What are the differences between Dedicated SQL Pool in Synapse Analytics and a regular MS SQL database ?

Question

I would like to know the differences in compute and storage between the two products - Dedicated SQL Pools in Synapse Analytics and Microsoft SQL Server RDBMS.

Answer 1

Hi Anil Saraswathy,

Thanks for reaching out to Microsoft Q&A.

"TLDR; Dedicated SQL Pools in Synapse is more suitable for scenarios involving large-scale, high-performance analytics, while Microsoft SQL Server excels in operational databases and transactional processing."

Both Dedicated SQL Pools in Synapse and Microsoft SQL Server serve as relational database management systems, but they differ significantly in their architecture, scalability, and operational mechanics.

Compute Architecture

Dedicated SQL Pools:

• Utilize a Massively Parallel Processing (MPP) architecture where compute and storage are decoupled. This allows for independent scaling of compute resources based on workload demands. Users can adjust the number of Data Warehouse Units (DWUs) to increase or decrease compute power as needed.
• Queries are processed across multiple compute nodes, which enhances performance for large datasets. Each dedicated SQL pool can have up to 60 distributions, with data evenly distributed among the compute nodes.

Microsoft SQL Server RDBMS:

Operates on a more traditional architecture where compute resources are tied to a single server instance. Scaling typically involves vertical scaling (upgrading hardware) rather than horizontal scaling (adding more servers).

Performance is limited by the capabilities of the single server, making it less effective for very large datasets compared to the distributed nature of Dedicated SQL Pools.

Storage Mechanism

Dedicated SQL Pools:

• Data is stored in a columnar format, which optimizes storage costs and improves query performance. The storage layer is separate from compute nodes, allowing users to pause the compute resources without losing data, thus incurring only storage costs during inactivity.
• Data can be ingested from various sources using PolyBase, enabling efficient loading of big data into relational tables.

Microsoft SQL Server RDBMS:

• Typically uses row-based storage, which may not be as efficient for analytical workloads compared to columnar storage. The storage is directly linked to the server instance, meaning that any scaling or performance improvements require hardware upgrades.
• Does not offer the same level of flexibility in terms of pausing compute resources; users incur costs continuously as long as the server is running.

Cost Management

Dedicated SQL Pools:

• Offers a pay-as-you-go model where users can pause compute resources and only pay for storage when not actively querying data. This flexibility helps manage costs effectively, especially for variable workloads.

Microsoft SQL Server RDBMS:

• Generally involves fixed costs associated with server maintenance and operation. Scaling often requires significant upfront investment in hardware or licensing.

Scaling and Elasticity

Dedicated SQL Pool:

• Elasticity: Can scale up or down with different DWU/vCore levels depending on workload demands.
• Supports pause and resume of compute resources, which means you can stop the SQL Pool and only pay for storage.
• Ideal for batch processing, ETL, and analytics due to its ability to handle distributed compute workloads.

Microsoft SQL Server:

• Scaling is vertical (adding more CPU, memory, or storage to a single instance).
• No option to decouple compute from storage, making scaling less flexible and often requiring downtime for scaling operations.
• More suited to transactional workloads (OLTP) or smaller analytical workloads that do not require distributed compute.

Data Integration and Ecosystem

Dedicated SQL Pool

• Integrated deeply with other Azure services such as Data Lake, Azure Machine Learning, Power BI, adf, etc.
• Supports PolyBase for querying external data sources, which allows you to access data in external systems directly.

Microsoft SQL Server

• More traditional RDBMS ecosystem, with integration capabilities like SSIS, SSRS, etc.
• Integration with Azure services is possible but generally not as seamless or optimized for big data scenarios compared to Synapse.

While both systems provide robust database capabilities, Dedicated SQL Pools are optimized for large-scale analytics with flexible cost management, whereas Microsoft SQL Server RDBMS is suited for traditional transactional workloads with less scalability. Dedicated SQL Pool is designed for large-scale analytics and data warehousing with a focus on parallelism, scalability, and distributed computing. SQL Server is more suited for transactional workloads, with compute and storage tied to the server's capacity. It is not designed for handling large-scale analytics in the way that an MPP system can.

https://learn.microsoft.com/vi-vn/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-overview-what-is

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Answer 2

@Anil Saraswathy
Thank you for the question and for using Microsoft Q&A platform.
The main differences between Dedicated SQL Pools in Azure Synapse Analytics and Microsoft SQL Server RDBMS (Relational Database Management System) in terms of compute and storage lie in their architecture and scalability. Here's a breakdown:

1. Compute Architecture

Dedicated SQL Pools

Distributed Compute: Uses a Massively Parallel Processing (MPP) architecture. Data is distributed across multiple nodes, and queries are executed in parallel across these nodes.

Compute Decoupled from Storage: You can scale compute independently of storage. Compute resources (Data Warehousing Units or DWUs) are provisioned, and you pay based on the amount of compute you use, not on storage.

Auto-pause and Resume: SQL Pools can be paused to save costs, as you only pay for storage during the pause.

Elasticity: Compute resources can be scaled up or down dynamically based on workload requirements.

Microsoft SQL Server RDBMS

Single Node Compute: SQL Server uses a Symmetric Multi-Processing (SMP) architecture, where queries are processed on a single node (server). Compute is not distributed, so scalability is more limited.

Compute Bound to Storage: Compute and storage are generally tied together. To scale compute, you typically need to scale the entire server, including memory and storage, which could be inefficient for larger workloads.

No Auto-pause/Resume: SQL Server runs continuously unless manually stopped, so you pay for the full server, regardless of usage.

2. Storage Architecture

Dedicated SQL Pools

Distributed Storage: Data is stored across multiple nodes in a distributed system. Azure Blob Storage is often used to store large amounts of data, which enables high scalability.

Columnar Storage: By default, Dedicated SQL Pools use columnar storage, which is optimized for analytics workloads. It compresses data and provides better performance for read-heavy operations like data warehousing.

Data Distribution: Data is distributed across the compute nodes using one of three distribution methods: hash-distributed, round-robin, or replicated tables. This is key to achieving the parallelism in MPP.

Scalable Storage: Storage scales separately from compute, which means you can store terabytes or petabytes of data without worrying about affecting compute performance.

Microsoft SQL Server RDBMS

Traditional Storage: SQL Server stores data in a traditional row-based format, optimized for transactional workloads (OLTP) rather than analytic workloads.

Single-instance Storage: Storage is tied to the server, and scaling storage usually requires adding more disks or expanding the existing storage attached to the instance.

No Native Data Distribution: Data is not distributed across multiple nodes as it is in Synapse. This makes SQL Server better suited for smaller, transactional workloads rather than massive, distributed datasets.

Scaling Storage: Scaling is typically done through vertical scaling (adding more storage to the same server), which has limitations in terms of hardware and cost efficiency.

3. Cost Model

Dedicated SQL Pools

Pay-per-Compute: You pay for the compute resources (DWUs) you provision. When paused, you only pay for storage.

Scalable Costs: Costs can vary based on how much compute power is needed and how often you run workloads.

Microsoft SQL Server RDBMS

Fixed Compute Costs: You generally pay for the server hosting the SQL Server instance, including compute and storage together.

Less Flexibility: Costs are more static and scaling requires more manual intervention.

For more information please refer to the below links:
https://learn.microsoft.com/en-us/answers/questions/976202/azure-sql-server-vs-synapse-dedicated-sql-pool
https://learn.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-overview-faq
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-data-warehouse/sql-data-warehouse-manage-compute-overview
https://learn.microsoft.com/en-us/azure/synapse-analytics/sql/overview-architecture
https://medium.com/@Lijitha/azure-sql-database-vs-azure-synapse-sql-dedicated-pool-4531c68a1722

Hope this helps. Do let us know if you have any further queries.

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