Controlling cost with Azure Synapse Serverless Pools

Curious how to save cost when using Azure Synapse Serverless Pools? Any way to minimize the amount of data you are processing? Patrick shows you how and has a script for you!

Cost management for serverless SQL pool in Azure Synapse Analytics

Controlling cost with Azure Synapse Serverless Pools is an important part of managing any Azure Synapse deployment. Azure Synapse Serverless Pools are a cost-efficient way to store data and run analytics queries in a low-cost environment. 

This article discusses how to estimate and manage costs for serverless SQL pool in Azure Synapse Analytics. Costs are determined by the amount of data processed during a query, which includes data read from storage, data in intermediate results, and data written to storage. The process of reading files from storage is optimized through prefetching and an optimized CSV parser.

The serverless SQL pool query optimizer uses statistics for optimal query execution plans. Statistics can be created manually or automatically by the serverless SQL pool. The amount of data processed is rounded up to the nearest MB per query, with a minimum of 10 MB per query.

Costs for serverless SQL pool in Azure Synapse Analytics are only a part of your Azure bill, which includes all the Azure services and resources used in your subscription. To manage costs effectively, estimate the data processed before issuing a query and use the cost control feature to set a budget.

Cost management for serverless SQL pool - Azure Synapse Analytics | Microsoft Learn

What is Azure Synapse Analytics?

Azure Synapse Analytics is a cloud-based, integrated analytics service provided by Microsoft that brings together big data and data warehousing. It aims to help organizations transform their raw data into actionable insights by providing a unified workspace for data preparation, data management, and data analysis.

Azure Synapse Analytics offers several key features, including:

1. Integration with Azure Data Factory, Azure Machine Learning, and Power BI, allowing users to build end-to-end data processing, machine learning, and visualization workflows.
2. Support for both on-demand and provisioned resources, enabling users to optimize costs and performance based on their specific needs.
3. A serverless SQL pool, allowing users to run complex queries on large datasets without the need to manage infrastructure or provision resources.
4. Built-in integration with Azure Active Directory for secure, role-based access control to data and resources.
5. High-performance data warehousing capabilities, including columnar storage, partitioning, indexing, and caching for fast query execution.
6. Support for a variety of data formats and data sources, such as relational databases, NoSQL databases, and file-based storage.
7. Flexible development options with support for popular languages like Python, Scala, and .NET, as well as integration with popular data processing frameworks like Apache Spark and Hadoop.

By offering a scalable and flexible platform, Azure Synapse Analytics enables organizations to process, analyze, and visualize large volumes of data more easily, helping them make data-driven decisions and improve their overall business performance.