Everyone around the internet is constantly talking about the bright future of Apache Spark. How cool it is, how innovative it is, how fast it is moving, how big its community is, how big the investments into it are, etc. But what is really hiding behind this enthusiasm of Spark adepts, and what is the real future of Apache Spark?
In this article I show you the real data and real trends, trying to be as agnostic and unbiased as possible. This article is not affiliated with any vendor.
Recently Cloudera announces new storage engine for fast analytics and fast data called Kudu. This is a very interesting piece of code and I couldn’t withstand an attraction of analyzing this technology deeper and going beyond the marketing.
Recently Databricks announced availability of DataFrames in Spark , which gives you a great opportunity to write even simpler code that would execute faster, especially if you are heavy Python/R user. In this article I would go a bit deeper than the publicly available benchmark results to show you how it really works.