HPCC is the big data analysis tool from LexisNexis. They have proprietary add-ons as well. They've got a meetup and have spoken at local users' groups. I don't see any upcoming meetups, but keep an eye on them.
This was the note from Trish, their program manager:
Hello, I am the Program Manager for the HPCC Systems open source project at LexisNexis. HPCC Systems (www.hpccsystems.com) from LexisNexis Risk Solutions offers a proven, data-intensive supercomputing platform designed for the enterprise to process and solve Big Data analytical problems. As an alternative to legacy technology, HPCC Systems offers a consistent data-centric programming language, two processing platforms and a single, complete end-to-end architecture for efficient processing. And it is open source! ... I'm happy to provide whitepapers, articles or any other information needed.
Upcoming HPCC Meetups
The Download: Community Tech Talks Episode 5
Big Data Processing & Analytics - HPCC Systems (LexisNexis)
The HPCC Systems Download - Community Tech Talks!
We continue this workshop series specifically designed for the community by the community to share knowledge, spark innovation and further build and link the relationships within our HPCC Systems community. Each series will feature 20 minute talks from 3-4 speakers.
IMPORTANT: Please register at the Webinar link here.
Episode 5 is scheduled for May 25 at 11am ET.
Featured speakers include:
Jeff Bradshaw, CTO, Adaptris
Interlok Deep Dive
Interlok is a powerful integration framework from Adaptris designed to help architects rapidly connect different applications, data stores and communications protocols using pre-built components. It facilitates real-time data ingestion and flexible stream processing. In this talk, I will explain how Interlok is used within the HPCC Systems platform, specifically the Thor component, and developing entity models for delivering data insights.
Jeff Bradshaw is the founder of Adaptris and Group CTO of Adaptris/F4F/DBT within Reed Business Information. He has spent his career integrating data wherever it resides and in-flight across a number of industries including Agriculture, Airlines, Telecommunications, Healthcare, Government and Finance.
Jeff has worked with and contributed to a number of international standards bodies and continues to work with large enterprises to help them extract value from their data silos and share data seamlessly with their trading partners to achieve business benefit. For the last few years Jeff has been focusing on Big Data and how to gather that across a wide range of sources to help gain insight into the agri-food supply chain.
Jon Burger, Sr Architect, LexisNexis Risk Solutions
Hive360, Cloud Ported HPCC Systems Platform
HPCC Systems is excited to announce the creation of the Hive360 & Swarm360 stacks. Hive360 and its companion Swarm360 are a set of AWS cloud formation scripts designed to easily create a scalable, self-configuring, self-healing, on-demand HPCC platform within an existing AWS VPC. Taking advantage of native AWS services such as auto-scaling groups, EFS, cloud watch, cloud formation and multiple AZs allows a regular user with little or no experience with HPCC to create a dynamic, production ready big data processing platform that can easily be scaled for future growth. This talk will introduce you to Hive360 and its components, give a brief demonstration of the process and answer any questions you have about this technology.
Jon Burger is LexisNexis Risk’s head infrastructure architect with 20+ years in information technology and over 15 years’ experience with the HPCC platform. He has worked in a variety of roles within technology including Director of Technology, Director of HPCC, Engineering in Network, Linux and Microsoft. He currently works out of the Boca Raton office and is the father to two teenage boys. Hive360 was created by him in an effort to aid in AWS deployments for LexisNexis Risk products.
Rodrigo Pastrana, Software Architect, LexisNexis Risk Solutions
SQL on HPCC Systems
HPCC Systems has the powerful ECL language, but what if you want to execute SQL queries on HPCC based data? Or what if you want to integrate HPCC Systems into your favorite business intelligence product? HPCC Systems provides an SQL interface into its data files and published Roxie queries called WsSQL. As its name implies, this functionality is provided as a web service, which allows interactive and/or programmatic SQL based access to HPCC Systems.
Rodrigo is an Architect with the HPCC systems supercomputer focusing in platform integration and plug-in development. He has been a member of the HPCC core technology team for over five years and a member of the LexisNexis team for seven. Rodrigo is the principle developer of WsSQL, the HPCC JDBC connector, the HPCC Java APIs library and tools, and the Dynamic ESDL component. He has more than fifteen years of experience in design, research and development of state of the art technology including IBM’s embedded text-to-speech and voice recognition products, Eclipse’s device development environment. Rodrigo holds an MS and BS in Computer Engineering from the University of Florida and during his professional career has filed more than ten patent disclosures through the USPTO.
Bob Foreman, Senior Software Engineer, HPCC Systems, LexisNexis Risk Solutions
ECL Tip of the Month
This session will showcase an “ECL Tip of the Month”, presented by one of our ECL instructors, Bob Foreman. The tip will usually be something interesting that was posted on our HPCC Systems Support Forums, or a cool teaching example found in one of our many ECL classes.
Submit a talk for an upcoming episode!
Have a new success story to share?
Want to pitch a new use case?
Have a new HPCC Systems application you want to demo?
Want to share some helpful ECL tips and code sample?
Have a new suggestion for the roadmap?
It’s easy! All you need to do is submit a talk title and brief abstract to email@example.com. If chosen, you will be asked to present remotely for an upcoming 20 minute tech talk.
Alpharetta, GA - USA
Thursday, May 25 at 11:00 AM