In the rapidly evolving SQL-on-Hadoop space, IBM’s Big SQL 3.0 moves the industry forward through its contributions in improving query performance and workload management, while maintaining compatibility with open source Hive and SQL standard. This blog gets under the hood to explain how Big SQL 3.
Big data technologies like Hadoop are providing enterprises a cost-effective way to store and analyze data. Enterprises are looking at using Hadoop to augment their traditional data warehouse. Compared to traditional data warehouse solutions, Hadoop can scale using commodity hardware and can be
Many people want SQL, the query language of the past two decades, to work on Hadoop. Derrick Harris of GigaOM outlined the approaches of 13 different vendors with their cleverly named projects in his Feb 2013 article. Recently, Information Week’s special coverage series on big data included an
Batch processing of big data often doesn’t provide the performance desired for interactive queries. Performance will depend on the type and volume of data being processed. When interactive exploration or analytics is needed, data staging with analytics-oriented, high-volume databases is needed.
Hadoop has acquired a large body of prevailing myths in its short history as the hottest new big data technology. I'm surprised and dismayed when I see these myths propagated in leading business publications, such as in this recent Forbes article. Here now are some quick debunks of the myths in