Users can now analyze big data with speed-of-thought performance and high availability
TDWI World - Orlando, Fla. — Octobre 31, 2011 —
Pentaho Corporation, the business analytics company providing power for technologists and rapid insight for users, today announced the latest release of Pentaho Business Analytics including major improvements to data analysis performance, scalability and reliability with support for cloud-scale distributed in-memory caching systems, new performance tuning aids, and support for analysis of more big data sources. These new capabilities are available today in the latest release of Pentaho Business Analytics, with the new product name representing Pentaho’s comprehensive and integrated business intelligence, data integration, data mining and predictive analytics capabilities.
This release provides the benefits of an in-memory solution without the limitations of an in-memory only architecture. Now, business users experience in-memory performance while IT gets a sound, scalable and manageable analytics platforms built on proven data warehousing and BI best practices. New features include:
In-memory analytics– Pentaho’s data analysis capability now supports Infinispan/JBoss Enterprise Data Grid and Memcached, with the option of extending to other in-memory cache systems. Infinispan and Memcached can cache terabytes of data distributed across multiple nodes, as well as maintain in-memory copies of data across nodes for high availability and failover. Using these in-memory caching systems results in more insight through predictable speed-of-thought access to vast in-memory datasets.
In-memory aggregation– granular data can now be rolled-up to higher-level summaries entirely in-memory, reducing the need to send new queries to the database, resulting in even faster performance for a wide range of analytic queries.
New analytic data sources– adding to Pentaho’s unmatched list of native big data platform support, native SQL generation is now supported for the EMC Greenplum Database and the Apache Hive data warehouse system for Hadoop. This optimizes interactive data exploration performance when using Greenplum, and for Hive this makes it possible to design analytic reports offline then schedule them for background execution.