Is batch-oriented ETL data integration, relational DW, and old-school analytics too slow, rigid, and expensive to keep up in the big-data era ?
InformationWeek, Doug Henschen, June 12, 2012
Karmasphere, two competing upstart vendors offering reporting, data-visualization, and data-analysis capabilities on top of Hadoop, released new versions of their software on Monday. Both talked up the need for next-generation tools.
It's not that old-school business intelligence software tools are going away, these upstarts grant. But both portray batch-oriented extract-transform-load (ETL) data integration, relational data warehousing, and old-school analytics as too slow, rigid, and expensive to keep up in the big-data era.
Hadoop is the future, these vendor's contend, because it's a massively scalable data-management and analysis environment that can handle variably structure data from many sources--log files, clickstreams, sensor data, social media sources and so on--without the delays inherent in dealing with the static schemas of relational databases.
If companies want to look at recent point-of-sale transactions alongside Web site clickstreams, recent online enrollments, email campaign results, and social media chatter, for example, it would be difficult if not impossible to quickly put all that data into a relational data warehouse and look for correlations.