TDWI | Training & Research | Business Intelligence, Analytics, Big Data, Data Warehousing

SQL-for-Hadoop: 14 Capable Solutions Reviewed

January 1, 2016

There’s a new dynamic duo in big data town: SQL and Hadoop. If you think this an unlikely combination, think twice. Enterprises have gobs of structured and semistructured data generated by all sorts of transactional applications, and SQL is still the best option for querying it. Much of that data is increasingly finding its way into Hadoop clusters for analytics because of its versatility and the economical, linear scalability of both data storage and compute.

In this white paper, Forrester has identified and reviewed open source and commercial SQL engines for Hadoop to help application and development and delivery professionals learn about the maturity and sweet spot for each and choose the best for their enterprise’s needs. You may need to choose more than one to satisfy all of your requirements.

Key takeaways from this white paper include:

• SQL-for-Hadoop is an essential tool for data lakes. A large majority of global data and analytics technology decision makers are interested in planning, implementing, or expanding SQL-for-Hadoop. That’s not surprising because enterprises are building data lakes full of structured and semistructured data.
• SQL-for-Hadoop is getting good—good enough for data warehouse use cases. SQL-for-Hadoop, especially the commercial SQL engines from the database kingpins, is American National Standards Institute (ANSI) compatible and fast. They are getting so good that, for some use cases, they are starting to give traditional data warehouse appliances a run for their big money. Some of the best SQL-for-Hadoop solutions come from those vendors.

Sponsored by Hewlett Packard Enterprise