StreamSets Data Integration Blog

Where change is welcome.

Calling External Java Code from Script Evaluators

By December 21, 2016

groovy logoWhen 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.

Continuous Data Integration with StreamSets Data Collector and Spark Streaming on Databricks

By December 19, 2016

Databricks LogoI’m frequently asked, ‘How does StreamSets Data Collector integrate with Spark Streaming? How about on Databricks?’. In this blog entry, I’ll explain how to use Data Collector to ingest data into a Spark Streaming app running on Databricks, but the principles apply to Spark apps running anywhere. This is one solution for continuous data integration that can be used in cloud data platforms.

Creating a Custom Origin for StreamSets Data Collector

By December 12, 2016

Git Commit Log RecordSince 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.

Running Apache Spark Code in StreamSets Data Collector

By December 8, 2016

Spark LogoNew in StreamSets Data Collector (SDC) 2.2.0.0 is the Spark Evaluator, a processor stage that allows you to run an Apache Spark application, termed a Spark Transformer, as part of an SDC pipeline. With the Spark Evaluator, you can build a pipeline to ingest data from any supported origin, apply transformations, such as filtering and lookups, using existing SDC processor stages, and have the Spark Evaluator hand off the data to your Java or Scala code as a Spark Resilient Distributed Dataset (RDD). Your Spark Transformer can then operate on the records, creating an output RDD, which is passed through the remainder of the pipeline to any supported destination.

Announcing Data Collector ver 2.2.0.0

By December 2, 2016

And here it is folks, the last release of 2016 – StreamSets Data Collector version 2.2.0.0. We’ve put in a host of important new features and resolved 120+ bugs.

We’re gearing up for a solid roadmap in 2017, enabling exciting new use cases and bringing in some great contributions from customers and our community.

Upgrading From Apache Flume to StreamSets Data Collector

By December 1, 2016

Apache Flume “is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data”. The typical use case is collecting log data and pushing it to a destination such as the Hadoop Distributed File System. In this blog entry we’ll look at a couple of Flume use cases, and see how they can be implemented with an Apache Flume alternative, such as StreamSets Data Collector.

More Than One Third of the Fortune 100 Have Downloaded StreamSets Data Collector

By November 10, 2016

It’s been a little over a year (9/24/15) since we launched StreamSets Data Collector as an open source project. For those of you unfamiliar with the product, it’s any-to-any big data ingestion software through which you can build and place into production complex batch and streaming pipelines using built-in processors for all sorts of data transformations. 

We’re thrilled to announce that as of last month StreamSets Data Collector had been downloaded by over ⅓ of the Fortune 100! That’s several dozen of the largest companies in the U.S. And downloads of this award-winning software have been accelerating, with over 500% growth in the quarter ending in October versus the previous quarter.

Contributing to the StreamSets Data Collector Community

By October 25, 2016

StreamSets MeetupAs you likely already know, StreamSets Data Collector (SDC) is open source, made available via the Apache 2.0 license. The entire source code for the product is hosted in a GitHub project and the binaries are always available for download.

As well as being part of our engineering culture, open source gives us a number of business advantages. Prospective users can freely download, install, evaluate and even put SDC into production, customers have access to the source code without a costly escrow process and, perhaps most importantly, our users can contribute fixes and enhancements to improve the product for the benefit of the whole community. In this post, I’d like to acknowledge some of those contributions, and invite you to contribute, too.

The Challenge of Fetching Data for Apache Spot (incubating)

By October 19, 2016

Reposted from the Cloudera Vision blog.

What do Sony, Target and the Democratic Party have in common?

Besides being well-respected brands, they’ve all been subject to some very public and embarrassing hacks over the past 24 months. Because cybercrime is no longer driven by angst-ridden teenagers but rather professional criminal organizations and state-sponsored hacker groups, the halcyon days of looking for a threat signatures are well behind us.

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