Dedicated vs serverless pools - difference and performance

WeirdMan 340 Reputation points
2025-03-24T12:25:31.65+00:00

I am working with dedicated pools in Azure Synapse, but never had a concrete understanding when to use them and when to use the serverless pools. What is the difference between them in terms of performance and what are the best practices ?

Azure Synapse Analytics
Azure Synapse Analytics
An Azure analytics service that brings together data integration, enterprise data warehousing, and big data analytics. Previously known as Azure SQL Data Warehouse.
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  1. Amira Bedhiafi 31,396 Reputation points
    2025-03-24T12:28:27.8466667+00:00

    Hello WeirdMan !

    Thank you for posting on Microsoft Learn.

    I am summarizing the differences between Dedicated SQL Pools and Serverless SQL Pools in Azure Synapse Analytics, including their performance characteristics, use cases, and best practices—summarized in a table format:

    Aspect Dedicated SQL Pool Serverless SQL Pool
    Provisioning Requires explicit resource provisioning (DWUs) On-demand, no provisioning needed
    Provisioning Requires explicit resource provisioning (DWUs) On-demand, no provisioning needed
    Billing Billed per hour based on DWU, regardless of usage Billed per TB of data processed
    Performance High performance for large, complex workloads Suitable for ad-hoc, lightweight, or infrequent queries
    Scalability Manually scalable (DWU scaling or pause/resume) Auto-scales behind the scenes
    Data Storage Stores data in dedicated distributed tables (example CCI) Queries external data (e.g., in Data Lake using OPENROWSET)
    Latency Lower latency for repeated queries on large data volumes Higher latency for large data scans or repeated queries
    Data Source Data stored in dedicated SQL pool tables External data in ADLS Gen2, Cosmos DB, etc.
    Use Cases - Large ETL workloads- Complex joins/aggregations- High concurrency- Data warehousing - Data exploration- Quick dashboards- ELT validation- Cost-efficient ad-hoc analytics
    Optimization Options Distribution, partitioning, indexing, materialized views Limited optimization; best practices include filtering early
    Concurrency Higher concurrency with proper scaling Limited by backend resources; good for smaller user base
    Security & Governance Advanced RBAC, auditing, managed VNET Supports RBAC; less granular control compared to dedicated

    https://learn.microsoft.com/en-us/azure/synapse-analytics/sql/best-practices-dedicated-sql-pool

    https://learn.microsoft.com/en-us/azure/synapse-analytics/sql/best-practices-serverless-sql-pool

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