1. EMC Greenplum has evolved its appliance product line. As I read that, the latest announcement boils down to saying that you can neatly network together various Greenplum appliances in quarter-rack increments. If you take a quarter rack each of four different things, then Greenplum says “Hooray! Our appliance is all-in-one!” Big whoop.
2. That said, the Hadoop part of EMC ‘s story is based on MapR, which so far as I can tell is actually a pretty good Hadoop implementation. More precisely, MapR makes strong claims about performance and so on, and Apache Hadoop folks don’t reply “MapR is full of &#$!” Rather, they say “We’re going to close the gap with MapR a lot faster than the MapR folks like to think — and by the way, guys, thanks for the butt-kick.” A lot more precision about MapR may be found in this M. C. Srivas SlideShare.
|Categories: Data warehouse appliances, eBay, EMC, Greenplum, Hadoop, MapR, MapReduce, Open source, Oracle||2 Comments|
I visited California recently, and chatted with numerous companies involved in Hadoop — Cloudera, Hortonworks, MapR, DataStax, Datameer, and more. I’ll defer further Hadoop technical discussions for now — my target to restart them is later this month — but that still leaves some other issues to discuss, namely adoption and partnering.
The total number of enterprises in the world paying subscription and license fees that they would regard as being for “Hadoop or something Hadoop-related” probably is not much over 100 right now, but I’d expect to see pretty rapid growth. Beyond that, let’s divide customers into three groups:
- Internet businesses.
- Traditional enterprises ‘ internet operations.
- Traditional enterprises’ other operations.
Hadoop vendors, in different mixes, claim to be doing well in all three segments. Even so, almost all use cases involve some kind of machine-generated data, with one exception being a credit card vendor crunching a large database of transaction details. Multiple kinds of machine-generated data come into play — web/network/mobile device logs, financial trade data, scientific/experimental data, and more. In particular, pharmaceutical research got some mentions, which makes sense, in that it’s one area of scientific research that actually enjoys fat for-profit research budgets.
|Categories: Cloudera, Hadoop, Health care, Hortonworks, Investment research and trading, Log analysis, MapR, MapReduce, Market share and customer counts, Scientific research, Web analytics||5 Comments|
Hadoop is immature technology. As such, it naturally offers much room for improvement in both industrial-strengthness and performance. And since Hadoop is booming, multiple efforts are underway to fill those gaps. For example:
- Cloudera’s proprietary code is focused on management, set-up, etc.
- The “Phase 1″ plans Hortonworks shared with me for Apache Hadoop are focused on industrial-strengthness, as are significant parts of “Phase 2″.*
- MapR tells a performance story versus generic Apache Hadoop HDFS and MapReduce. (One aspect of same is just C++ vs. Java.)
- So does Hadapt, but mainly vs. Hive.
- Cloudera also tells me there’s a potential 4-5X performance improvement in Hive coming down the pike from what amounts to an optimizer rewrite.
(Zettaset belongs in the discussion too, but made an unfortunate choice of embargo date.)
|Categories: Cloudera, Greenplum, Hadapt, Hadoop, HBase, MapR, MapReduce, Parallelization, Zettaset||20 Comments|