From Microsoft, the Conditional Split transformation can route data rows to different outputs depending on the content of the data. The implementation of the Conditional Split transformation is similar to a CASE decision structure in a programming language. The transformation evaluates expressions, and based on the results, directs the data row to the specified output. This transformation also provides a default output, so that if a row matches no expression it is directed to the default output.

You can configure the Conditional Split transformation in the following ways:

• Provide an expression that evaluates to a Boolean for each condition you want the transformation to test.
• Specify the order in which the conditions are evaluated. Order is significant, because a row is sent to the output corresponding to the first condition that evaluates to true.
• Specify the default output for the transformation. The transformation requires that a default output be specified.

Let’s take a look at how this transformation might be used in the real world.

Open Visual Studio and drag a Data Flow task into the design pane. Open the Data Flow task and drag in an OLE DB Source task. For this post, I’m going to use the AdventureWorks2019 database and the HumanResources.vEmployeeDepartment view.

This view has some good data to play around with, but we’re going to focus on the Department and Start Date columns. Let’s pretend the bossman needs to see all of the Employees in the Quality Assurance (QA), Production and Sales Department in a separate database table. Bonus, he needs to see all of the Production employees split up into two more tables based on who started before and after Jan 1, 2010. All other employees can go into their own table. Got it? Great! That’s 5 total tables. QA=1, Production=2, Sales=1, Leftovers=1 Let’s go.

Back in Visual Studio, drag in a Conditional Split task and connect it to our OLE DB Source.

Open the Conditional Split task editor and you’ll see a few options (from left to right, top to bottom):

1. We can use columns and/or variables and parameters in our expressions that define how to split the data flow.
2. We can use functions such as Date/Time, NULL and String in our expressions that define how to split the data flow.
3. These are the conditions that define how to split the data flow. These need to be set in priority order; any rows that evaluate to true for one condition will not be available to the condition that follows.

Let’s start adding some conditions for our data. First, we’ll add a condition for all of our QA Department Employees. I’ll name the output “QA” and my condition is pretty simple whereas Department == “Quality Assurance”.

I’ll do the same for Production, Sales and Leftovers (everything else that doesn’t satisfy a condition). Since Leftovers is everything else we’ll just change the name of the Default Output name to identify it.

Let’s go ahead and add our destination tasks (except for Production since we need another condition) and link them to the appropriate condition. See below for QA as an example. When we drag our connector to our destination task we get prompted with an Input Output Selection box. Here is where we choose our Condition that will match up with our table. For the screenshot below, we’ll choose QA output for our QA destination.

Now that we have QA mapped, go ahead and map Sales and Leftovers.

Looks great!  QA, Sales and Leftovers are mapped successfully. Let’s take a look at adding another Conditional Split task for Production. Drag a Conditional Split task into the design pane and connect it to the current Conditional Split. It automatically maps to Production since it’s the only output left.

From our new Conditional split task, let’s open the editor and configure the date conditions for Production. We’ll leave the Default output name box as is since we shouldn’t have any leftover data from this split.

Now we can map the two new conditions to their appropriate destinations.

Cross fingers and hit Execute.

Yay, no red X! Let’s take a look at our SQL tables to make sure everything exported correctly.

Boom. Let’s go grab a bourbon!

From Microsoft, the Balanced Data Distributor (BDD) transformation takes advantage of concurrent processing capability of modern CPUs. It distributes buffers of incoming rows uniformly across outputs on separate threads. By using separate threads for each output path, the BDD component improves the performance of an SSIS package on multi-core or multi-processor machines.

The Balanced Data Distributor transformation helps improve performance of a package in a scenario that satisfies the following conditions:

1. There is large amount of data coming into the BDD transformation. If the data size is small and only one buffer can hold the data, there is no point in using the BDD transformation. If the data size is large and several buffers are required to hold the data, BDD can efficiently process buffers of data in parallel by using separate threads.
2. The data can be read faster than the rest of the data flow can process it. In this scenario, the transformations that are performed on the data run slowly compared to the rate at which data is coming. If the bottleneck is at the destination, the destination must be parallelizable though.
3. The data does not need to be ordered. For example, if the data needs to stay sorted, you should not split the data using the BDD transformation.

Let’s dive in and take a quick look at how the Balanced Data Distributor works.

Go ahead and open Visual Studio, it might take a minute to load.

We’re going to use AdventureWorks2019 database for this example. The SalesOrderDetail table has over 121k records so that’s a good candidate.

SELECT * FROM Sales.SalesOrderDetail

Now that we have data let’s go back over to Visual Studio and see if it’s still spinning.

Drag in a Data Flow task, double click to open and then let’s drag in an OLE DB Source and a Flat File Destination task.

I’m going to configure the source to point to my AdventureWorks2019 database and the destination to point to a .csv file on my local laptop.

Easy enough. Let’s go ahead and run this and see what happens.

We can see that our 121k rows were written to our CSV file and the CSV file ended up being 12.3 MB. That’s pretty large for an email file attachment and might crash your laptop even trying to open this file. BDD not only offers performance benefits by using multiple threads, but it can also break up large files into smaller ones. IMO, this is what makes this task great.

We need to get this file down to less than 4MB so we’ll need to break this up 4 times. With that said,  let’s add the BDD task between the source and destination tasks. There is nothing configurable *in* this task, however, there are some properties that may need to be tweaked. After adding the BDD task, we’ll need to add 3 more flat file destination tasks and 3 more flat file connection managers. End result should look something like this:

That’s really about it. Let’s fire it off and see the results of our flat files.

It worked! 121k rows were written across 4 flat files. Our flat file size is less than 4MB each and we can send these very easily via email. This is a very quick and easy way to split data between files, however, there is no order to these records so an ORDER BY is not helpful here. If the goal is to separate data by a category or condition then you’ll need to use a Conditional Split task which I wrote about in this post.