ARMONK, N.Y., May 22, 2013 /PRNewswire/ -- IBM (NYSE: IBM) today announced new IBM SmartCloud capabilities, including DB2 with BLU Acceleration that can result in as much as 25 times faster reporting and analytics, and SmartCloud availability for SAP's in-memory database, the SAP High Performance Analytics Appliance (SAP HANA).
New BLU Acceleration technology from IBM Research and Development labs adds innovative Dynamic In-memory analytics to DB2 database software on IBM SmartCloud to help companies and governments tackle Big Data by making it dramatically simpler, faster and more economical to analyze massive amounts of data. For example, Cognos Business Intelligence with Dynamic Cubes powered by DB2 with BLU acceleration delivers speed of thought analytics – with 18 times faster data loading and 14 times faster answers.
IBM also announced that its open IBM SmartCloud, built on decades of hosting experience, is now available for SAP HANA. The new offering includes a physical SAP HANA Appliance on IBM SmartCloud for SAP Applications, hosted on SmartCloud Enterprise+. In addition, a virtual SAP HANA Appliance is now available on IBM SmartCloud Enterprise (SCE) for early stage development and test."Clients are looking to leverage big data and analytics at the speed of business," said Jim Comfort, general manager of IBM SmartCloud Services. "Today's news shows how IBM is delivering mission-critical cloud services to provide essential analytics innovations to business users at the point of impact." With IBM's DB2 10.5 with BLU Acceleration, typical queries in an analytics workload have been shown to be more than 1,000 times faster than other leading databases. Innovations in BLU Acceleration include:
- "Dynamic in-memory" columnar processing providing not only dramatic analytics performance – up to 25 times faster -– but also the ability to scale for expanding Big Data needs without the limitations imposed by traditional in-memory systems.
- "Load and go" simplicity which allows clients access to blazing-fast analytics transparently to their applications, without the need to develop a separate layer of data modeling.
- "Parallel vector processing" for high-performance data analysis in parallel across different processors.
- "Actionable compression," providing as much as 10 times storage space savings where data no longer has to be decompressed to be analyzed.