The SQL Server Integration Services SSIS Data Flow
To get to grips with SQL Server Integration Services (SSIS) there are a number of elements that need to be understood and ultimately mastered.
When you are wondering how to create your first SSIS package, the Data Flow task will be a key part of this process.
Here is an introductory unpicking of what Data Flow involves and why SQL servers remain one of the best business technology resources available in the digital era.
The basics SSIS Data Flow
The purpose of the Data Flow task within the context of Microsoft’s widely used SQL ecosystem is to ensure that information is not only capable of being transplanted from its point of origin to its destination but also that it can be adjusted in transit according to the needs of the user or administrator.
This task can be made up of multiple components, each of which will determine how information gets where it is going and what state it will be in when it arrives.
For example, components of the data flow task can be used to route the data as required, summarize it if necessary and ultimately load it cleanly.
Encompassed within the SSIS Toolbox, the Data Flow task sits after the Control Flow and will usually be set to execute once truncation has occurred.
Ordering operations correctly is one of the main ways to make sure that SSIS functions seamlessly and of course dealing with the data flow in an optimal way is therefore crucial.
It is worth drilling down into what the SSIS Data Flow task can offer in terms of impactful components in order to appreciate just how important it is in this context.
For example, with the Lookup component it is possible to make comparisons between different data sets, whether for the purposes of validation or perhaps to retrieve keys, amongst other uses.
The Derived Column component, meanwhile, is effective as a means of changing the types of data in transit, or making adjustments to string values.
The Sort component is easy to get to grips with, since it lets you organize a given subset of data that is encompassed by the task as you see fit. Then there is the Conditional Split, which can uncouple a single set of data and create multiple distinct sets at the destination.
In total there are three different categories of Data Flow task components, starting with the source, moving to the transformation and concluding with the destination.
In terms of managing complexity, you have a powerful set of tools at your disposal and it is possible to opt for a task that is very straightforward, or to build one which is far more advanced, consisting of a laundry list of transformations.
Sources & Destinations
We have already touched upon the idea that both the source of the data that you are migrating and the destination will be central to configuring the Data Flow task effectively, but it is also worth providing a few examples of what these two end points might be.
Popular file types tend to be the source for extraction, with everything from Microsoft Excel workbooks and XML files to simple flat files often harnessed in this context.
The idea is to use a source where data is kept in a relatively raw form and then, through the Data Flow task, make it more convenient to harness when it reaches its destination.
Destinations can be similarly multifaceted, and indeed it is possible to assign multiple designations as part of the Data Flow task if this makes sense for your needs.
It is even an option to set custom destinations for data to be written to, although to achieve this you will need to familiarize yourself with the ins and outs of creating a bespoke component to integrate with this task.
Transformations are where the magic happens, and have already been outlined above in terms of the types of components that can hold sway over data as it is handled by this SSIS task.
In addition, it is a good idea to gain an appreciation for how transformations operate in terms of their input and output options. For example, you can take a multitude of inputs and generate a single output, or vice versa.
Like sources and destinations, you can create custom transformation components from scratch if there is not a preexisting setup that is suitable.
This demonstrates the extent to which the Data Flow task can be orchestrated and micromanaged on a minuscule level, should you wish to.
Ultimately the Data Flow task is central to what makes SSIS impactful and in the hands of an experienced, skilled operator it is possible to use it to achieve great things and address issues with inefficiencies.