A logical data warehouse enables access to multiple, diverse data sources, allows querying data from these data sources using SQL language, while displays itself as one logical data source for users. The recently released Azure Synapse Analytics brings together database, data warehouse, and data lake technologies in a single Azure service.
One of its key components is Azure Synapse Serverless SQL Pool (a.k.a. Azure Synapse On-Demand SQL Pool), which provides T-SQL queries over high-scale data in various formats (CSV, Parquet, JSON) stored in Azure Data Lake Storage Gen2. Furthermore, these T-SQL queries could get data not just from data lakes, but also from different kinds of databases (e.g. Azure Cosmos DB, Azure SQL, Synapse Dedicated SQL Pool).
Thus Azure Synapse Serverless SQL Pool ensures building logical data warehouses in a serverless manner, i.e. without implementing infrastructural details and dedicated data pipelines, while the users have to pay only for query execution costs.
In this session, we will present how to build logical data warehouses in a Serverless SQL Pool to combine diverse data coming from Azure Data Lake Storage Gen2, Azure Cosmos DB, and other databases and how to use these logical data warehouses for ad-hoc reporting, proof-of-concepts, and BI sandboxes.
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