Posts by CyberH

Enrico, very informative approach when tackling Big Data. With the explosion of big data, companies are faced with data challenges in three different areas. First, you know the type of results you want from your data but it’s computationally difficult to obtain. Second, you know the questions to ask but struggle with the answers and need to do data mining to help find those answers. And third is in the area of data exploration where you need to reveal the unknowns and look through the data for patterns and hidden relationships. The open source HPCC Systems big data processing platform can help companies with these challenges by deriving insights from massive data sets quickly and simply. Designed by data scientists, it is a complete integrated solution from data ingestion and data processing to data delivery. Their built-in Machine Learning Library and Matrix processing algorithms can assist with business intelligence and predictive analytics. More at http://hpccsystems.com

Chris, very nice article on Big Data. When considering a big data strategy, I think it's worth mentioning HPCC Systems from LexisNexis. Designed by data scientists, HPCC Systems is an open source data-intensive supercomputing platform to process and solve Big Data analytical problems and can help companies derive actionable insights from their data.

HPCC Systems provides proven solutions to handle what are now called Big Data problems, and have been doing so for more than a decade. The main advantages over other alternatives are the real-time delivery of data queries and the extremely powerful ECL language programming model. More info at http://hpccsystems.com

Big Data Solution

Alexandre, good article. It is worth mentioning the HPCC Systems open source offering which provides a single platform that is easy to install, manage and code. Their built-in analytics libraries for Machine Learning and BI integration tools make it easy for users to analyze Big Data using a complete integrated solution from data ingestion and data processing to data delivery. Their free online training courses allow for students, academia and other developers to quickly get started. More at http://hpccsystems.com.

Very insightful article Jack. One other open source technology to mention is HPCC Systems from LexisNexis, a data-intensive supercomputing platform for processing and solving big data analytical problems. Their open source Machine Learning Library and Matrix processing algorithms assist data scientists and developers with business intelligence and predictive analytics. Its integration with Hadoop, R and Pentaho extends further capabilities providing a complete solution for data ingestion, processing and delivery. In fact, a webhdfs implementation, (web based API provided by Hadoop) was recently released.

More at http://hpccsystems.com/h2h

Matt, I agree we are seeing more efforts from Big Data companies trying to make Hadoop a mature and complete solution. As an alternative to Hadoop, LexisNexis has open sourced the HPCC Systems platform that is a complete enterprise-ready solution. Designed by data scientists, it provides for a single architecture, a consistent data-centric programming language (ECL), and two data processing clusters. Their built-in analytics libraries for Machine Learning and BI integration provide a complete integrated solution from data ingestion and data processing to data delivery. This all in one platform means only one thing to support and from a significant lower number of resources. In contrast, the complexity of the Hadoop ecosystem requires a huge investment in technology and resources up front and throughout.

Re

Great insight Tim. I heard MapR is doing quite a bit in the Hadoop space. One Hadoop alternative worth mentioning is HPCC Systems – a mature platform with its main differentiator being the powerful ECL language programming model. ECL is the native language behind the Thor and Roxie components which provide for data transformation “chewing” and linking with real-time delivery of data queries. An advantage over Hadoop as it requires less nodes and fewer programmers. View more at http://hpccsystems.com