Owen O’Malley is one of the folks I chatted with at the last Hadoop Summit in San Jose. I already discovered the first time I met him that he was the big Tolkien geek behind the naming of ORC files, as well as making sure that Not All Hadoop Users Drop ACID. In this conversation, I learned that Hadoop and Spark are both partially his fault, about the amazing performance strides Hive with ORC, Tez and LLAP have made, and that he’s a Trek geek, too.
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A few weeks ago at Hadoop Summit, I caught up with some friends from the project I worked on last year with Hortonworks, including Ryan Merriman who is now an Apache Metron architect. Since Apache Metron was a project I knew virtually nothing about beforehand, I quizzed Ryan about it. The conversation evolved into a discussion of the merits of Storm versus Flink and Heron, something I’ve been meaning to delve into for months here.
I learned a lot at Data Day Texas. I live tweeted a lot of interesting bits on @RobertsPaige as I went along, but some of the most enjoyable and enlightening stuff happened at the happy hour afterward.
Spark is the darling of the open source community right now. It’s setting the Hadoop world on fire with its power and speed in large scale data processing on Hadoop clusters. Spark is one of the most active big data open source projects, has bunches of enthusiastic committers, has its own group of ecosystem applications, and is now part of most standard Hadoop distributions. Neat trick for a data processing framework that didn’t even start life as a Hadoop project.
Tez is one of the marvelous ironies of the fast moving big data and open source software space, a piece of brilliant technology that was obsolete almost as soon as it was released. In the second in my series of short posts on Hadoop data processing frameworks, I’ll look at the bouncing baby born of the Stinger Initiative, and point out where it’s ugly.
MapReduce is no longer the only way to process data on Hadoop. In fact, it’s arguably the worst Hadoop data processing framework.
By now, everyone knows how awesome Hadoop is for large scale, data storage, processing and analysis. Hadoop is the darling of large scale data processing, while MapReduce keeps getting nothing but bad press and complaints that it’s too slow, too hard to use, and generally doesn’t live up to its hype. But aren’t Hadoop and MapReduce the same thing?