Hadapt Overview: Question Everything

Join the revolution. Put the power of Big Data in the palm of your hand and...Hadapt.

How to Play Big Data

Bessemer Venture Partners' Kent Bennett discusses his plays for Big Data with Deirdre Bolton on Bloomberg Television's "Money Moves". Bennett says Hadapt is Bessemer's "core investment" at the center of its Big Data stack.

Hadapt Wants to Give Everyone a Competitive Advantage, Not Just the Quants

Vice President of Marketing Scott Howser discusses Hadapt’s current customer focus of fraud detection for financial services, as well as enhancements they are pursuing for customer behavior analytics (CBA). Scott says that Hadapt’s CBA work has attempted to use information like click-throughs and windowing to harvest more granular data about transactional behaviors.

GigaOM 2013: Hadapt is Accelerating Hadoop into Enterprise Production

The ability to run SQL-based applications on top of Hadoop is accelerating Hadoop into production environments, says CEO Justin Borgman. Hadapt empowers analysts to work with all data types, in one place, with just the click of a standard BI tool.

Strata 2013: Hadapt Leads the Way Amongst “SQL on Hadoop” Players

Hadapt Vice President of Marketing Scott Howser steps inside theCUBE to discuss the growing market trend of “SQL on Hadoop” offerings, and why Hadapt remains unique in its value-add. Howser also summarizes Hadapt’s latest customer traction and use cases.

Recent Hadapt Blog Posts

Classifying Today’s “Big Data Innovators” Friday, December 21, 2012 at 9:57 am | Daniel Abadi

Uncategorized (7)

Dhiraj ShettyCould this product play a role in bringing medical big data analysis to mobiles/handhelds, without comprising on HIPAA guidelines. Regards, Dhiraj.Keith PrinceOh dear, MPP and Hadoop comparisons based around the premise that "There is absolutely no technical reason why there needs to be two separate systems doing the exact same type of parallel processing". I'd 100% agree that there are some folks who are replicating the same type of workload on two platforms. The early adopter/leaders understand that MPP systems don't do parallel operations in the same way as Hadoop. That's why you'll see Google, Yahoo, Amazon, etc, use Hadoop architectures for certain types of operations and MPP systems (mainly appliances today) for other types. Each architecture is optimized for a different type of workload. And there are some relational databases that do poly-structured really well so it should not be a structured vs unstructured debate too. Yes you can race a rally car at a F1 meeting, it will go around the track pretty rapidly and look great but it won't compete. I nearly said its a horses for courses thing but then I'd be disappearing up my own metaphor :). Apart from the fundamental point that parallel architectures are not the same, I think more articles like yours should fuel a more honest appraisal of how things really work. Then again the in-memory folks will come along and say Hadoop and MPP are both yesterdays news!Roberto V. ZicariTwo comments: - I wonder how in-memory databases for analytics such a SAP HANA fits into your classification - I`d suggest to include MPP database vendors in the classification as well (e.g. Teradata, Vertica) to make it complete.