A few weeks ago I had the opportunity to spend some time at the Strata Data Conference, presented by O'Reilly and Cloudera, in New York. While on site, I was able to meet with some of the speakers from the conference. In this series, you'll hear a few of those great conversations. Cloudera's modern platform for machine learning and analytics, optimized for the cloud, lets you build and deploy AI solutions at scale, efficiently and securely, anywhere you want. In addition, Cloudera Fast Forward Lab's expert guidance helps you realize your AI future, faster.
The last time I attended Strata Hadoop World was in 2013. The conference was held at the New York Midtown Hilton for just a little over two days. Back then in the Stone Ages of Big Data, there was no Kafka, Spark, or Tensorflow. Many teams were just beginning to explore realtime as a possibility, and the data formats we most associate with Hadoop today, Avro and Parquet, were just starting to gain traction as Apache projects. Some of the ideas of that year's conference focused on the nascent field of data science.
Cloudera's Impala open source project is now a public beta. The company also launches a real-time query services as its second subscription offering. This is a big week in the analytics world as both Gartner's Data & Analytics Summit in Grapevine, TX and the Strata Data Conference in San Jose are taking place. Many vendors are attending and exhibiting at both; some vendors are only at one, but just about everyone in the analytics world is exhibiting at one of them, at least. Strata, which kicks off today, is more of an announcement vehicle for vendors though, and today three big names in the Big Data world -- Cloudera, MapR and AtScale -- have new releases to announce.
The fall Strata conference is when Big Data makes it to Broadway. And the week was very much a blur. We used to come away from Strata with the memory of one or two overriding themes; last year it was machine learning and the new infatuation with Spark, before that it was about Hadoop opening up the opportunity for exploratory analytics and for Hadoop to disappear behind a veneer of familiar SQL. It's easy to get excited by the idealism around the shiny new thing. But let's set something straight: Spark ain't going to replace Hadoop.