StreamSets Data Integration Blog

When you’re building a pipeline with StreamSets Data Collector (SDC), you can often implement the data transformations you require using a combination of ‘off-the-shelf’ processors. Sometimes, though, you need to write some code. The script evaluators included with SDC allow you to manipulate records in Groovy, JavaScript and Jython (an implementation of Python integrated with the Java platform). You can usually achieve your goal using built-in scripting functions, as in the credit card issuing network computation shown in the SDC tutorial, but, again, sometimes you need to go a little further. For example, a member of the StreamSets community Slack channel recently asked about computing SHA-3 digests in JavaScript. In this blog entry I’ll show you how to do just this from Groovy, JavaScript and Jython.

Since writing tutorials for creating custom destinations and processors for StreamSets Data Collector (SDC), I’ve been looking for a good use case for a custom origin tutorial. It’s been trickier than I expected, partly because the list of out of the box origins is so extensive, and partly because the HTTP Client origin can access most web service APIs, rendering a custom origin redundant. Then, last week, StreamSets software engineer Jeff Evans suggested Git. Creating a custom origin for StreamSets to read the Git commit log turned into the perfect tutorial.

Back in March, I wrote a tutorial showing how to create a custom destination for StreamSets Data Collector (SDC). Since then I’ve been looking for a good sample use case for a custom processor. It’s tricky to find one, since the set of out-of-the-box processors is pretty extensive now! In particular, the scripting processors make it easy to operate on records with Groovy, JavaScript or Jython, without needing to break out the Java compiler.

Looking at the Whole File data format, introduced last month in SDC 1.6.0.0, inspired me… Our latest tutorial, Creating a Custom StreamSets Processor, explains how to extract metadata tags from image files as they are ingested, adding them to records as fields.

Sandish Kumar, a Solutions Engineer at phData, builds and manages solutions for phData customers. In this article, reposted from the phData blog, he explains how to generate simulated NetFlow data, read it into StreamSets Data Collector via the UDP origin, then buffer it in Apache Kafka before sending it to Apache Kudu. A true big data enthusiast, Sandish spends his spare time working to understand Kudu internals.