Kognitio evolved out of two businesses – Kognitio and White Cross Systems – which merged in 2005. It was responsible for releasing the world’s first fully functional, scaled-out, in-memory analytical platform in 1992.
We set out with a simple vision: that scale-out computing or Massively Parallel Processing (MPP) was the answer to the then emerging problem of how to analyze, in a timely manner, rapidly growing data sets. We realized early on, that massive parallelization required that the data be held in fast computer memory.
Historically, the high upfront cost of installing a dedicated hardware infrastructure meant that Kognitio remained a niche product for many years, however the emergence of Hadoop has provided us with a ready-made platform that can be shared with other data processing / analysis technologies.
There are many SQL on Hadoop technologies out there, so you might well ask what’s special about Kognitio. Well, put simply, there are large variations in the performance, flexibility and maturity of available SQL engines.
Hive, Impala and SparkSQL, for example, are new SQL implementations that were developed from scratch for Hadoop. Yet SQL is a very large, complex standard. It’s difficult enough to implement on a serial platform, but to implement it in parallel is mind-bogglingly difficult, and time consuming. Full parallel execution of the SQL functionality is important, because it allows products to scale.
For the past 25 years, Kognitio has been deployed on an infrastructure platform of clusters of industry standard servers – exactly like Hadoop’s. So we simply evolved the established Kognitio platform to work on Hadoop, and what’s more, using a mature SQL implementation means that it is more likely to have solved the complex issues around concurrent workloads.
Today our focus is to provide ultra-fast access to big data, especially for users using BI tools like Tableau or MicroStrategy, who need to make fast-paced decisions for their business. We enable these BI tools to maintain interactive performance, even when the data volume is large and the user count is high.