Data mart outsourcing

Discussion of services that analyze large databases on an outsourced basis. Related subjects include:

November 12, 2011

Clarifying SAND’s customer metrics, positioning and technical story

Talking with my clients at SAND can be confusing. That said:

A few months ago, I wrote:

SAND Technology reported >600 total customers, including >100 direct.

Upon talking with the company, I need to revise that figure downward, from > 600 to 15.

Read more

July 5, 2011

Eight kinds of analytic database (Part 2)

In Part 1 of this two-part series, I outlined four variants on the traditional enterprise data warehouse/data mart dichotomy, and suggested what kinds of DBMS products you might use for each. In Part 2 I’ll cover four more kinds of analytic database — even newer, for the most part, with a use case/product short list match that is even less clear.  Read more

May 23, 2010

More on Sybase IQ, including Version 15.2

Back in March, Sybase was kind enough to give me permission to post a slide deck about Sybase IQ. Well, I’m finally getting around to doing so. Highlights include but are not limited to:

Sybase IQ may have a bit of a funky architecture (e.g., no MPP), but the age of the product and the substantial revenue it generates have allowed Sybase to put in a bunch of product features that newer vendors haven’t gotten around to yet.

More recently, Sybase volunteered permission for me to preannounce Sybase IQ Version 15.2 by a few days (it’s scheduled to come out this week). Read more

May 15, 2010

Stakeholder-facing analytics

There’s a point I keep making in speeches, and used to keep making in white papers, yet have almost never spelled out in this blog. Let me now (somewhat) correct the oversight.

Analytic technology isn’t only for you. It’s also for your customers, citizens, and other stakeholders.

I am not referring here to what is well understood to be an important, fast-growing activity — providing data and its analysis to customers as your primary or only business — nor to the related business of taking people’s data, crunching it for them, and giving them results. That combined sector — which I am pretty alone in aggregating into one and calling data mart outsourcing — is one of the top several vertical markets for a lot of the analytic DBMS vendors I write about. Rather, I’m talking about enterprises that gather data for some primary purpose, and have discovered that a good secondary use of the data is to reflect it back to stakeholders, often the same ones who provided or created it in the first place.

For now I’ll call this category stakeholder-facing analytics, as the shorter phrase “stakeholder analytics” would be ambiguous.* I first picked up the idea early this decade from Information Builders, for whom it had become something of a specialty. I’ve been asking analytics vendors for examples of stakeholder-facing analytics ever since, and a number have been able to comply. But the whole thing is in its early days even so; almost any sufficiently large enterprise should be more active in stakeholder-facing analytics than it currently is.
Read more

March 19, 2010

Infobright blog update

I often offer that, if a company puts up a sufficiently good blog post, I’ll link to it. Well, I just noticed that Infobright CEO Mark Burton (somewhere along the way he seems to have dropped the “interim”) put up an excellent post last month.

Highlights on the market share/sector side include: Read more

January 25, 2010

Netezza Skimmer

As I previously complained, last week wasn’t a very convenient time for me to have briefings. So when Netezza emailed to say it would release its new entry-level Skimmer appliance this morning, while I asked for and got a Friday afternoon briefing, I kept it quick and basic.

That said, highlights of my Netezza Skimmer briefing included:

Read more

October 14, 2009

Infobright notes

I had lunch w/ Bob Zurek and Susan Davis of Infobright today. This wasn’t primarily a briefing, but a few takeaways are:

September 29, 2009

What Nielsen really uses in data warehousing DBMS

In its latest earnings call, Oracle made a reference to The Nielsen Company that was — to put it politely — rather confusing. I just plopped down in a chair next to Greg Goff, who evidently runs data warehousing at Nielsen, and had a quick chat. Here’s the real story.

August 25, 2009

Sybase IQ business notes

As specialized analytic DBMS go, Sybase is near the top of the charts both in age (Sybase IQ was first introduced in the mid 1990s) and adoption. That’s even more true, of course, if we restrict the discussion strictly to columnar DBMS, aka column stores. Basic Sybase IQ adoption claims include:

Note that 98% of Sybase IQ installations are under 5 terabytes; the heart of Sybase IQ’s business is the sub-terabyte data warehouse market.* Read more

July 29, 2009

What are the best choices for scaling Postgres?

March, 2011 edit: In its quaintness, this post is a reminder of just how fast Short Request Processing DBMS technology has been moving ahead.  If I had to do it all over again, I’d suggest they use one of the high-performance MySQL options like dbShards, Schooner, or both together.  I actually don’t know what they finally decided on in that area. (I do know that for analytic DBMS they chose Vertica.)

I have a client who wants to build a new application with peak update volume of several million transactions per hour.  (Their base business is data mart outsourcing, but now they’re building update-heavy technology as well. ) They have a small budget.  They’ve been a MySQL shop in the past, but would prefer to contract (not eliminate) their use of MySQL rather than expand it.

My client actually signed a deal for EnterpriseDB’s Postgres Plus Advanced Server and GridSQL, but unwound the transaction quickly. (They say EnterpriseDB was very gracious about the reversal.) There seem to have been two main reasons for the flip-flop.  First, it seems that EnterpriseDB’s version of Postgres isn’t up to PostgreSQL’s 8.4 feature set yet, although EnterpriseDB’s timetable for catching up might have tolerable. But GridSQL apparently is further behind yet, with no timetable for up-to-date PostgreSQL compatibility.  That was the dealbreaker.

The current base-case plan is to use generic open source PostgreSQL, with scale-out achieved via hand sharding, Hibernate, or … ??? Experience and thoughts along those lines would be much appreciated.

Another option for OLTP performance and scale-out is of course memory-centric options such as VoltDB or the Groovy SQL Switch.  But this client’s database is terabyte-scale, so hardware costs could be an issue, as of course could be product maturity.

By the way, a large fraction of these updates will be actual changes, as opposed to new records, in case that matters.  I expect that the schema being updated will be very simple — i.e., clearly simpler than in a classic order entry scenario.

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