Aster Data has launched version 4.0 of its massively parallel data warehouse designed for companies that need real-time analysis of huge data volumes.
Traditional architectures will only support analysis of samples of data, where as organisations with ‘big data’ requirements such as real-time fraud detection, trading surveillance or customer calling patterns require analysis of all the data.
The platform takes a different approach from traditional data warehouses, DBMS and data analytics solutions by housing data and applications together in one system, fully parallelizing both. This eradicates the need for movements of massive amounts of data and the problems with latency and restricted access that creates.
“Data volume is an issue, and as it grows, applications have to work out how to use that data, For example, fraud applications need to analyse lots of data before they can decide whether something is fraudulent or not,” said Mayank Bawa, CEO of Aster Data.
Although data and application processing are housed together, Aster’s massively parallel data-application server 4.0, they operate independently. Separating the two enables a wide range of applications to be pushed down into the system and these applications do not need to be rewritten in any way.
Customers include UK firm Full Tilt Poker. Prior to adopting the Aster approach, fraud analysis took the 90 minutes and could only be run once a week. Now the firm can complete the analysis in 90 seconds and run it every 15 minutes, giving it the ability to catch fraud as it happens rather than after the fact. Transaction speeds and volumes have increased from 1,200 per second to 140,000 per second.