SSIS Scale Out vs Traditional ETL Strategies
By Tom Nonmacher
In the rapidly evolving world of data management and analytics, it's essential to stay informed about the latest technologies and strategies. In this regard, SQL Server Integration Services (SSIS) has been a staple component in the Microsoft data platform for many years. With the release of SQL Server 2022, SSIS Scale Out has emerged as a promising alternative to traditional ETL strategies. In this blog post, we will compare SSIS Scale Out with traditional ETL strategies and discuss how it can be leveraged in conjunction with other cutting-edge technologies like Azure SQL, Microsoft Fabric, Delta Lake, OpenAI, and Databricks.
SSIS Scale Out is a distributed execution feature in SQL Server 2022 that enables you to run SSIS packages across multiple machines. With traditional ETL strategies, you would typically run your ETL workloads on a single server, which can cause performance bottlenecks due to CPU or memory constraints. However, with SSIS Scale Out, you can distribute your ETL workloads across several servers, thereby significantly improving performance and reliability.
Let's take a look at an example of how you might implement SSIS Scale Out. Suppose you have a large ETL workload that involves extracting data from an Azure SQL database, transforming it, and then loading it into a SQL Server 2022 database. Instead of running this workload on a single server, you can use the following T-SQL command to distribute it across multiple servers:
EXEC [SSISDB].[catalog].[create_execution] @package_name=N'MyPackage.dtsx',
@execution_id=@exec_id OUTPUT,
@folder_name=N'MyFolder',
@project_name=N'MyProject',
@use32bitruntime=False,
@reference_id=Null
EXEC [SSISDB].[catalog].[set_execution_parameter_value] @exec_id, @object_type=50,@parameter_name=N'SYNCHRONIZED',@parameter_value=1
EXEC [SSISDB].[catalog].[start_execution] @exec_id
One of the primary benefits of SSIS Scale Out is that it can be easily integrated with other modern data technologies. For instance, Microsoft Fabric is an excellent tool for managing and orchestrating your distributed systems, and it can be used in conjunction with SSIS Scale Out to ensure that your ETL workloads are executed efficiently and reliably across your distributed environment.
Delta Lake and Databricks can also play a crucial role in your SSIS Scale Out strategy. Delta Lake is an open-source storage layer that provides ACID transactions, scalable metadata handling, and unifies streaming and batch data processing. It can be used to store the output of your SSIS packages in a highly scalable and reliable manner. On the other hand, Databricks is a unified analytics platform that provides a collaborative environment for data scientists and engineers to work together. It can be used to analyze the output of your SSIS packages and generate valuable insights.
Lastly, OpenAI has recently introduced a SQL interface that allows you to interact with your data using natural language queries. This can be a game-changer for business users who are not familiar with SQL syntax. You can use this feature in conjunction with SSIS Scale Out to make your ETL processes more accessible and user-friendly.
In conclusion, SSIS Scale Out offers a powerful and flexible alternative to traditional ETL strategies. By leveraging cutting-edge technologies like Azure SQL, Microsoft Fabric, Delta Lake, OpenAI, and Databricks, you can build robust, scalable, and user-friendly ETL processes that meet the needs of your organization.