Azure Synapse Serverless SQL Pool Feature Support – What’s Allowed and What’s No | Azure Synapse Analytics Tutorial

📘 Serverless SQL Pool - Feature Support Overview | Azure Synapse Analytics

Azure Synapse Analytics Serverless SQL Pool enables you to analyze data in your data lake without needing to provision infrastructure. It's ideal for ad hoc querying and lightweight reporting scenarios.

✅ What's Supported in Serverless SQL Pool

• Schemas, Views, Stored Procedures, Functions: You can define logical schemas, and create reusable views, stored procedures, and inline table-valued functions.
• External Resources: Serverless SQL supports querying external sources such as Azure Data Lake Storage (ADLS), Azure Blob Storage, and Azure Cosmos DB analytical store.
• T-SQL Functions: A broad range of built-in T-SQL functions are supported (string, date, math, and aggregate).
• Auto Statistics: Automatically generated statistics are used to optimize queries. Manual updates aren't supported.
• Cross-Database Queries: You can query across databases within the same Synapse workspace.

❌ What’s Not Supported

• Persistent Tables: Serverless SQL doesn’t support traditional DDL table creation. Use EXTERNAL TABLE definitions instead.
• Triggers: No support for DML triggers since DML operations are restricted.
• INSERT/UPDATE/DELETE: You cannot perform data modification via T-SQL. These are read-only environments.

🔗 Best Practices & Recommendations

• Use for read-heavy, on-demand analytics
• Pair with Synapse Pipelines or Spark for write operations
• Ideal for CSV, Parquet, and JSON file querying directly from data lake

🧪 Example Query with External Table

SELECT *
FROM OPENROWSET(
    BULK 'https://yourlakehouse.dfs.core.windows.net/sales/customer.csv',
    FORMAT = 'CSV',
    PARSER_VERSION = '2.0',
    HEADER_ROW = TRUE
) AS result;

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Credit: This blog post was created with the help of ChatGPT and Gemini.