In the + menu, select Integration dataset to create a new dataset. In this section, you’ll create datasets for the SQL tables that will serve as data sinks for data pipelines you’ll create later in this lab. User profile data containing, among other things, product preferences and product reviews is stored as JSON documents in Cosmos DB. The customer profile data from an e-commerce system that provides top product purchases for each visitor of the site (customer) over the past 12 months is stored within JSON files in the data lake. User profile data comes from two different data sources, which we will create now. Then select Test connection to ensure success, before clicking Create. Name the linked service asacosmosdb01, and then select the asacosmosdb xxxxxxx Cosmos DB account name and the CustomerProfile database. Select Azure Cosmos DB (SQL API) in the list of options, then select Continue. Open Linked services and select + New to create a new linked service. Note: Skip this section if you have already created a Cosmos DB linked service. Follow the steps in this section to create one. Tailwind Traders has not yet created the linked service. (50) NOT NULL,Īzure Cosmos DB is one of the data sources that will be used in the Mapping Data Flow. In the query window, replace the script with the following to create a new table for the Campaign Analytics CSV file: CREATE TABLE. Select Run on the toolbar menu to run the script (you may need to wait for the SQL pool to resume). In the query window, replace the script with the following code to create a new table that joins users’ preferred products stored in Azure Cosmos DB with top product purchases per user from the e-commerce site, stored in JSON files within the data lake: CREATE TABLE. In the toolbar menu, connect to the SQLPool01 database. In Synapse Analytics Studio, navigate to the Develop hub. We will execute a SQL script to create this table as a pre-requisite. Tailwind Traders does not yet have a table to store this data. The Mapping Data Flow we will build will write user purchase data to a dedicated SQL pool. Monitor and manage data flows from a single pane of glass.Flexibility to transform data per user’s comfort.Guided experience to easily build resilient data flows.This feature offers data cleansing, transformation, aggregation, conversion, joins, data copy operations, etc. Mapping Data flows are pipeline activities that provide a visual way of specifying how to transform data, through a code-free experience. Given these requirements, you recommend building Mapping Data Flows. This gives them the flexibility to retain more fields in their data sets than they otherwise store in fact and dimension tables, and doing this allows them to access the data when they have paused the dedicated SQL pool, as a cost optimization. Their other requirement is to maintain transformed data in a data lake in addition to the dedicated SQL pool. The other driver for this requirement is to reduce fragility caused by complex code with reliance on libraries pinned to specific versions, remove code testing requirements, and improve ease of long-term maintenance. Their motivation is driven by the desire to allow junior-level data engineers who understand the data but do not have a lot of development experience build and maintain data transformation operations. Tailwind Traders would like code-free options for data engineering tasks. If you take a break from this lab, or decide not to complete it follow the instructions at the end of the lab to pause your SQL pool! Exercise 1 - Code-free transformation at scale with Azure Synapse Pipelines Important: Once started, a dedicated SQL pool consumes credits in your Azure subscription until it is paused. Continue to the next exercise while the dedicated SQL pool resumes.It will take a minute or two to resume the pool. If the SQLPool01 dedicated SQL pool is paused, hover over its name and select ▷. You should have paused the SQL pool at the end of the previous lab, so resume it by following these instructions: This lab uses the dedicated SQL pool you created in the previous lab. Create data pipeline to import poorly formatted CSV filesīefore starting this lab, you must complete Lab 5: Ingest and load data into the Data Warehouse.Execute code-free transformations at scale with Azure Synapse Pipelines.This lab teaches you how to build data integration pipelines to ingest from multiple data sources, transform data using mapping data flows and notebooks, and perform data movement into one or more data sinks.Īfter completing this lab, you will be able to: Lab 6 - Transform data with Azure Data Factory or Azure Synapse Pipelines
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