In the previous post, you were introduced to the Control Flow tab through tasks. In this chapter, you’ll continue along those lines with an exploration of the Data Flow tab, which is where you will spend most of your time as an SSIS developer. The Data Flow Task is where the bulk of your data-heavy lifting occurs in SSIS. This chapter walks you through the transformations in the Data Flow Task, demonstrating how they can help you move and clean your data. You’ll notice a few components (the CDC ones) aren’t covered in this chapter. Those needed more coverage than this chapter had room for and are covered in Chapter 11.
The SSIS Data Flow is implemented as a logical pipeline, where data flows from one or more sources, through whatever transformations are needed to cleanse and reshape it for its new purpose, and into one or more destinations. The Data Flow does its work primarily in memory, which gives SSIS its strength, allowing the Data Flow to perform faster than any ELT type environment (in most cases) where the data is first loaded into a staging environment and then cleansed with a SQL statement.
One of the toughest concepts to understand for a new SSIS developer is the difference between the Control Flow and the Data Flow tabs. Chapter 2 explains this further, but just to restate a piece of that concept, the Control Flow tab controls the workflow of the package and the order in which each task will execute. Each task in the Control Flow has a user interface to configure the task, with the exception of the Data Flow Task. The Data Flow Task is configured in the Data Flow tab. Once you drag a Data Flow Task onto the Control Flow tab and double-click it to configure it, you’re immediately taken to the Data Flow tab.
The Data Flow is made up of three components that are discussed in this chapter: sources, transformations (also known as transforms), and destinations. These three components make up the fundamentals of ETL. Sources extract data out of flat files, OLE DB databases, and other locations; transformations process the data once it has been pulled out; and destinations write the data to its final location.
Much of this ETL processing is done in memory, which is what gives SSIS its speed. It is much faster to apply business rules to your data in memory using a transformation than to have to constantly update a staging table. Because of this, though, your SSIS server will potentially need a large amount of memory, depending on the size of the file you are processing.
Data flows out of a source in-memory buffers that are 10 megabytes in size or 10,000 rows (whichever comes first) by default. As the first transformation is working on those 10,000 rows, the next buffer of 10,000 rows is being processed at the source. This architecture limits the consumption of memory by SSIS and, in most cases, means that if you had 5 transforms dragged over, 50,000 rows will be worked on at the same time in memory. This can change only if you have asynchronous components like the Aggregate or Sort Transforms, which cause a full block of the pipeline.
Data viewers are a very important feature in SSIS for debugging your Data Flow pipeline. They enable you to view data at points in time at runtime. If you place a data viewer before and after the Aggregate Transformation, for example, you can see the data flowing into the transformation at runtime and what it looks like after the transformation happens. Once you deploy your package and run it on the server as a job or with the service, the data viewers do not show because they are only a debug feature within SQL Server Data Tools (SSDT).
To place a data viewer in your pipeline, right-click one of the paths (red or blue arrows leaving a transformation or source) and select Enable Data Viewer.
Once you run the package, you’ll see the data viewers open and populate with data when the package gets to that path in the pipeline that it’s attached to. The package will not proceed until you click the green play button (>). You can also copy the data into a viewer like Excel or Notepad for further investigation by clicking Copy Data. The data viewer displays up to 10,000 rows by default, so you may have to click the > button multiple times in order to go through all the data.
After adding more and more data viewers, you may want to remove them eventually to speed up your development execution. You can remove them by right-clicking the path that has the data viewer and selecting Disable Data Viewer.
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Yamuna Karumuri is a content writer at Mindmajix.com. Her passion lies in writing articles on IT platforms including Machine learning, PowerShell, DevOps, Data Science, Artificial Intelligence, Selenium, MSBI, and so on. You can connect with her via LinkedIn.
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