Azure Synapse Analytics: Choosing Between Serverless and Dedicated SQL Pools for Optimal Data Management

Azure Synapse Analytics is a powerful tool that combines big data and data warehousing. It has two important features: a Serverless SQL pool and a Dedicated SQL pool. Each feature has specific uses, helping organizations manage different data tasks effectively.

Serverless SQL Pool:

It allows you to query files in your Azure Storage accounts. It doesn’t have local storage or ingestion capabilities. Billing for the Serverless SQL pool is based on the amount of data processed to run the query.
Every workspace comes with a pre-configured Serverless SQL pool called Built-In.

Dedicated SQL Pool:

The SQL pool in Azure Synapse Analytics was previously known as Azure SQL Data Warehouse. In Synapse, a dedicated SQL pool provides a set of allocated analytical resources.

This Dedicated SQL pool is a big data solution that stores data in relational tables using columnar storage, which enhances query performance and reduces storage costs. The power of a dedicated SQL server is measured in Data warehousing units (DWU). Once data is stored in the dedicated SQL pool, it can be used for large-scale analytics.

Difference between Serverless SQL Pool and Dedicated SQL pool:

DWU( Data Warehousing unit): DWUs are used to scale the performance of your Dedicated SQL Pool. By increasing the DWUs, you allocate more compute resources, which can lead to faster query performance and more concurrent operations.

  • The cost of a Dedicated SQL Pool is directly tied to the DWUs. Higher DWUs mean higher costs, but they also mean better performance.
  • You can scale the DWUs up or down depending on your performance needs and budget, making it flexible to manage costs.

Use Case of Serverless SQL Pool:

Steps:
1.
Uploaded the data in Azure Data Lake storage gen2 > demo > employees.csv;

2. In Develop we create an SQL script and query the employee_sample.csv file using OPENROWSET()
OPENROWSET():- It allows you to access files in Azure storage and returns the content as rows.

PARSER version 2.0:- In Azure Synapse Analytics it is an improved feature that enhances query performance and compatibility. This version also provides better error messages and debugging capabilities, helping users to troubleshoot issues more efficiently.

3. To Find the total_sal for each department;

4. The Data can be visualized in Synapse Studio by switching from Table to the Chart view.
Below is the result using a pie chart that shows the total salary by each department;

Uses Case of Dedicated SQL Pool:

Steps:
1. To create a Dedicated SQL Pool:- 
Click the Manage Hub — -> SQL pools — -> New

While creating the Dedicated SQL Pool we specify the name and choose the performance level

DWU (Data Warehouse Units):

  • DWU is the measure of compute resources available to your dedicated SQL pool.

DW100c:

  • The “DW100c” designation specifies a configuration of 100 Data Warehouse Units.
  • The “c” in DW100c stands for “compute optimized.” This means it is designed to balance computing power and cost-efficiently, efficiently handling data analysis tasks.

2. Uploaded the NYCTripsmall.parquet within Data Hub — -> ADLS Gen 2 — → Demo folder

The code first will check whether the table is present in the database (or) It will create a new table as we mentioned with the column name and its datatype. Once the table is created copy the entire data into the table “dbo.NYCTaxiTripSmall” from the parquet file mentioned as the URL

3. Using the query Select count(*) from dbo.NYCTaxiTripSmall we get a count of the records

4. Navigate to Data Hub — -> Workspace — -> Databases — -> Tables — ->
we can see the dbo.NYCTaxiTripSmall stored within Tables

5. We can write our query to find the Total trip distance and Average Trip Distance

6. Results represented in Chart:

Conclusion:

Azure Synapse Analytics has two important features — Serverless SQL pool and Dedicated SQL pool — designed for different data management needs. The Serverless SQL pool lets you query data from Azure Storage without needing dedicated infrastructure. This makes it a cost-effective option for running quick, ad-hoc queries on unstructured data, like querying CSV files and showing results in a pie chart.

The Dedicated SQL pool, on the other hand, offers powerful and scalable compute resources called Data Warehousing Units (DWUs). This feature is ideal for large-scale data analytics. It stores data in tables using columnar storage, which helps boost query performance and reduce storage costs. In the NYC taxi trip example, it is used to load parquet files, create structured tables, and run detailed analytical queries.

Both pools have different strengths. Serverless SQL pools are good for flexibility and saving costs. Dedicated SQL pools are better for handling big, structured data with high-performance needs. Choosing between them depends on the size of your data and the complexity of your queries.

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Author: Sharan Kumar

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