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Building a Data Warehouse Dimensional Model using Azure Synapse Analytics SQL Serverless
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The Serverless SQL Pools service within Azure Synapse Analytics allows querying CSV, JSON and Parquet data in Azure Storage, Data Lake Gen1/2 and Cosmos DB. With this functionality we are able to create a Logical Data Warehouse over data stored in these systems without moving and loading the data. However, the source data may not be in the best possible format for analytical workloads...
In this session we'll be looking at using Azure Synapse Analytics SQL Serverless Pools to create a Data Warehouse using the Dimensional Modelling technique to create a set of Dimensions and Facts and store this data in a more appropriate structure and file format.
All data will be stored in an Azure Data Lake Gen2 account with processing and serving performed by the SQL Serverless Pools engine.
In this session we'll be looking at using Azure Synapse Analytics SQL Serverless Pools to create a Data Warehouse using the Dimensional Modelling technique to create a set of Dimensions and Facts and store this data in a more appropriate structure and file format.
All data will be stored in an Azure Data Lake Gen2 account with processing and serving performed by the SQL Serverless Pools engine.
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