Leverage what you already know with - SQL on Hadoop

Come and join use fo the look into BigSQL 3.0! Come for networking and Food at 6:30 PM and we'll get started at 7:00 PM. We look forward to seeing you there!

When considering SQL-on-Hadoop, the most fundamental question is: What is the right tool for the job? For interactive queries that require a few seconds (or even milliseconds) of response time, MapReduce (MR) is the wrong choice. On the other hand, for queries that require massive scale and runtime fault tolerance, an MR framework works well. MR was built for large-scale processing on big data, viewed mostly as “batch” processing.

As enterprises start using Apache Hadoop as a central data repository for all data — originating from sources as varied as operational systems, sensors, smart devices, metadata and internal applications — SQL processing becomes an optimal choice. A fundamental reason is that most enterprise data management and analytical tools rely on SQL.

As a tool for interactive query execution, SQL processing (of relational data) benefits from decades of research, usage experience and optimizations. Clearly, the SQL skills pool far exceeds that of MR developers and data scientists. As a general-purpose processing framework, MR may still be appropriate for ad hoc analytics, but that is as far as it can go with current technology.