|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.
We're entering the post-smartphone era, where wearable gadgets from watches and fitness bands to glasses and health aids will power the next technological revolution. With mass adoption of wearable devices comes a new data ecosystem that must be protected. Wearables open new pathways that facilitate the tracking, sharing and storing of consumers’ personal health, location and daily activity data. Consumers have some idea of the data these devices capture, but most don’t realize how revealing and...
Jul. 25, 2016 06:00 PM EDT Reads: 2,070
"Software-defined storage is a big problem in this industry because so many people have different definitions as they see fit to use it," stated Peter McCallum, VP of Datacenter Solutions at FalconStor Software, in this SYS-CON.tv interview at 18th Cloud Expo, held June 7-9, 2016, at the Javits Center in New York City, NY.
Jul. 25, 2016 06:00 PM EDT Reads: 1,407
Unless your company can spend a lot of money on new technology, re-engineering your environment and hiring a comprehensive cybersecurity team, you will most likely move to the cloud or seek external service partnerships. In his session at 18th Cloud Expo, Darren Guccione, CEO of Keeper Security, revealed what you need to know when it comes to encryption in the cloud.
Jul. 25, 2016 06:00 PM EDT Reads: 2,392
Jul. 25, 2016 05:30 PM EDT Reads: 735
Jul. 25, 2016 04:38 PM EDT Reads: 152
Jul. 25, 2016 04:00 PM EDT Reads: 993
Jul. 25, 2016 03:45 PM EDT Reads: 943
Jul. 25, 2016 03:30 PM EDT Reads: 1,684
Jul. 25, 2016 03:15 PM EDT Reads: 415
Jul. 25, 2016 03:00 PM EDT Reads: 1,969
Jul. 25, 2016 02:45 PM EDT Reads: 859
Jul. 25, 2016 02:30 PM EDT Reads: 866
Jul. 25, 2016 02:00 PM EDT Reads: 956
Jul. 25, 2016 01:15 PM EDT Reads: 1,911
Jul. 25, 2016 01:00 PM EDT Reads: 1,930