This post was jointly authored by Merv Adrian (@merv) and Nick Heudecker (@nheudecker) and appears on both of our Gartner blogs.
In the early days of Hadoop (versions up through 1.x), the project consisted of two primary components: HDFS and MapReduce. One thing to store the data in an append-only file model, distributed across an arbitrarily large number of inexpensive nodes with disk and processing power; another to process it, in batch, with a relatively small number of available function calls. And some other stuff called Commons to handle bits of the plumbing. But early adopters demanded more functionality, so the Hadoop footprint grew. The result was an identity crisis that grows progressively more challenging for decisionmakers with almost every new announcement.