli\u003EHow to Acquire Live Weather Source Data in your Account\u003C/li\u003E\u003Cli\u003EHow to Create a View\u003C/li\u003E\u003Cli\u003EHow to Create a SQL Function\u003C/li\u003E\u003Cli\u003EHow to Leverage Snowsight Charts to Explore Visual Insights\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You Will Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EThe Harmonization of First Party Sales and Third Party Weather Data\u003C/li\u003E\u003Cli\u003ESeamless convertibility from Fahrenheit to Celsius\u003C/li\u003E\u003Cli\u003ESeamless convertibility from Inch to Millimeter\u003C/li\u003E\u003Cli\u003EAn Understanding of How to Unlock Additional Insights via the Snowflake Marketplace\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003ECreating a Worksheet and Copying in our SQL\u003C/h2\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003EWithin this Quickstart we will follow a Tasty Bytes themed story via a Snowsight SQL Worksheet with this page serving as a side by side guide complete with additional commentary, images and documentation links.\u003C/p\u003E\n","\u003Cp\u003EThis section will walk you through logging into Snowflake, Creating a New Worksheet, Renaming the Worksheet, Copying SQL from GitHub, and Pasting the SQL we will be leveraging within this Quickstart.\u003C/p\u003E\n","\u003Ch3\u003EStep 1 - Accessing Snowflake via URL\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EOpen a browser window and enter the URL of your Snowflake Account\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EStep 2 - Logging into Snowflake\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ELog into your Snowflake account.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EStep 3 - Navigating to Worksheets\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EClick on the Projects Tab in the left-hand navigation bar and click Worksheets.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EStep 4 - Creating a Worksheet\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EWithin Worksheets, click the "+" button in the top-right corner of Snowsight.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EStep 5 - Renaming a Worksheet\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ERename the Worksheet by clicking on the auto-generated Timestamp name and inputting "Tasty Bytes - Collaboration"\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EStep 6 - Accessing Quickstart SQL in GitHub\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EClick the button below which will direct you to our Tasty Bytes SQL file that is hosted on GitHub.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E<button>\u003Ca href=\"https://github.com/Snowflake-Labs/sf-samples/blob/main/samples/tasty_bytes/FY25_Zero_To_Snowflake/tb_collaboration.sql\"\u003Etb_zts_collaboration.sql\u003C/a\u003E</button>\u003C/p\u003E\n","\u003Ch3\u003EStep 7 - Copying Setup SQL from GitHub\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EWithin GitHub navigate to the right side and click "Copy raw contents". This will copy all of the required SQL into your clipboard.\n\u003Cul\u003E\u003Cli\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration/github_copy_raw_contents.png\" alt=\"assets/github_copy_raw_contents.png\"\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EStep 8 - Pasting Setup SQL from GitHub into your Snowflake Worksheet\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EPath back to Snowsight and your newly created Worksheet and Paste (\u003Cem\u003ECMD + V for Mac or CTRL + V for Windows\u003C/em\u003E) what we just copied from GitHub.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EStep 9 - Click Next -->\u003C/h3\u003E\n","\u003Ch2\u003EInvestigating Days with Zero Sales\u003C/h2\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003ETasty Bytes Financial Analysts have let the business know that there are Trucks in the Raw Point of Sales Sytem Data that are missing Sales for various days.\u003C/p\u003E\n","\u003Cp\u003EWe will investigate these missing days and leverage the Snowflake Marketplace to enhance our analysis with Weather and Location data.\u003C/p\u003E\n","\u003Cp\u003EWe will then dive into one example where Hamburg, Germany Trucks were flagged as having zero sales for a few days in February 2022.\u003C/p\u003E\n","\u003Ch3\u003EStep 1 - Querying Point of Sales Data for Trends\u003C/h3\u003E\n","\u003Cp\u003ELet's start by kicking off this steps three queries to initially set our Role, Warehouse and Database context to \u003Ccode\u003Etb_data_engineer\u003C/code\u003E, \u003Ccode\u003Etb_de_wh\u003C/code\u003E, \u003Ccode\u003Etb_101\u003C/code\u003E. With the context set, we will then query our Analytics \u003Ccode\u003Eorders_v\u003C/code\u003E View to provide a result set of sales for Hamburg, Germany in 2022.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE ROLE tb_data_engineer;\nUSE WAREHOUSE tb_de_wh;\nUSE DATABASE tb_101;\n\nWITH _feb_date_dim AS \n    (\n    SELECT DATEADD(DAY, SEQ4(), '2022-02-01') AS date FROM TABLE(GENERATOR(ROWCOUNT => 28))\n    )\nSELECT\n    fdd.date,\n    ZEROIFNULL(SUM(o.price)) AS daily_sales\nFROM _feb_date_dim fdd\nLEFT JOIN analytics.orders_v o\n    ON fdd.date = DATE(o.order_ts)\n    AND o.country = 'Germany'\n    AND o.primary_city = 'Hamburg'\nWHERE fdd.date BETWEEN '2022-02-01' AND '2022-02-28'\nGROUP BY fdd.date\nORDER BY fdd.date ASC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration/orders_v.png\" alt=\"assets/orders_v.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EBased on what we are seeing above, we can agree with our analysts that we do not have daily sales records for a few days in February so our analysts are definitely on to something. Let's see if we can dig further into why this may have happened in our next section.\u003C/p\u003E\n","\u003Ch3\u003EStep 2 - Click Next -->\u003C/h3\u003E\n","\u003Ch2\u003EAcquiring Weather Source Data from the Snowflake Marketplace\u003C/h2\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003EFrom what we saw above, it looks like we are missing sales for February 16th through February 21st for Hamburg. Within our first party data there is not much else we can use to investigate what might have caused this.\u003C/p\u003E\n","\u003Cp\u003EHowever, with live data sets and native applications available in the Snowflake Marketplace we can immediately add Weather Metrics to our analysis and determine if severe weather may have been the root cause.\u003C/p\u003E\n","\u003Cp\u003EWithin this step, we will retrieve a free, public listing provided by Weather Source, who is a leading provider of global weather and climate data.\u003C/p\u003E\n","\u003Ch3\u003EStep 1 - Acquiring the Weather Source LLC: frostbyte Snowflake Marketplace Listing\u003C/h3\u003E\n","\u003Cp\u003EThe Snowflake Marketplace is the premier location to find, try, and buy the data and applications you need to power innovative business solutions. In this step, we will be access the \u003Ca href=\"https://app.snowflake.com/marketplace/listing/GZSOZ1LLEL/weather-source-llc-weather-source-llc-frostbyte\"\u003EWeather Source LLC: frostbyte\u003C/a\u003E listing to help drive additional analysis on our Hamburg sales slump.\u003C/p\u003E\n","\u003Cp\u003EPlease follow the steps below to acquire this listing in your Snowflake Account.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EIn the bottom left corner, ensure you are operating as ACCOUNTADMIN\u003C/li\u003E\u003Cli\u003EIn the left pane, navigate to 'Data Products' (Cloud Icon) and select 'Marketplace'\u003C/li\u003E\u003Cli\u003EIn the search bar, enter: 'frostbyte'\u003C/li\u003E\u003Cli\u003ESelect the 'Weather Source LLC: frostbyte' listing and click 'Get'\u003C/li\u003E\u003Cli\u003EAdjust Database name to:'TB_WEATHERSOURCE'\u003C/li\u003E\u003Cli\u003EGrant access to: 'PUBLIC'\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\nWeather Source is a leading provider of global weather and climate data and its OnPoint Product Suite provides businesses with the necessary weather and climate data to quickly generate meaningful and actionable insights for a wide range of use cases across industries.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 2 - Harmonizing First and Third Party Data\u003C/h3\u003E\n","\u003Cp\u003EWith the shared \u003Ccode\u003Etb_weathersource\u003C/code\u003E database in place, please execute this steps query to create a \u003Ccode\u003Eharmonized.daily_weather_v\u003C/code\u003E View joining two Weather Source tables to our country table on the Countries and Cities that Tasty Bytes Food Trucks operate within. This query will provide a \u003Ccode\u003EView DAILY_WEATHER_V successfully created.\u003C/code\u003E result.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECREATE OR REPLACE VIEW harmonized.daily_weather_v\nCOMMENT = 'Weather Source Daily History filtered to Tasty Bytes supported Cities'\n    AS\nSELECT\n    hd.*,\n    TO_VARCHAR(hd.date_valid_std, 'YYYY-MM') AS yyyy_mm,\n    pc.city_name AS city,\n    c.country AS country_desc\nFROM tb_weathersource.onpoint_id.history_day hd\nJOIN tb_weathersource.onpoint_id.postal_codes pc\n    ON pc.postal_code = hd.postal_code\n    AND pc.country = hd.country\nJOIN tb_101.raw_pos.country c\n    ON c.iso_country = hd.country\n    AND c.city = hd.city_name;\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAs we see in the View definition above we are joining two of the \u003Ccode\u003Etb_weathersource\u003C/code\u003E Tables within the \u003Ccode\u003Eonpoint_id\u003C/code\u003E Schema and then Harmonizing it with our \u003Ccode\u003Ecountry\u003C/code\u003E Table from our \u003Ccode\u003Etb_101\u003C/code\u003E Database and \u003Ccode\u003Eraw_pos\u003C/code\u003E Schema.\u003C/p\u003E\n","\u003Cp\u003EThis is the sort of operation we typically find in the Harmonized layer or what others may describe as the Silver zone.\u003C/p\u003E\n","\u003Ch3\u003EStep 3 - Visualizing Daily Temperatures\u003C/h3\u003E\n","\u003Cp\u003EWith the \u003Ccode\u003Edaily_weather_v\u003C/code\u003E View in our Harmonized Schema in place let's take a look at the Average Daily Weather Temperature for Hamburg in February 2022 by executing our next query.\u003C/p\u003E\n","\u003Cp\u003EAlong the way we will leverage \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/avg\"\u003EAVG\u003C/a\u003E, \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/year\"\u003EYEAR\u003C/a\u003E and \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/year\"\u003EMONTH\u003C/a\u003E functions.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT\n    dw.country_desc,\n    dw.city_name,\n    dw.date_valid_std,\n    AVG(dw.avg_temperature_air_2m_f) AS avg_temperature_air_2m_f\nFROM harmonized.daily_weather_v dw\nWHERE 1=1\n    AND dw.country_desc = 'Germany'\n    AND dw.city_name = 'Hamburg'\n    AND YEAR(date_valid_std) = '2022'\n    AND MONTH(date_valid_std) = '2' -- February\nGROUP BY dw.country_desc, dw.city_name, dw.date_valid_std\nORDER BY dw.date_valid_std DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration/germany_temp.png\" alt=\"assets/germany_temp.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003ETo further investigate trends, let's utilize Snowsight Charting to create a Line Graph of the Average Temperature over time.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EChart Type: Line\u003C/li\u003E\u003Cli\u003EX-Axis: DATE_VALID_STD(none)\u003C/li\u003E\u003Cli\u003ELine: AVG_TEMPERATURE_AIR_2M_F(none)\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration/temp_chart.png\" alt=\"assets/temp_chart.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EBased on what we saw above, there is nothing really standing out yet as the obvious reason for zero sales days at our trucks. Let's see what else we can find that might explain things in the next step.\u003C/p\u003E\n","\u003Ch3\u003EStep 4 - Bringing in Wind Data\u003C/h3\u003E\n","\u003Cp\u003EAs we saw in our previous step, it does not look like Average Daily Temperature is the reason for our zero sales days in Hamburg. Thankfully, Weather Source provides other weather metrics we can dive into as well.\u003C/p\u003E\n","\u003Cp\u003EPlease now execute the next query where we will leverage our Harmonized View to bring in Wind metrics. In this query we will see the usage of our \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/min\"\u003EMAX\u003C/a\u003E function.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT\n    dw.country_desc,\n    dw.city_name,\n    dw.date_valid_std,\n    MAX(dw.max_wind_speed_100m_mph) AS max_wind_speed_100m_mph\nFROM harmonized.daily_weather_v dw\nWHERE 1=1\n    AND dw.country_desc IN ('Germany')\n    AND dw.city_name = 'Hamburg'\n    AND YEAR(date_valid_std) = '2022'\n    AND MONTH(date_valid_std) = '2' -- February\nGROUP BY dw.country_desc, dw.city_name, dw.date_valid_std\nORDER BY dw.date_valid_std DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration/germany_wind.png\" alt=\"assets/germany_wind.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EOnce again this sort of data might better present trends via a quick Snowsight Chart.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EChart Type: Line\u003C/li\u003E\u003Cli\u003EX-Axis: DATE\u003C/li\u003E\u003Cli\u003ELine: MAX_WIND_SPEED_100M_MPH(none)\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration/wind_chart.png\" alt=\"assets/wind_chart.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAh ha!\u003C/strong\u003E The wind for those zero sales days was at hurricane levels. This seems to be a better reason for why our trucks were not able to sell anything on those days. However since we ran this analysis in Harmonized let's now begin on our path to make this accessible in Analytics where our analysts can access these insights on their own.\u003C/p\u003E\n","\u003Ch3\u003EStep 5 - Click Next -->\u003C/h3\u003E\n","\u003Ch2\u003EDemocratizing First and Third Party Data\u003C/h2\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003EWe have determined that hurricane-level winds were likely the reason for the days with zero sales that our financial analysts brought to our attention.\u003C/p\u003E\n","\u003Cp\u003ELet's now make these sort of research available to anyone in our organization by deploying an Analytics view that all Tasty Bytes employees can access.\u003C/p\u003E\n","\u003Ch3\u003EStep 1 - Creating SQL Functions\u003C/h3\u003E\n","\u003Cp\u003EAs we are a global company, let's start our process by first creating two SQL functions to convert Fahrenheit to Celsius and Inches to Millimeters.\u003C/p\u003E\n","\u003Cp\u003EPlease execute the two queries within this step one by one to create our \u003Ccode\u003Efahrenheit_to_celsius\u003C/code\u003E and \u003Ccode\u003Einch_to_millimeter\u003C/code\u003E functions which leverage the \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/create-function\"\u003ECREATE FUNCTION\u003C/a\u003E command and result in \u003Ccode\u003EFunction _ successfully created.\u003C/code\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECREATE OR REPLACE FUNCTION analytics.fahrenheit_to_celsius(temp_f NUMBER(35,4))\nRETURNS NUMBER(35,4)\nAS\n$$\n    (temp_f - 32) * (5/9)\n$$;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode\u003ECREATE OR REPLACE FUNCTION analytics.inch_to_millimeter(inch NUMBER(35,4))\nRETURNS NUMBER(35,4)\n    AS\n$$\n    inch * 25.4\n$$;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003Easide positive\nWhen you create a UDF, you specify a handler whose code is written in one of the supported languages. Depending on the handler’s language, you can either include the handler source code in-line with the CREATE FUNCTION statement or reference the handler’s location from CREATE FUNCTION, where the handler is precompiled or source code on a stage.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 2 - Creating the SQL for our View\u003C/h3\u003E\n","\u003Cp\u003EBefore deploying our Analytics view, let's create our SQL we will use in the View to combine Daily Sales and Weather together and also leverage our SQL conversion functions.\u003C/p\u003E\n","\u003Cp\u003EPlease execute the next query where we filter for Hamburg, Germany and leverage a few functions we have not seen yet being \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/zeroifnull\"\u003EZEROIFNULL\u003C/a\u003E, \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/round\"\u003EROUND\u003C/a\u003E and \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions/to_date\"\u003EDATE\u003C/a\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT\n    fd.date_valid_std AS date,\n    fd.city_name,\n    fd.country_desc,\n    ZEROIFNULL(SUM(odv.price)) AS daily_sales,\n    ROUND(AVG(fd.avg_temperature_air_2m_f),2) AS avg_temperature_fahrenheit,\n    ROUND(AVG(analytics.fahrenheit_to_celsius(fd.avg_temperature_air_2m_f)),2) AS avg_temperature_celsius,\n    ROUND(AVG(fd.tot_precipitation_in),2) AS avg_precipitation_inches,\n    ROUND(AVG(analytics.inch_to_millimeter(fd.tot_precipitation_in)),2) AS avg_precipitation_millimeters,\n    MAX(fd.max_wind_speed_100m_mph) AS max_wind_speed_100m_mph\nFROM harmonized.daily_weather_v fd\nLEFT JOIN harmonized.orders_v odv\n    ON fd.date_valid_std = DATE(odv.order_ts)\n    AND fd.city_name = odv.primary_city\n    AND fd.country_desc = odv.country\nWHERE 1=1\n    AND fd.country_desc = 'Germany'\n    AND fd.city = 'Hamburg'\n    AND fd.yyyy_mm = '2022-02'\nGROUP BY fd.date_valid_std, fd.city_name, fd.country_desc\nORDER BY fd.date_valid_std ASC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration/sql_for_view.png\" alt=\"assets/sql_for_view.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe results we have just recieved look great. We can now wrap this SQL within a View in our next step.\u003C/p\u003E\n","\u003Ch3\u003EStep 3 - Deploying our Analytics View\u003C/h3\u003E\n","\u003Cp\u003EUsing the same query we just explored, we will need to remove the filters in the WHERE clause, add a \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/comment\"\u003ECOMMENT\u003C/a\u003E and promote this to our \u003Ccode\u003Eanalytics\u003C/code\u003E Schema as the \u003Ccode\u003Edaily_city_metrics_v\u003C/code\u003E View.\u003C/p\u003E\n","\u003Cp\u003EPlease now kick off the last query of this section to do just this which will result in \u003Ccode\u003EView DAILY_CITY_METRICS_V successfully created.\u003C/code\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECREATE OR REPLACE VIEW analytics.daily_city_metrics_v\nCOMMENT = 'Daily Weather Metrics and Orders Data'\n    AS\nSELECT\n    fd.date_valid_std AS date,\n    fd.city_name,\n    fd.country_desc,\n    ZEROIFNULL(SUM(odv.price)) AS daily_sales,\n    ROUND(AVG(fd.avg_temperature_air_2m_f),2) AS avg_temperature_fahrenheit,\n    ROUND(AVG(tb_101.analytics.fahrenheit_to_celsius(fd.avg_temperature_air_2m_f)),2) AS avg_temperature_celsius,\n    ROUND(AVG(fd.tot_precipitation_in),2) AS avg_precipitation_inches,\n    ROUND(AVG(tb_101.analytics.inch_to_millimeter(fd.tot_precipitation_in)),2) AS avg_precipitation_millimeters,\n    MAX(fd.max_wind_speed_100m_mph) AS max_wind_speed_100m_mph\nFROM tb_101.harmonized.daily_weather_v fd\nLEFT JOIN tb_101.harmonized.orders_v odv\n    ON fd.date_valid_std = DATE(odv.order_ts)\n    AND fd.city_name = odv.primary_city\n    AND fd.country_desc = odv.country\nGROUP BY fd.date_valid_std, fd.city_name, fd.country_desc;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAmazing we have now democratized these sort of insights to the Tasty Bytes organization. Let's bring this all together in our next section and validate our work.\u003C/p\u003E\n","\u003Ch3\u003EStep 4 - Click Next -->\u003C/h3\u003E\n","\u003Ch2\u003EUsing Harmonized Data to Answer Questions from the Business\u003C/h2\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003EWith Harmonized Sales and Weather Data now available in our Analytics layer, our users can begin to leverage this data to derive insights and ensure we are using data when making decisions.\u003C/p\u003E\n","\u003Cp\u003EIn this step, we will leverage our work so far to answer a few questions an analyst may receive from the business.\u003C/p\u003E\n","\u003Ch3\u003EStep 1 - Answer Questions with Data\u003C/h3\u003E\n","\u003Cp\u003EEarlier we had to manually join Point of Sales and Weather Source Data to investigate our Hamburg sales issues, but we greatly simplified that process via our \u003Ccode\u003Eanalytics.daily_city_metrics_v\u003C/code\u003E View.\u003C/p\u003E\n","\u003Cp\u003ELet's now run a few questions to answer real questions our analysts are recieving from the business.\u003C/p\u003E\n","\u003Ch4\u003EWhat were the 2022 Sales in Hamburg on days with over 1/4 inch of Precipitation?\u003C/h4\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT\n    * EXCLUDE (city_name, country_desc)\nFROM analytics.daily_city_metrics_v\nWHERE 1=1\n    AND country_desc = 'Germany'\n    AND city_name = 'Hamburg'\n    AND YEAR(date) = '2022' \n    AND avg_precipitation_inches > 0.25\nORDER BY date DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration/q1.png\" alt=\"assets/q1.png\"\u003E\u003C/p\u003E\n","\u003Ch4\u003EWhat Cities in the United States have had sales on days with temperatures below freezing?\u003C/h4\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT  \n    city_name AS city,\n    ARRAY_AGG(date) AS date_array\nFROM analytics.daily_city_metrics_v\nWHERE 1=1\n    AND avg_temperature_fahrenheit <= 32\n    AND daily_sales > 0\n    AND country_desc = 'United States'\n    AND YEAR(date) >= 2022\nGROUP BY city;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration/q2.png\" alt=\"assets/q2.png\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EYay!\u003C/strong\u003E If this had been available when our Financial Analysts were initially running their research, they would not have even needed to ping our data teams as the insights are right there.\u003C/p\u003E\n","\u003Cp\u003EBy completing this Quickstart we have seen how quickly we are able to derive real world business value by our work and how easy it is to use the Snowflake Marketplace to unlock additional data insights.\u003C/p\u003E\n","\u003Ch3\u003EStep 2 - Click Next -->\u003C/h3\u003E\n","\u003Ch2\u003EConclusion and Next Steps\u003C/h2\u003E\n","\u003Ch3\u003EConclusion\u003C/h3\u003E\n","\u003Cp\u003EFantastic work! You have successfully completed the Tasty Bytes - Zero to Snowflake - Collaboration Quickstart.\u003C/p\u003E\n","\u003Cp\u003EBy doing so you have now:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EAccessed the Snowflake Marketplace\u003C/li\u003E\u003Cli\u003EAcquired Live Weather Source Data in your Account\u003C/li\u003E\u003Cli\u003ECreated a View\u003C/li\u003E\u003Cli\u003ECreated a SQL Function\u003C/li\u003E\u003Cli\u003ELeveraged Snowsight Charts to Explore Visual Insights\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EIf you would like to re-run this Quickstart please leverage the Reset scripts in the bottom of your associated Worksheet.\u003C/p\u003E\n","\u003Ch3\u003ENext Steps\u003C/h3\u003E\n","\u003Cp\u003ETo continue your journey in the Snowflake AI Data Cloud, please now visit the link below to see all other Powered by Tasty Bytes - Quickstarts available to you.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Ch3\u003E\u003Ca href=\"/en/developers/guides/tasty-bytes-introduction/\"\u003EPowered by Tasty Bytes - Quickstarts Table of Contents\u003C/a\u003E\u003C/h3\u003E\n\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Tasty Bytes - Zero to Snowflake - Collaboration",":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\r\n\u003C!-- ------------------------ --\u003E\r\n\r\n## Collaboration in Snowflake\r\n![assets/collaboration_header.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration/collaboration_header.png)\r\n\r\n### Overview\r\nWelcome to the Tasty Bytes - Zero to Snowflake Quickstart focused on Collaboration! Within this Quickstart we will highlight how you can enrich your first-party data with weather data via Snowflake Marketplace. Snowflake Marketplace is where you can find, try, and buy third party data and apps, all without having to set up any ETL, data pipelines, or integrations.\r\n\r\n\r\n### Prerequisites\r\n- Before beginning, please make sure you have completed the [**Introduction to Tasty Bytes Quickstart**](/en/developers/guides/tasty-bytes-introduction/) which provides a walkthrough on setting up a trial account and deploying the Tasty Bytes Foundation required to complete this Quickstart.\r\n\r\n### What You Will Learn\r\n- How to Access the Snowflake Marketplace\r\n- How to Acquire Live Weather Source Data in your Account\r\n- How to Create a View\r\n- How to Create a SQL Function\r\n- How to Leverage Snowsight Charts to Explore Visual Insights\r\n\r\n### What You Will Build\r\n- The Harmonization of First Party Sales and Third Party Weather Data\r\n- Seamless convertibility from Fahrenheit to Celsius\r\n- Seamless convertibility from Inch to Millimeter\r\n- An Understanding of How to Unlock Additional Insights via the Snowflake Marketplace\r\n\r\n## Creating a Worksheet and Copying in our SQL\r\n\r\n### Overview\r\nWithin this Quickstart we will follow a Tasty Bytes themed story via a Snowsight SQL Worksheet with this page serving as a side by side guide complete with additional commentary, images and documentation links.\r\n\r\nThis section will walk you through logging into Snowflake, Creating a New Worksheet, Renaming the Worksheet, Copying SQL from GitHub, and Pasting the SQL we will be leveraging within this Quickstart.\r\n\r\n### Step 1 - Accessing Snowflake via URL\r\n- Open a browser window and enter the URL of your Snowflake Account \r\n\r\n### Step 2 - Logging into Snowflake\r\n- Log into your Snowflake account.\r\n\r\n### Step 3 - Navigating to Worksheets\r\n- Click on the Projects Tab in the left-hand navigation bar and click Worksheets.\r\n\r\n### Step 4 - Creating a Worksheet\r\n- Within Worksheets, click the \"+\" button in the top-right corner of Snowsight.\r\n\r\n### Step 5 - Renaming a Worksheet\r\n- Rename the Worksheet by clicking on the auto-generated Timestamp name and inputting \"Tasty Bytes - Collaboration\"\r\n\r\n### Step 6 - Accessing Quickstart SQL in GitHub\r\n- Click the button below which will direct you to our Tasty Bytes SQL file that is hosted on GitHub.\r\n\r\n\u003Cbutton\u003E[tb_zts_collaboration.sql](https://github.com/Snowflake-Labs/sf-samples/blob/main/samples/tasty_bytes/FY25_Zero_To_Snowflake/tb_collaboration.sql)\u003C/button\u003E\r\n\r\n### Step 7 - Copying Setup SQL from GitHub\r\n- Within GitHub navigate to the right side and click \"Copy raw contents\". This will copy all of the required SQL into your clipboard.\r\n    - ![assets/github_copy_raw_contents.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration/github_copy_raw_contents.png)\r\n\r\n### Step 8 - Pasting Setup SQL from GitHub into your Snowflake Worksheet\r\n- Path back to Snowsight and your newly created Worksheet and Paste (*CMD + V for Mac or CTRL + V for Windows*) what we just copied from GitHub.\r\n\r\n### Step 9 - Click Next --\u003E\r\n\r\n## Investigating Days with Zero Sales\r\n\r\n### Overview\r\nTasty Bytes Financial Analysts have let the business know that there are Trucks in the Raw Point of Sales Sytem Data that are missing Sales for various days.\r\n \r\nWe will investigate these missing days and leverage the Snowflake Marketplace to enhance our analysis with Weather and Location data.\r\n\r\nWe will then dive into one example where Hamburg, Germany Trucks were flagged as having zero sales for a few days in February 2022.\r\n\r\n\r\n### Step 1 - Querying Point of Sales Data for Trends\r\nLet's start by kicking off this steps three queries to initially set our Role, Warehouse and Database context to `tb_data_engineer`, `tb_de_wh`, `tb_101`. With the context set, we will then query our Analytics `orders_v` View to provide a result set of sales for Hamburg, Germany in 2022.\r\n\r\n```\r\nUSE ROLE tb_data_engineer;\r\nUSE WAREHOUSE tb_de_wh;\r\nUSE DATABASE tb_101;\r\n\r\nWITH _feb_date_dim AS \r\n    (\r\n    SELECT DATEADD(DAY, SEQ4(), '2022-02-01') AS date FROM TABLE(GENERATOR(ROWCOUNT =\u003E 28))\r\n    )\r\nSELECT\r\n    fdd.date,\r\n    ZEROIFNULL(SUM(o.price)) AS daily_sales\r\nFROM _feb_date_dim fdd\r\nLEFT JOIN analytics.orders_v o\r\n    ON fdd.date = DATE(o.order_ts)\r\n    AND o.country = 'Germany'\r\n    AND o.primary_city = 'Hamburg'\r\nWHERE fdd.date BETWEEN '2022-02-01' AND '2022-02-28'\r\nGROUP BY fdd.date\r\nORDER BY fdd.date ASC;\r\n```\r\n\r\n![assets/orders_v.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration/orders_v.png)\r\n\r\nBased on what we are seeing above, we can agree with our analysts that we do not have daily sales records for a few days in February so our analysts are definitely on to something. Let's see if we can dig further into why this may have happened in our next section.\r\n\r\n### Step 2 - Click Next --\u003E\r\n\r\n## Acquiring Weather Source Data from the Snowflake Marketplace \r\n\r\n### Overview\r\nFrom what we saw above, it looks like we are missing sales for February 16th through February 21st for Hamburg. Within our first party data there is not much else we can use to investigate what might have caused this.\r\n\r\nHowever, with live data sets and native applications available in the Snowflake Marketplace we can immediately add Weather Metrics to our analysis and determine if severe weather may have been the root cause.\r\n\r\nWithin this step, we will retrieve a free, public listing provided by Weather Source, who is a leading provider of global weather and climate data.\r\n\r\n### Step 1 - Acquiring the Weather Source LLC: frostbyte Snowflake Marketplace Listing\r\nThe Snowflake Marketplace is the premier location to find, try, and buy the data and applications you need to power innovative business solutions. In this step, we will be access the [Weather Source LLC: frostbyte](https://app.snowflake.com/marketplace/listing/GZSOZ1LLEL/weather-source-llc-weather-source-llc-frostbyte) listing to help drive additional analysis on our Hamburg sales slump.\r\n\r\nPlease follow the steps below to acquire this listing in your Snowflake Account.\r\n\r\n- In the bottom left corner, ensure you are operating as ACCOUNTADMIN\r\n- In the left pane, navigate to 'Data Products' (Cloud Icon) and select 'Marketplace'\r\n- In the search bar, enter: 'frostbyte'\r\n- Select the 'Weather Source LLC: frostbyte' listing and click 'Get'\r\n- Adjust Database name to:'TB_WEATHERSOURCE'\r\n- Grant access to: 'PUBLIC'\r\n\r\n\u003Easide positive\r\n\u003EWeather Source is a leading provider of global weather and climate data and its OnPoint Product Suite provides businesses with the necessary weather and climate data to quickly generate meaningful and actionable insights for a wide range of use cases across industries.\r\n\u003E\r\n\r\n### Step 2 - Harmonizing First and Third Party Data\r\nWith the shared `tb_weathersource` database in place, please execute this steps query to create a `harmonized.daily_weather_v` View joining two Weather Source tables to our country table on the Countries and Cities that Tasty Bytes Food Trucks operate within. This query will provide a `View DAILY_WEATHER_V successfully created.` result.\r\n\r\n```\r\nCREATE OR REPLACE VIEW harmonized.daily_weather_v\r\nCOMMENT = 'Weather Source Daily History filtered to Tasty Bytes supported Cities'\r\n    AS\r\nSELECT\r\n    hd.*,\r\n    TO_VARCHAR(hd.date_valid_std, 'YYYY-MM') AS yyyy_mm,\r\n    pc.city_name AS city,\r\n    c.country AS country_desc\r\nFROM tb_weathersource.onpoint_id.history_day hd\r\nJOIN tb_weathersource.onpoint_id.postal_codes pc\r\n    ON pc.postal_code = hd.postal_code\r\n    AND pc.country = hd.country\r\nJOIN tb_101.raw_pos.country c\r\n    ON c.iso_country = hd.country\r\n    AND c.city = hd.city_name;\r\n\r\n```\r\n\r\n\r\nAs we see in the View definition above we are joining two of the `tb_weathersource` Tables within the `onpoint_id` Schema and then Harmonizing it with our `country` Table from our `tb_101` Database and `raw_pos` Schema. \r\n\r\nThis is the sort of operation we typically find in the Harmonized layer or what others may describe as the Silver zone.\r\n\r\n### Step 3 - Visualizing Daily Temperatures\r\nWith the `daily_weather_v` View in our Harmonized Schema in place let's take a look at the Average Daily Weather Temperature for Hamburg in February 2022 by executing our next query.\r\n\r\nAlong the way we will leverage [AVG](https://docs.snowflake.com/en/sql-reference/functions/avg), [YEAR](https://docs.snowflake.com/en/sql-reference/functions/year) and [MONTH](https://docs.snowflake.com/en/sql-reference/functions/year) functions.\r\n\r\n```\r\nSELECT\r\n    dw.country_desc,\r\n    dw.city_name,\r\n    dw.date_valid_std,\r\n    AVG(dw.avg_temperature_air_2m_f) AS avg_temperature_air_2m_f\r\nFROM harmonized.daily_weather_v dw\r\nWHERE 1=1\r\n    AND dw.country_desc = 'Germany'\r\n    AND dw.city_name = 'Hamburg'\r\n    AND YEAR(date_valid_std) = '2022'\r\n    AND MONTH(date_valid_std) = '2' -- February\r\nGROUP BY dw.country_desc, dw.city_name, dw.date_valid_std\r\nORDER BY dw.date_valid_std DESC;\r\n```\r\n\r\n![assets/germany_temp.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration/germany_temp.png)\r\n\r\nTo further investigate trends, let's utilize Snowsight Charting to create a Line Graph of the Average Temperature over time.\r\n- Chart Type: Line\r\n- X-Axis: DATE_VALID_STD(none)\r\n- Line: AVG_TEMPERATURE_AIR_2M_F(none)\r\n\r\n![assets/temp_chart.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration/temp_chart.png)\r\n\r\nBased on what we saw above, there is nothing really standing out yet as the obvious reason for zero sales days at our trucks. Let's see what else we can find that might explain things in the next step.\r\n\r\n### Step 4 - Bringing in Wind Data\r\nAs we saw in our previous step, it does not look like Average Daily Temperature is the reason for our zero sales days in Hamburg. Thankfully, Weather Source provides other weather metrics we can dive into as well. \r\n\r\nPlease now execute the next query where we will leverage our Harmonized View to bring in Wind metrics. In this query we will see the usage of our [MAX](https://docs.snowflake.com/en/sql-reference/functions/min) function.\r\n\r\n```\r\nSELECT\r\n    dw.country_desc,\r\n    dw.city_name,\r\n    dw.date_valid_std,\r\n    MAX(dw.max_wind_speed_100m_mph) AS max_wind_speed_100m_mph\r\nFROM harmonized.daily_weather_v dw\r\nWHERE 1=1\r\n    AND dw.country_desc IN ('Germany')\r\n    AND dw.city_name = 'Hamburg'\r\n    AND YEAR(date_valid_std) = '2022'\r\n    AND MONTH(date_valid_std) = '2' -- February\r\nGROUP BY dw.country_desc, dw.city_name, dw.date_valid_std\r\nORDER BY dw.date_valid_std DESC;\r\n```\r\n\r\n![assets/germany_wind.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration/germany_wind.png)\r\n\r\nOnce again this sort of data might better present trends via a quick Snowsight Chart.\r\n- Chart Type: Line\r\n- X-Axis: DATE\r\n- Line: MAX_WIND_SPEED_100M_MPH(none)\r\n\r\n![assets/wind_chart.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration/wind_chart.png)\r\n\r\n**Ah ha!** The wind for those zero sales days was at hurricane levels. This seems to be a better reason for why our trucks were not able to sell anything on those days. However since we ran this analysis in Harmonized let's now begin on our path to make this accessible in Analytics where our analysts can access these insights on their own.\r\n\r\n### Step 5 - Click Next --\u003E\r\n\r\n## Democratizing First and Third Party Data\r\n\r\n### Overview\r\nWe have determined that hurricane-level winds were likely the reason for the days with zero sales that our financial analysts brought to our attention.\r\n\r\nLet's now make these sort of research available to anyone in our organization by deploying an Analytics view that all Tasty Bytes employees can access.\r\n\r\n### Step 1 - Creating SQL Functions\r\nAs we are a global company, let's start our process by first creating two SQL functions to convert Fahrenheit to Celsius and Inches to Millimeters. \r\n\r\nPlease execute the two queries within this step one by one to create our `fahrenheit_to_celsius` and `inch_to_millimeter` functions which leverage the [CREATE FUNCTION](https://docs.snowflake.com/en/sql-reference/sql/create-function) command and result in `Function _ successfully created.`\r\n\r\n\r\n```\r\nCREATE OR REPLACE FUNCTION analytics.fahrenheit_to_celsius(temp_f NUMBER(35,4))\r\nRETURNS NUMBER(35,4)\r\nAS\r\n$$\r\n    (temp_f - 32) * (5/9)\r\n$$;\r\n```\r\n\r\n```\r\nCREATE OR REPLACE FUNCTION analytics.inch_to_millimeter(inch NUMBER(35,4))\r\nRETURNS NUMBER(35,4)\r\n    AS\r\n$$\r\n    inch * 25.4\r\n$$;\r\n```\r\n\r\n\u003Easide positive\r\n\u003EWhen you create a UDF, you specify a handler whose code is written in one of the supported languages. Depending on the handler’s language, you can either include the handler source code in-line with the CREATE FUNCTION statement or reference the handler’s location from CREATE FUNCTION, where the handler is precompiled or source code on a stage.\r\n\u003E\r\n\r\n### Step 2 - Creating the SQL for our View\r\nBefore deploying our Analytics view, let's create our SQL we will use in the View to combine Daily Sales and Weather together and also leverage our SQL conversion functions. \r\n\r\nPlease execute the next query where we filter for Hamburg, Germany and leverage a few functions we have not seen yet being [ZEROIFNULL](https://docs.snowflake.com/en/sql-reference/functions/zeroifnull), [ROUND](https://docs.snowflake.com/en/sql-reference/functions/round) and [DATE](https://docs.snowflake.com/en/sql-reference/functions/to_date).\r\n\r\n```\r\nSELECT\r\n    fd.date_valid_std AS date,\r\n    fd.city_name,\r\n    fd.country_desc,\r\n    ZEROIFNULL(SUM(odv.price)) AS daily_sales,\r\n    ROUND(AVG(fd.avg_temperature_air_2m_f),2) AS avg_temperature_fahrenheit,\r\n    ROUND(AVG(analytics.fahrenheit_to_celsius(fd.avg_temperature_air_2m_f)),2) AS avg_temperature_celsius,\r\n    ROUND(AVG(fd.tot_precipitation_in),2) AS avg_precipitation_inches,\r\n    ROUND(AVG(analytics.inch_to_millimeter(fd.tot_precipitation_in)),2) AS avg_precipitation_millimeters,\r\n    MAX(fd.max_wind_speed_100m_mph) AS max_wind_speed_100m_mph\r\nFROM harmonized.daily_weather_v fd\r\nLEFT JOIN harmonized.orders_v odv\r\n    ON fd.date_valid_std = DATE(odv.order_ts)\r\n    AND fd.city_name = odv.primary_city\r\n    AND fd.country_desc = odv.country\r\nWHERE 1=1\r\n    AND fd.country_desc = 'Germany'\r\n    AND fd.city = 'Hamburg'\r\n    AND fd.yyyy_mm = '2022-02'\r\nGROUP BY fd.date_valid_std, fd.city_name, fd.country_desc\r\nORDER BY fd.date_valid_std ASC;\r\n```\r\n\r\n![assets/sql_for_view.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration/sql_for_view.png)\r\n\r\nThe results we have just recieved look great. We can now wrap this SQL within a View in our next step.\r\n\r\n### Step 3 - Deploying our Analytics View\r\nUsing the same query we just explored, we will need to remove the filters in the WHERE clause, add a [COMMENT](https://docs.snowflake.com/en/sql-reference/sql/comment) and promote this to our `analytics` Schema as the `daily_city_metrics_v` View.\r\n\r\nPlease now kick off the last query of this section to do just this which will result in `View DAILY_CITY_METRICS_V successfully created.`\r\n\r\n```\r\nCREATE OR REPLACE VIEW analytics.daily_city_metrics_v\r\nCOMMENT = 'Daily Weather Metrics and Orders Data'\r\n    AS\r\nSELECT\r\n    fd.date_valid_std AS date,\r\n    fd.city_name,\r\n    fd.country_desc,\r\n    ZEROIFNULL(SUM(odv.price)) AS daily_sales,\r\n    ROUND(AVG(fd.avg_temperature_air_2m_f),2) AS avg_temperature_fahrenheit,\r\n    ROUND(AVG(tb_101.analytics.fahrenheit_to_celsius(fd.avg_temperature_air_2m_f)),2) AS avg_temperature_celsius,\r\n    ROUND(AVG(fd.tot_precipitation_in),2) AS avg_precipitation_inches,\r\n    ROUND(AVG(tb_101.analytics.inch_to_millimeter(fd.tot_precipitation_in)),2) AS avg_precipitation_millimeters,\r\n    MAX(fd.max_wind_speed_100m_mph) AS max_wind_speed_100m_mph\r\nFROM tb_101.harmonized.daily_weather_v fd\r\nLEFT JOIN tb_101.harmonized.orders_v odv\r\n    ON fd.date_valid_std = DATE(odv.order_ts)\r\n    AND fd.city_name = odv.primary_city\r\n    AND fd.country_desc = odv.country\r\nGROUP BY fd.date_valid_std, fd.city_name, fd.country_desc;\r\n```\r\n\r\n\r\nAmazing we have now democratized these sort of insights to the Tasty Bytes organization. Let's bring this all together in our next section and validate our work.\r\n\r\n### Step 4 - Click Next --\u003E\r\n\r\n## Using Harmonized Data to Answer Questions from the Business\r\n\r\n### Overview\r\nWith Harmonized Sales and Weather Data now available in our Analytics layer, our users can begin to leverage this data to derive insights and ensure we are using data when making decisions.\r\n\r\nIn this step, we will leverage our work so far to answer a few questions an analyst may receive from the business.\r\n\r\n### Step 1 - Answer Questions with Data\r\nEarlier we had to manually join Point of Sales and Weather Source Data to investigate our Hamburg sales issues, but we greatly simplified that process via our `analytics.daily_city_metrics_v` View. \r\n\r\nLet's now run a few questions to answer real questions our analysts are recieving from the business.\r\n\r\n\r\n#### What were the 2022 Sales in Hamburg on days with over 1/4 inch of Precipitation?\r\n\r\n```\r\nSELECT\r\n    * EXCLUDE (city_name, country_desc)\r\nFROM analytics.daily_city_metrics_v\r\nWHERE 1=1\r\n    AND country_desc = 'Germany'\r\n    AND city_name = 'Hamburg'\r\n    AND YEAR(date) = '2022' \r\n    AND avg_precipitation_inches \u003E 0.25\r\nORDER BY date DESC;\r\n```\r\n\r\n![assets/q1.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration/q1.png)\r\n\r\n\r\n\r\n#### What Cities in the United States have had sales on days with temperatures below freezing?\r\n\r\n```\r\nSELECT  \r\n    city_name AS city,\r\n    ARRAY_AGG(date) AS date_array\r\nFROM analytics.daily_city_metrics_v\r\nWHERE 1=1\r\n    AND avg_temperature_fahrenheit \u003C= 32\r\n    AND daily_sales \u003E 0\r\n    AND country_desc = 'United States'\r\n    AND YEAR(date) \u003E= 2022\r\nGROUP BY city;\r\n```\r\n\r\n![assets/q2.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/tasty-bytes-zero-to-snowflake-collaboration/q2.png)\r\n\r\n\r\n\r\n**Yay!** If this had been available when our Financial Analysts were initially running their research, they would not have even needed to ping our data teams as the insights are right there. \r\n\r\nBy completing this Quickstart we have seen how quickly we are able to derive real world business value by our work and how easy it is to use the Snowflake Marketplace to unlock additional data insights.\r\n\r\n### Step 2 - Click Next --\u003E\r\n\r\n## Conclusion and Next Steps\r\n\r\n### Conclusion\r\nFantastic work! You have successfully completed the Tasty Bytes - Zero to Snowflake - Collaboration Quickstart. \r\n\r\nBy doing so you have now:\r\n- Accessed the Snowflake Marketplace\r\n- Acquired Live Weather Source Data in your Account\r\n- Created a View\r\n- Created a SQL Function\r\n- Leveraged Snowsight Charts to Explore Visual Insights\r\n\r\nIf you would like to re-run this Quickstart please leverage the Reset scripts in the bottom of your associated Worksheet.\r\n\r\n### Next Steps\r\nTo continue your journey in the Snowflake AI Data Cloud, please now visit the link below to see all other Powered by Tasty Bytes - Quickstarts available to you.\r\n\r\n- ### [Powered by Tasty Bytes - Quickstarts Table of Contents](/en/developers/guides/tasty-bytes-introduction/)\r\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"isDeveloperGuidesPage":false,"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-361592f223","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-62c6201dc4",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-44923800e4","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2024-05-24",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-c76c76a2d9","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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