Matching Synapse dedicated SQL pool vs Spark pool

Question

Hi,

I'd like to know the differencies between Synapse dedicated SQl pool and Spark pool, in terms of pros and cons, when it is better to use a Synapse pool and when it is better to use a Spark pool, when it is better to use both.

Any suggests to me, please?

Thanks

Answer 1

Dedicated SQL Pool

Based on the documentation :

Dedicated SQL pool (formerly SQL DW) represents a collection of analytic resources that are provisioned when using Synapse SQL. The size of a dedicated SQL pool (formerly SQL DW) is determined by Data Warehousing Units (DWU).

Once your dedicated SQL pool is created, you can import big data with simple PolyBase T-SQL queries, and then use the power of the distributed query engine to run high-performance analytics. As you integrate and analyze the data, dedicated SQL pool (formerly SQL DW) will become the single version of truth your business can count on for faster and more robust insights.

Pros:

• Designed for high-performance, complex queries on large datasets, using MPP architecture
• Offers full support for T-SQL, making it familiar to users with SQL Server experience
• Integrates with other Azure services and many SQL-based tools and applications

Cons:

• Can be more expensive, especially for on-demand query processing, due to its provisioned resources model
• Less flexible in processing unstructured data compared to Spark pools

Best Use Cases:

• Large scale data warehousing solutions
• Complex query execution over large structured datasets
• Scenarios where business intelligence and reporting on structured data are highly important

Spark Pool

Based on the documentation :

A Spark pool is a set of metadata that defines the compute resource requirements and associated behavior characteristics when a Spark instance is instantiated. These characteristics include but aren't limited to name, number of nodes, node size, scaling behavior, and time to live. A Spark pool in itself doesn't consume any resources. There are no costs incurred with creating Spark pools. Charges are only incurred once a Spark job is executed on the target Spark pool and the Spark instance is instantiated on demand.

Pros:

• Highly versatile in processing both structured and unstructured data, ideal for big data scenarios
• Offers fast data processing capabilities, leveraging in-memory computation
• Easily scalable, allowing for dynamic adjustment of resources based on workload.

Cons:

• Requires familiarity with Spark and possibly Scala, Python, or R, which might present a learning curve for traditional SQL users
• Queries and jobs may require optimization for best performance, potentially increasing development time

Best Use Cases:

• Big data processing, including streaming and batch processing
• Machine learning model training and evaluation
• Processing and analyzing unstructured data, like logs, text, or multimedia

When to Use Both ?

In some scenarios :

• You can use Spark pools for complex ETL processes, especially when unstructured data is involved, and then move processed data into a Dedicated SQL Pool for querying and reporting
• You can use Spark pools for data exploration and machine learning model development, then use Dedicated SQL Pools to manage and query large volumes of structured data efficiently
• In cases where both structured and unstructured data are involved, Spark pools can handle the unstructured component, while Dedicated SQL Pools can deal with the structured part for more traditional data warehousing needs

More links :

https://www.youtube.com/watch?v=GiZKPAAOUuQ

https://lytix.be/synapse-dedicated-sql-pool-vs-serverless-sql-pool/

https://learn.microsoft.com/en-us/answers/questions/1242386/should-i-use-dedicated-sql-pool-or-serverless-sql?cid=kerryherger

https://www.linkedin.com/pulse/dedicated-vs-serverless-sql-azure-synapse-which-should-nikola-ilic/

Answer 2

@pmscorca

Thanks for reaching out to Microsoft Q&A

You're right, your understanding of the strengths and weaknesses of Synapse Dedicated SQL Pool and Spark Pool is spot on. Here's a breakdown addressing your specific points:

Cost Considerations:

• While Spark Pool offers potential cost savings due to auto-scaling and pay-per-use, it's not always a guarantee. Complex Spark jobs can consume significant resources, potentially negating the cost advantage.

Performance:

• Spark excels at processing massive datasets in parallel, making it faster for complex queries compared to T-SQL on a Dedicated SQL Pool, especially for large tables. However, for smaller datasets (like yours with 5-6 million rows), the overhead of setting up and running Spark jobs might negate the performance benefit.

Learning Curve & Administration:

• You're right, the learning curve for PySpark might be offset by the complexity of managing a Dedicated SQL Pool with DWUs, service levels, distribution, and indexing. Spark offers auto-scaling and auto-pausing, simplifying administration compared to manual scaling for Dedicated SQL Pools.

Complex Logic:

• PySpark can handle complex logic more efficiently than T-SQL with nested IF/CASE statements and cursors. Spark's functional programming paradigm often leads to cleaner and more scalable solutions for complex operations.

Synapse vs. Spark for Your Scenario:

Given your scenario with:

• Structured data (millions of rows)
• Preference for PySpark
• ETL/ELT with ADF

Here's why Synapse still holds value:

• Maintainability: For ingesting data from your on-premises Oracle source and maintaining structured data, Synapse offers a familiar SQL environment.
• Serverless SQL Pool: Consider using Synapse Serverless SQL Pool for cost-effective querying of your existing data. It scales automatically based on workload and eliminates infrastructure management.
• ADF Integration: You can leverage ADF for your ETL/ELT needs, as it integrates well with Synapse and offers global parameters.

Spark Pool as a Complimentary Tool:

While Spark might not be the most cost-effective solution for your current data volume, it can be a valuable addition for:

• Future Growth: If you anticipate significant data growth, Spark will be ready to handle the scale.
• Complex Transformations: For complex data transformations or advanced analytics needs, Spark's distributed processing power will shine.

Understanding the Architecture:

• Synapse Dedicated SQL Pool stores data on persistent disks, similar to a traditional data warehouse.
• Spark Pools leverage a combination of in-memory processing and disk storage for efficient data shuffling and intermediate results.

In Conclusion:

For your current data size and focus on structured data maintenance, Synapse with a Serverless SQL Pool and ADF can be a good fit. However, consider incorporating a Spark Pool for future scalability and complex transformations as your data needs evolve.

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

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