Open Source, Flattery, and The Platform for Big Data
It has been a busy time for announcements coinciding with this week’s Strata conference. There’s no corner of the technology world that has not embraced Apache Hadoop as the new platform for big data. Apache Hadoop began as a telegram from the future from Google, turned into real software by Doug Cutting while on a freelance assignment. While Hadoop’s origins are surprising, its ongoing popularity is not – open source has been a major contributing factor to Hadoop’s current ubiquity. Easy to trial, fast to evolve, inexpensive to own: open source makes a compelling case for itself.
From the founding of the company, Cloudera recognized the importance of Apache open source to Hadoop’s continued evolution. We’re now entering our fifth year of shipping a 100% open source platform. Every significant advance we have added to the platform has stayed consistent to our open source strategy. In the process Cloudera has now sponsored the development of seven new open source projects including Apache Flume, Apache Sqoop, Apache Bigtop, Apache MRUnit, Cloudera Hue, Apache Crunch, and most recently, Cloudera Impala. Acknowledging the maxim “innovation happens elsewhere,” we’ve also managed to convince the founders and/or PMC chairs of Apache Hadoop, Apache Oozie, Apache Zookeeper, and Apache HBase to come join Cloudera.
Our investment in open source is not altruistic — we think it is good business. Today, Cloudera employees contribute more patches to the Apache Hadoop ecosystem than every other software vendor combined. Meanwhile more enterprises have adopted our open source platform than every other Hadoop distribution combined. We do not think it is a coincidence that these two things are simultaneously true.
I’m reminded of our open source strategy this week not only because of the further validation of Hadoop’s popularity but also because of the entry of a new round of proprietary imitators. At one point there were six distinct vendors all promoting proprietary filesystems as alternatives to HDFS, many of which included breathless claims of how they could make Apache Hadoop faster and “more powerful.” This year we get to see history repeat itself, this time with SQL engines. The marketing is nearly identical to that of the proprietary filesystem era: damning open source with faint praise, pointing out its limitations and extolling the virtues of some feature(s) proprietary to that particular vendor.
Our bet continues to be that open source wins and Impala is evidence of that belief. We took an expensive and innovative R&D project and released it for free as Apache-licensed open source. It is the first and only functioning open source interactive SQL engine for the Hadoop stack and one that will continue to rapidly evolve.
Proprietary SQL vendors will pull a page from the proprietary storage playbook: damn open source Impala with faint praise and point out its limitations, both real and contrived. They will be equally ineffective. We will continue to bet on an open, integrated, and highly flexible big data platform. Saying you are “all in on Hadoop” while simultaneously promoting a proprietary platform means you are missing the point.