October 16, 2012

Hadapt Version 2

My clients at Hadapt are coming out with a Version 2 to be available in Q1 2013, and perhaps slipstreaming some of the features before then. At that point, it will be reasonable to regard Hadapt as offering:

Solr is in the mix as well.

Hadapt+Hadoop is positioned much more as “better than Hadoop” than “a better scale-out RDBMS”– and rightly so, due to its limitations when viewed strictly from an analytic RDBMS standpoint. I.e., Hadapt is meant for enterprises that want to do several of:

Hadapt has 6 or so production customers, a dozen or so more coming online soon, 35 or so employees (mainly in Cambridge or Poland), reasonable amounts of venture capital, and the involvement of a variety of industry luminaries. Hadapt’s biggest installation seems to have 10s of terabytes of relational data and 100s of TBs of multi-structured; Hadapt is very confident in its ability to scale an order of magnitude beyond that with the Version 2 product, and reasonably confident it could go even further.

At the highest level, Hadapt works like this: Read more

June 19, 2012

“Enterprise-ready Hadoop”

This is part of a four-post series, covering:

The posts depend on each other in various ways.

Cloudera, Hortonworks, and MapR all claim, in effect, “Our version of Hadoop is enterprise-ready, unlike those other guys’.” I’m dubious.

That said, “enterprise-ready Hadoop” really is an important topic.

So what does it mean for something to be “enterprise-ready”, in whole or in part? Common themes in distinguishing between “enterprise-class” and other software include:

For Hadoop, as for most things, these concepts overlap in many ways. Read more

April 24, 2012

Notes on the Hadoop and HBase markets

I visited my clients at Cloudera and Hortonworks last week, along with scads of other companies. A few of the takeaways were:

February 6, 2012

Comments on the 2012 Forrester Wave: Enterprise Hadoop Solutions

Forrester has released its Q1 2012 Forrester Wave: Enterprise Hadoop Solutions. (Googling turns up a direct link, but in case that doesn’t prove stable, here also is a registration-required link from IBM’s Conor O’Mahony.) My comments include:

September 23, 2011

Some notes on Hadoop (mainly) and appliances

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.

3. On its latest earnings call, Oracle clearly said it would introduce a Hadoop appliance, versus just hinting at a Hadoop appliance the prior quarter. The money quote was:  Read more

September 12, 2011

Hadoop notes

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:

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.

Read more

July 10, 2011

Hadoop futures and enhancements

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:

(Zettaset belongs in the discussion too, but made an unfortunate choice of embargo date.)

Read more

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