Azure Synapse Analytics, Serverless SQL Pools

A Serverless SQL Pool endpoint is a public entry point to an Azure Synapse Analytics Serverless SQL Pool. This endpoint enables you to query data stored in your Synapse workspace using the SQL language, without needing to manage a dedicated SQL instance.

Every Azure Synapse Analytics workspace comes with Serverless SQL Pool endpoints that you can use to query data in the Azure Data Lake (Parquet, Delta Lake, delimited text formats), Azure Cosmos DB, or Dataverse.

The Serverless SQL Pool endpoint is a REST API that provides programmatic access to the Synapse Serverless SQL Pool. You can use the endpoint to execute SQL queries, create and manage databases and tables, and perform other data management tasks.

To use the Serverless SQL Pool endpoint, you need to authenticate using Azure Active Directory (AAD) credentials or a Shared Access Signature (SAS) token. You can send HTTP requests to the endpoint using tools like PowerShell, Azure CLI, or other REST API clients.

Once you’ve authenticated, you can use SQL statements to query data in your Synapse workspace. The Serverless SQL Pool endpoint supports a subset of the T-SQL language, including SELECT, INSERT, UPDATE, DELETE, and more. You can also use functions, stored procedures, and views to perform more complex data manipulation tasks.

In summary, the Serverless SQL Pool endpoint provides a simple and flexible way to query and manage data in an Azure Synapse Analytics Serverless SQL Pool, without needing to manage a dedicated SQL instance. It offers a REST API interface that supports a subset of T-SQL language and can be used with various tools and programming languages.

What are the Serverless SQL Pool benefits?

If you need to explore data in the data lake, gain insights from it or optimize your existing data transformation pipeline, you can benefit from using serverless SQL pool. Suitability includes the following scenarios:

• Basic discovery and exploration – Quickly reason about the data in various formats (Parquet, CSV, JSON) in your data lake, so you can plan how to extract insights from it.
• Logical data warehouse – Provide a relational abstraction on top of raw or disparate data without relocating and transforming data, allowing always up-to-date view of your data. Learn more about creating logical data warehouse.
• Data transformation – Simple, scalable, and performant way to transform data in the lake using T-SQL, so it can be fed to Business Intelligence applications (BI) and other tools or loaded into a relational data store (Synapse SQL databases, Azure SQL Database, etc.).

Who can benefit from Serverless SQL Pool?

• Data Engineers can explore the data lake, transform, and prepare data using this service, and simplify their data transformation pipelines. For more information, check this tutorial.
• Data Scientists can quickly reason about the contents and structure of the data in the lake, thanks to features such as OPENROWSET and automatic schema inference.
• Data Analysts can explore data and Spark external tables created by Data Scientists or Data Engineers using familiar T-SQL language or their favorite tools, which can connect to serverless SQL pool.
• BI Professionals can quickly create Power BI reports on top of data in the lake and Spark tables.