Hadoop, Big Data Analytics and Challenges Old Business Intelligence

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, 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.

Data-analysis and data visualization run on Hadoop. ETL and data warehousing and BI are just fine for the problem of looking at transactions here and there, but there's no chance of bringing it all together to look at the interactions across all of the islands of information.
Hadoop provides modules for data-integration (including connectors to mainframes, databases, social sources like Facebook and Twitter, and more), a spreadsheet-driven data-analysis environment, and a dashboarding and data-visualization environment.