ETL Pipeline using Microsoft SQL Server Integration Services

In this article, we will understand how to build an ETL (Extraction, Transformation, and Loading) pipeline using SQL Server Integration Services (SSIS). This involves extracting data from multiple sources, transforming them into useful data, and then loading it into a Data Warehouse or Data Lake.

We can work with data in any format, such as Excel files, flat files, XML files, database files, web API, etc.

Now, to develop the ETL pipeline in SSIS, we need to do the following steps.

1. Creating SSIS Projects
2. Creating SSIS Packages
3. Building Control Flow & Data Flow Task
4. Deploying SSIS Packages
5. Monitoring and Debugging SSIS Packages

Creating SSIS Projects

To Develop the ETL process in SSIS, the first step is to create an SSIS project in SQL Server Data Tools (SSDT) where we can create multiple packages and other task related to project. Below graphical representation below shows how to create a new SSIS project.

Creating SSIS Packages

Once the SSIS project is created, we can create multiple packages based on project/business needs and in each SSIS package, we can have multiple Control Flow and Data Flow tasks. Below is the graphical representation of what the SSIS Package looks like.

In the above screenshot, we can see the ETL_SSIS_Package has been created, which is where we can create Control Flow and Data Flow tasks.

Building Control Flow & Data Flow Task

Control flow task controls the flow of data and execution of tasks. For example, it can execute tasks in a defined order, conditionally, or in a loop, whereas the Data Flow Task moves data from source to destination and transforms/cleans/modify based on project needs. We can build one or more Data Flow Tasks within the SSIS package, and this can be implemented within the Control flow.

Now, we can see in the screenshot below that the source and destination connection is established inside the control flow.

Now, let’s understand the Data flow task in detail and go with each step involved in the process of creating the Data Flow Task.

Data Source

In this step, we can define the data source from where data is to be extracted. Let’s understand how to connect/integrate with the source system and add Flat File in the source connection manager.

Now, let’s see the screenshot below where we have added the Flat File source.

Data Transformation

In this step, we can perform data transformation, such as derived Columns, data conversion, aggregation, lookup, conditional spill, etc.

Now, let’s see the screenshot below, where we have performed the Lookup transformation.

Data Destination

In this step, we can define the Target where data is to be exported.

Now, let’s see the screenshot below where we have defined the Target OLE DB Destination.

Deploying SSIS Packages

Once we have created and validated the SSIS package, we can deploy it to the SQL Server.

The screenshot below shows we have two options: one is to hit on START Executing without debugging, and another one we can directly hit on start.

We can see in the screenshot below that the package was executed successfully.

Monitoring and Debugging SSIS Packages

Monitoring and debugging are a part of the SSIS package, which helps identify errors, optimize performance, and ensure smooth data flow. SSIS comes with several built-in debugging tools, for example, Data viewers that monitor Data flow; setting up Breakpoints helps when we want to observe how values of variables change at different stages and check if tasks are executing as expected, and Enable Logging for log entries (For example if FTP transfer fails, an error logged to a folder related to the transferred files).

Conclusion

We have gone through the steps involved in creating an ETL pipeline. Building an ETL pipeline with Microsoft SSIS is easier than it may seem. Using this tool, we create a powerful data-driven application to help businesses make better decisions. Hope you liked the article, please share your feedback/ suggestions in the comments section below.