The discussion of big data technology can often be split into one side or another: the realtime capabilities of databases that often have to organize records at sub-second speeds, or the analytical capabilities of databases that have to comprehensively search those same records at the same level of speed.
Matthew Dennis from DataStax interviewed at OSCON 2011
Jonathan Ellis, CTO of DataStax and project chair for Apache Cassandra, keynoted at Cassandra SF 2011.
Last week, in a piece from our friends at GigaOM, Database Grandpoobah Mike Stonebraker announced that Facebook’s continued dependance on MySQL was “a fate worse than death,” insisting that the social network’s only route to salvation is to “bite the bullet and rewrite everything.”
One of the key, driving changes to IT infrastructure today is the exponential growth in the areas of data, storage, processing power and bandwidth utilization.
Big data — as in managing and analyzing — large volumes of information, has come a long way in the past couple of years.
Established vendors and startups alike have spearheaded advanced technologies for managing petabytes of data that have sprung from social computing and data analysis applications, commonly called Big Data.
CQL will look very familiar to anyone who knows SQL, with most of the usual keywords – Select, Use, Update, Drop and Create are all there and work pretty much as you expect.
- Fighting Sepsis with Real-Time Analytics
- Out with the old… in with the new
- Why We Added In-Memory to Cassandra
- DataStax Enterprise 4.0 Gives in-Memory Option to Cassandra
- DataStax Brings In-Memory To NoSQL
- DataStax’s Cassandra Isn’t Just a NoSQL Database
- DataStax Adds In-Memory Option to Cassandra Database for 100x Speed-Up
- Apache Cassandra gets in-memory option with DataStax Enterprise 4.0
- DataStax adds in-memory option to Cassandra database
- DataStax Brings In-Memory Computing to NoSQL Cassandra