How do you eliminate data integration friction? 

In a highly competitive environment, the speed at which you make decisions can impact both top and bottom lines—meaning your enterprise needs to have all the data it needs as quickly as possible. 

But there are a number of steps in the process of connecting, transforming and processing data that threaten to slow you down. And because different areas of your business each have their own needs, it’s hard to do it all at scale.

That’s why most organizations end up with a hodgepodge of disconnected, siloed systems. Filling in these gaps often requires hand-coding and one-off implementations, but the shortage of data engineers is making it hard to keep up.

Some hire specialized teams to try and focus on specific platforms, but not only is this expensive, it reinforces the silos. And as the volume of data continues to increase, so does the risk of breaking these hand-coded integrations.
Because it’s all so complex, you have to use expensive talent to do what should be simple pipeline-building tasks. All of this creates inefficiencies that impede competitiveness, which can lead to customer churn and reduced revenue.

Every new tool you bring on board requires you to learn a new interface and a different way of doing things, which takes time. 

When your enterprise has a wide range of needs, you end up with a confusing array of tools and fragmented knowledge about how to use them, creating bottlenecks.

With StreamSets’ single interface you can learn once to create many different data integration pipelines. You can reduce the number of tools and skill sets required, not to mention maintenance and other related technology overhead, to drive out complexity. And you can enable business users—the people who understand the data best—to build data pipelines. You don’t need to start from scratch every time and you gain the added flexibility of creating custom code if needed. You’ll accelerate—and lower the cost of—developing and managing your data pipelines.

Graphical tools are easy to use, but when you can only drag and drop within the interface your teams have to replicate that “easy” work over and over again.

Not only does this laborious repetition take a lot of time, but it also creates the opportunity for error. With our Python SDK, you can templatize data pipelines for scale, easily creating hundreds of pipelines with just a few lines of code. Ultimately, you’ll be able to free up engineering resources to address additional activities and requests.

Graphical drag-and-drop tools make for quick work—until you have a unique requirement you need to address. 

At that point, your experienced data engineers have to write something from scratch for just that use case, holding up the implementation process.

Using StreamSets’ extensible drag-and-drop processors, you can simplify all transformations—even advanced or rare cases. With more than 50 pre-defined processors, you can meet 99% of your analytics requirements out of the box and give your “pro-level” users the ability to include custom code and deliver it as a new element that can be easily reused via drag-and-drop.

In the end, you’ll reduce project backlogs and enable a more productive workforce.

In today’s marketplace, being able to make data-backed decisions quickly is everything. But there are several speed bumps that you’ll need to account for if you want to scale up and integrate your data while avoiding silos.

With StreamSets you’ll be able to…

Drive out complexity through our single interface and enable business users to create data pipelines, templatize data pipelines for scale, and simplify all transformations with the help of our extensible drag-and-drop processors.

And that means you’ll…