|By Jnan Dash||
|August 20, 2014 08:45 AM EDT||
Back when we were doing DB2 at IBM, there was an important older product called IMS which brought significant revenue. With another database product coming (based on relational technology), IBM did not want any cannibalization of the existing revenue stream. Hence we coined the phrase “dual database strategy” to justify the need for both DBMS products. In a similar vain, several vendors are concocting all kinds of terms and strategies to justify newer products under the banner of Big Data.
One such phrase is Fast Data. We all know the 3Vs associated with the term Big Data – volume, velocity and variety. It is the middle V (velocity) that says data is not static, but is changing fast, like stock market data, satellite feeds, even sensor data coming from smart meters or an aircraft engine. The question always has been how to deal with such type of changing data (as opposed to static data typical in most enterprise systems of record).
Recently I was listening to a talk by IBM and VoltDB where VoltDB tried to justify the world of “Fast Data” as co-existing with “Big Data” which is narrowed to static data warehouse or “data lake” as IBM calls it. Again, they have chosen to pigeonhole Big Data into the world of HDFS, Netezza, Impala, and batch Map-Reduce. This way, they justify the phrase Fast Data as representing operational data that is changing fast. They call VoltDB as “the fast, operational database” implying every other database solution as slow. Incumbents like IBM, Oracle, and SAP have introduced in-memory options for speed and even NoSQL databases can process very fast reads on distributed clusters.
VoltDB folks also tried to show how the two worlds (Fast Data and their version of Big Data) will coexist. The Fast Data side will ingest and interact on streams of inbound data, do real time data analysis and export to the data warehouse. They bragged about the performance benchmark of 1m tps on a 3-node cluster scaling to 2.4m on a 12-node system running in the SoftLayer cloud (owned by IBM). They also said that this solution is much faster than Amazon’s AWS cloud. The comparison is not apple-to-apple as the SoftLayer deployment is on bare metal compared to the AWS stack of software.
I wish they call this simply – real-time data analytics, as it is mostly read type transactions and not confuse with update-heavy workloads. We will wait and see how enterprises adopt this VoltDB-SoftLayer solution in addition to their existing OLTP solutions.
Big Data Expo's giant Silicon View billboard is viewed by more than 1.3 million motorists per week.
Aug. 24, 2016 08:00 AM EDT Reads: 3,803
Aug. 24, 2016 08:00 AM EDT Reads: 1,789
Aug. 24, 2016 07:45 AM EDT Reads: 2,045
Aug. 24, 2016 07:15 AM EDT Reads: 1,634
Aug. 24, 2016 07:15 AM EDT Reads: 1,774
Aug. 24, 2016 07:00 AM EDT Reads: 1,721
Aug. 24, 2016 05:00 AM EDT Reads: 2,045
Aug. 24, 2016 04:30 AM EDT Reads: 2,163
Aug. 24, 2016 03:30 AM EDT Reads: 2,903
Aug. 24, 2016 02:00 AM EDT Reads: 1,388
Aug. 24, 2016 01:45 AM EDT Reads: 1,254
Aug. 24, 2016 01:45 AM EDT Reads: 1,853
Aug. 24, 2016 01:00 AM EDT Reads: 1,577
Aug. 24, 2016 12:45 AM EDT Reads: 2,052
Aug. 24, 2016 12:30 AM EDT Reads: 1,853