We had a ton of fun presenting at Strata’s “UN-conference” this week. In our presentation (slides here) we covered some of the most important transformations the space is going through right now, namely:

Legacy approaches have restricted your potential

Current Big Data Analytics solutions limit your people’s perspective. The average business user sits on Terabytes of Data that they could be mining for insights. Yet, legacy in-memory solutions only allow users to analyze small datasets at a time. The industry has been telling them that it is ok to compromise completeness for speed. We don’t agree. You can have both. We showed that anyone could analyze 10TB in less than 10 seconds. Try it for yourself here.

Working with Data is Still Too Expensive

Storing 1 Terabyte used to cost $14M in 1980. It costs $30 today. Yet, analyzing data is still incredibly expensive. Some vendors will have you believe that it is ok to charge $500,000 to analyze .5TB of data. We don’t agree. At Strata, we showed that anyone can analyze 10TB for $10,000. How did we do it? Our software uses the latest CPU technology advancements; while other solutions let your CPU hang or waste cycles, we take full advantage of your machines and make them work for you. Find out more here.

The Demise of “The Stack”

For decades, customers have been told they had to build “best-of-breed” analytics stacks. This meant buying a database, an ETL platform and a visualization tool. Each part of stack would be deployed by different parts of the organization and bought from different vendors. Beyond the obvious issue of technology integration, this led to lack of alignment across the organization and poor performance. We don’t think that customers should have to buy, deploy, maintain or pay of the consequences of poorly integrated stacks. Our software has it all in one package. Try out for free here.

If you were there, I hope you had fun. If you weren’t, I’m embedding our presentation below (hope you’ll enjoy some of our *less geeky-but-still-funny* slides in there). Feel free to reuse, full deck is here!

The Problem with Big Data from Bruno Aziza