Analysis of SAP AG, and most especially its memory-centric BI Accelerator technology. Also covered are SAP’s overall database, connectivity, and analytics strategies. Related subjects include:

September 23, 2013

Thoughts on in-memory columnar add-ons

Oracle announced its in-memory columnar option Sunday. As usual, I wasn’t briefed; still, I have some observations. For starters:

I’d also add that Larry Ellison’s pitch “build columns to avoid all that index messiness” sounds like 80% bunk. The physical overhead should be at least as bad, and the main saving in administrative overhead should be that, in effect, you’re indexing ALL columns rather than picking and choosing.

Anyhow, this technology should be viewed as applying to traditional business transaction data, much more than to — for example — web interaction logs, or other machine-generated data. My thoughts around that distinction start:

Read more

September 11, 2013

SAP is buying KXEN

First, some quick history.

However, I don’t want to give the impression that KXEN is the second coming of Crystal Reports. Most of what I heard about KXEN’s partnership chops, after Roman’s original heads-up, came from Teradata. Even KXEN itself didn’t seem to see that as a major part of their strategy.

And by the way, KXEN is yet another example of my observation that fancy math rarely drives great enterprise software success.

KXEN’s most recent strategies are perhaps best described by contrasting it to the vastly larger SAS.  Read more

August 12, 2013

Things I keep needing to say

Some subjects just keep coming up. And so I keep saying things like:

Most generalizations about “Big Data” are false. “Big Data” is a horrific catch-all term, with many different meanings.

Most generalizations about Hadoop are false. Reasons include:

Hadoop won’t soon replace relational data warehouses, if indeed it ever does. SQL-on-Hadoop is still very immature. And you can’t replace data warehouses unless you have the power of SQL.

Note: SQL isn’t the only way to provide “the power of SQL”, but alternative approaches are just as immature.

Most generalizations about NoSQL are false. Different NoSQL products are … different. It’s not even accurate to say that all NoSQL systems lack SQL interfaces. (For example, SQL-on-Hadoop often includes SQL-on-HBase.)

Read more

July 20, 2013

The refactoring of everything

I’ll start with three observations:

As written, that’s probably pretty obvious. Even so, it’s easy to forget just how pervasive the refactoring is and is likely to be. Let’s survey some examples first, and then speculate about consequences. Read more

June 23, 2013

Hadoop news and rumors, June 23, 2013


*Of course, there will always be exceptions. E.g., some formats can be updated on a short-request basis, while others can only be written to via batch conversions.

Everybody else

February 21, 2013

One database to rule them all?

Perhaps the single toughest question in all database technology is: Which different purposes can a single data store serve well? — or to phrase it more technically — Which different usage patterns can a single data store support efficiently? Ted Codd was on multiple sides of that issue, first suggesting that relational DBMS could do everything and then averring they could not. Mike Stonebraker too has been on multiple sides, first introducing universal DBMS attempts with Postgres and Illustra/Informix, then more recently suggesting the world needs 9 or so kinds of database technology. As for me — well, I agreed with Mike both times. :)

Since this is MUCH too big a subject for a single blog post, what I’ll do in this one is simply race through some background material. To a first approximation, this whole discussion is mainly about data layouts — but only if we interpret that concept broadly enough to comprise:

To date, nobody has ever discovered a data layout that is efficient for all usage patterns. As a general rule, simpler data layouts are often faster to write, while fancier ones can boost query performance. Specific tradeoffs include, but hardly are limited to: Read more

February 5, 2013

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

To my taste, the most glaring mis-rankings in the 2012/2013 Gartner Magic Quadrant for Data Warehouse Database Management are that it is too positive on Kognitio and too negative on Infobright. Secondarily, it is too negative on HP Vertica, and too positive on ParAccel and Actian/VectorWise. So let’s consider those vendors first.

Gartner seems confused about Kognitio’s products and history alike.

Gartner is correct, however, to note that Kognitio doesn’t sell much stuff overall.

* non-existent

In the cases of HP Vertica, Infobright, ParAccel, and Actian/VectorWise, the 2012 Gartner Magic Quadrant for Data Warehouse Database Management’s facts are fairly accurate, but I dispute Gartner’s evaluation. When it comes to Vertica: Read more

October 6, 2012

Analyzing big companies is hard

Analyzing companies of any size is hard. Analyzing large ones, however, is harder yet.

Such limitations should be borne in mind in connection with anything I write about, for example, Oracle, Microsoft, IBM, or SAP.

There are many reasons for large companies to communicate less usefully with analysts than smaller ones do. Some of the biggest are:

Read more

September 7, 2012

Integrated internet system design

What are the central challenges in internet system design? We probably all have similar lists, comprising issues such as scale, scale-out, throughput, availability, security, programming ease, UI, or general cost-effectiveness. Screw those up, and you don’t have an internet business.

Much new technology addresses those challenges, with considerable success. But the success is usually one silo at a time — a short-request application here, an analytic database there. When it comes to integration, unsolved problems abound.

The top integration and integration-like challenges for me, from a practical standpoint, are:

Other concerns that get mentioned include:

Let’s skip those latter issues for now, focusing instead on the first four.

Read more

April 7, 2012

Many kinds of memory-centric data management

I’m frequently asked to generalize in some way about in-memory or memory-centric data management. I can start:

Getting more specific than that is hard, however, because:

Consider, for example, some of the in-memory data management ideas kicking around. Read more

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