After visiting California recently, I made a flurry of posts, several of which generated considerable discussion.
- My claim that Spark will replace Hadoop MapReduce got much Twitter attention — including some high-profile endorsements — and also some responses here.
- My MemSQL post led to a vigorous comparison of MemSQL vs. VoltDB.
- My post on hardware and storage spawned a lively discussion of Hadoop hardware pricing; even Cloudera wound up disagreeing with what I reported Cloudera as having said. Sadly, there was less response to the part about the partial (!) end of Moore’s Law.
- My Cloudera/SQL/Impala/Hive apparently was well-balanced, in that it got attacked from multiple sides via Twitter & email. Apparently, I was too hard on Impala, I was too hard on Hive, and I was too hard on boxes full of cardboard file cards as well.
- My post on the Intel/Cloudera deal garnered a comment reminding us Dell had pushed the Intel distro.
- My CitusDB post picked up a few clarifying comments.
Here is a catch-all post to complete the set.
1. The recently-announced Cloudera/MongoDB relationship* is still at the Barney stage. That said, I’m optimistic that their stated intention to add substance to the relationship will eventually come to fruition. If nothing else, the two companies have high regard for each other, at least at the Mike Olson/Max Schireson level.
*That’s one of numerous deals with my fingerprints on it, but in this case only lightly. It was probably on track to happen even without my nudges.
2. Most of what I talked about when I visited MongoDB is confidential; the public stuff was mainly in my recent MongoDB technology post. But in one exception, I asked Max for an update as to MongoDB enterprise use cases. He reported a cluster in data combination, especially but not only in use cases which have both high-volume part and dynamic-schema aspects. Specific examples Max cited included:
- Tracking financial holdings from a variety of asset classes — especially if derivatives are involved, because they have a dynamic-schema aspect.
- Product catalogs, including for use on web sites.
- Customer information.
- Patient information.
3. I didn’t ask everybody I saw in California about business trends, and much of what we did discuss was confidential. That said:
- MapR was proud of its numbers.
- So was DataStax.
- ClearStory has a bunch of Very Big Enterprises as customers, mainly but not only in consumer sectors (e.g. retail, packaged goods).
4. Platfora is focusing a bit, starting with clickstream and security — i.e., event series stuff. And by the way, they report that the term “event series” is working well for them.
5. I gather from a variety of comments and conversations that Amazon Redshift has achieved considerable traction.
6. Something I can’t find evidence of having posted before: I think multiple businesses monitor online sales or similar business successes as a guide to network problems. eBay did this via a custom in-memory MOLAP (Multidimensional Online Analytic Process) system years ago. Best evidence that this is hardly restricted to eBay: all the “me-too” responses I get from telling that story.
7. Citus Data tells me that as of PostgreSQL 9.4, Postgres will be able to return just the part of a JSON column needed for a query. This is as opposed to storing the whole thing as text and only retrieving it in its entirety.
8. In the comments to my “Spark on fire” post, Patrick McFadin pointed out that Mahout is transitioning from MapReduce to Spark. (All new work will be on Spark, although old MapReduce-based routines will continue to be supported.) It turns out that Derrick Harris wrote about that over a month ago, and I just missed the news.
9. Also in predictive analytics — there are rumblings that R could eventually be supplanted by Julia, although R’s massive libraries of algorithms still give it the advantage now.
10. Multiple vendors, fed up with the intermittent slowdowns from garbage collection, are moving some processing off the Java heap. Unfortunately, I neglected to ask any of them what the remaining differences then were between Java and C++ programming.
11. And to finish on a light note: BDAS — the project of which Spark is only a part — is pronounced “bad-ass”, something I first heard from Dave Patterson.