August 24, 2013

Hortonworks business notes

Hortonworks did a business-oriented round of outreach, talking with at least Derrick Harris and me. Notes  from my call — for which Rob Bearden* didn’t bother showing up — include, in no particular order:

*Speaking of CEO Bearden, an interesting note from Derrick’s piece is that Bearden is quoted as saying “I started this company from day one …”, notwithstanding that the now-departed Eric Baldeschwieler was founding CEO.

In Hortonworks’ view, Hadoop adopters typically start with a specific use case around a new type of data, such as clickstream, sensor, server log, geolocation, or social.  Read more

May 29, 2013

Syncsort extends Hadoop MapReduce

My client Syncsort:

*Perhaps we should question Syncsort’s previous claims of having strong multi-node parallelism already. :)

The essence of the Syncsort DMX-h ETL Edition story is:

More details can be found in a slide deck Syncsort graciously allowed me to post. Read more

April 15, 2013

Notes on Teradata systems

Teradata is announcing its new high-end systems, the Teradata 6700 series. Notes on that include:

Teradata is also talking about data integration and best-of-breed systems, with buzzwords such as:

Read more

April 15, 2013

Teradata SQL-H

As vendors so often do, Teradata has caused itself some naming confusion. SQL-H was introduced as a facility of Teradata Aster, to complement SQL-MR.* But while SQL-MR is in essence a set of SQL extensions, SQL-H is not. Rather, SQL-H is a transparency interface that makes Hadoop data responsive to the same code that would work on Teradata Aster …

*Speaking of confusion — Teradata Aster seems to use the spellings SQL/MR and SQL-MR interchangeably.

… except that now there’s also a SQL-H for regular Teradata systems as well. While it has the same general features and benefits as SQL-H for Teradata Aster, the details are different, since the underlying systems are.

I hope that’s clear. :)

April 1, 2013

Some notes on new-era data management, March 31, 2013

Hmm. I probably should have broken this out as three posts rather than one after all. Sorry about that.

Performance confusion

Discussions of DBMS performance are always odd, for starters because:

But in NoSQL/NewSQL short-request processing performance claims seem particularly confused. Reasons include but are not limited to:

MongoDB and 10gen

I caught up with Ron Avnur at 10gen. Technical highlights included: Read more

March 26, 2013

Platfora at the time of first GA

Well-resourced Silicon Valley start-ups typically announce their existence multiple times. Company formation, angel funding, Series A funding, Series B funding, company launch, product beta, and product general availability may not be 7 different “news events”, but they’re apt to be at least 3-4. Platfora, no exception to this rule, is hitting general availability today, and in connection with that I learned a bit more about what they are up to.

In simplest terms, Platfora offers exploratory business intelligence against Hadoop-based data. As per last weekend’s post about exploratory BI, a key requirement is speed; and so far as I can tell, any technological innovation Platfora offers relates to the need for speed. Specifically, I drilled into Platfora’s performance architecture on the query processing side (and associated data movement); Platfora also brags of rendering 100s of 1000s of “marks” quickly in HTML5 visualizations, but I haven’t a clue as to whether that’s much of an accomplishment in itself.

Platfora’s marketing suggests it obviates the need for a data warehouse at all; for most enterprises, of course, that is a great exaggeration. But another dubious aspect of Platfora marketing actually serves to understate the product’s merits — Platfora claims to have an “in-memory” product, when what’s really the case is that Platfora’s memory-centric technology uses both RAM and disk to manage larger data marts than could reasonably be fit into RAM alone. Expanding on what I wrote about Platfora when it de-stealthedRead more

February 13, 2013

It’s hard to make data easy to analyze

It’s hard to make data easy to analyze. While everybody seems to realize this — a few marketeers perhaps aside — some remarks might be useful even so.

Many different technologies purport to make data easy, or easier, to an analyze; so many, in fact, that cataloguing them all is forbiddingly hard. Major claims, and some technologies that make them, include:

*Complex event/stream processing terminology is always problematic.

My thoughts on all this start:  Read more

February 6, 2013

Key questions when selecting an analytic RDBMS

I recently complained that the Gartner Magic Quadrant for Data Warehouse DBMS conflates many use cases into one set of rankings. So perhaps now would be a good time to offer some thoughts on how to tell use cases apart. Assuming you know that you really want to manage your analytic database with a relational DBMS, the first questions you ask yourself could be:

Let’s drill down. Read more

February 5, 2013

Comments on Gartner’s 2012 Magic Quadrant for Data Warehouse Database Management Systems — concepts

The 2012 Gartner Magic Quadrant for Data Warehouse Database Management Systems is out. I’ll split my comments into two posts — this one on concepts, and a companion on specific vendor evaluations.

Links:

Let’s start by again noting that I regard Gartner Magic Quadrants as a bad use of good research. On the facts:

When it comes to evaluations, however, the Gartner Data Warehouse DBMS Magic Quadrant doesn’t do as well. My concerns (which overlap) start:

Read more

January 5, 2013

Data(base) virtualization — a terminological mess

Data/database virtualization seems to be a hot subject right now, and vendors of a broad variety of different technologies are all claiming to be in the space. A terminological mess has ensued, as Monash’s First and Third Laws of Commercial Semantics are borne out in spades.

If something is like “virtualization”, then it should resemble hypervisors such as VMware. To me:

Anything that claims to be “like virtualization” should be viewed in that light. Read more

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