Skip to main content

HPCC Systems blog contributors are engineers and data scientists who for years have enabled LexisNexis customers to use big data to fulfill critical missions, gain competitive advantage, or unearth new discoveries. Check this blog regularly for insights into how HPCC Systems technology can put big data to work for your own organization.

Andrew Farrell on 11/11/2014

I have always wanted to say I was working on big data projects and in my own time I have dabbled with frameworks such as Hadoop on Amazon EC2 but could not make the case for merging my day job with the big data aspiration.

Flavio Villanustre on 07/14/2014

5.0 has been released! Right on time for the festivities celebrating the third anniversary of our Open Source HPCC Systems platform!

Flavio Villanustre on 06/26/2014

On May 1, the report “Big Data: Preserving Values, Seizing Opportunities” was released by the Executive Office of the President in response to a directive from President Obama to examine the impact of big data technology on society at large.

David Bayliss on 05/14/2014

If you are familiar with other graph systems and have you not read any of my previous blogs please do not read this one. If you do you will rapidly convince yourself that for some inexplicable reason KEL is far more complex than all the other systems.

David Bayliss on 04/21/2014

I once opened a meeting of extremely senior folks all of whom specialized in large scale graph analytics by stating: “There is no such thing as an interesting large scale graph problem; but there are lots of huge graphs containing interesting small problems that we can’t find.” My statement was clearly hyperbolic and exceptions do exist although I would contend it is rather closer to the truth than most people would like to admit.

David Bayliss on 03/27/2014

If you have digested my blog on The Wayward Chicken then as you gaze out upon GraphLand you should no longer be thinking of a land of clearings connect via a maze of pathways. Rather we have a modern industrial landscape; every node is a factory with a rail link to the neighboring factories. Raw goods (data) regularly travel towards each node to be processed into a more refined good which may in turn become the raw material for a neighboring factory.

Gavin Halliday on 03/24/2014

Different Types of Joins

Matching records from multiple data sources is one of the fundamental operations you need to process data. As you would expect ECL makes it easy – but the number of options can be bewildering. The following aims to guide you through the different options, and explain when they are appropriate.

David Bayliss on 03/14/2014

The other evening I was distracted from a lengthy debug session by a commotion coming from the chicken coop. I raced out the door fearful that some local predator had decided to attempt to steal some dinner. I discovered no such thing; rather ‘the girls’ were all agitating to get out of the coop. In my concentration I had forgotten that it was past the time they are normally allowed out for their daily forage.

Richard Taylor on 03/13/2014

I get fairly frequent emails asking, "How can I ... (do something) in ECL?" I've saved the example code from many of these questions, so now I'll begin sharing them through this blog.

If anybody has this type of question they'd like me to answer (in the blog), please just send me an email to richard.taylor@lexisnexisrisk.com and I will do my best to come up with an ECL example demonstrating the solution.

David Bayliss on 02/26/2014

Well over a decade ago, I had the privilege of being one of the first programmers to use ECL. A new language at a new level of abstraction allowed us to think about data absent of process. We were no longer working in the realm of time and method, but in the realm of data. We wanted a name for this place that our minds could now wander; so we called it ‘Dataland’.