Analysis of data warehousing giant Teradata. Related subjects include:

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

January 26, 2013

Editing code is easier than writing it

I’ve hacked both the PHP and CSS that drive this website. But if I had to write PHP or CSS from scratch, I literally wouldn’t know how to begin.

Something similar, I suspect, is broadly true of “business analysts.” I don’t know how somebody can be a competent business analyst without being able to generate, read, and edit SQL. (Or some comparable language; e.g., there surely are business analysts who only know MDX.) I would hope they could write basic SELECT statements as well.

But does that mean business analysts are comfortable with the fancy-schmantzy extended SQL that the analytic platform vendors offer them? I would assume that many are but many others are not. And thus I advised such a vendor recently to offer sample code, and lots of it — dozens or hundreds of isolated SQL statements, each of which does a specific task.* A business analyst could reasonably be expected to edit any of those to point them his own actual databases, even though he can’t necessarily be expected to easily write such statements from scratch.  Read more

October 17, 2012

Notes on analytic hardware

I took the opportunity of Teradata’s Aster/Hadoop appliance announcement to catch up with Teradata hardware chief Carson Schmidt. I love talking with Carson, about both general design philosophy and his views on specific hardware component technologies.

From a hardware-requirements standpoint, Carson seems to view Aster and Hadoop as more similar to each other than either is to, say, a Teradata Active Data Warehouse. In particular, for Aster and Hadoop:

The most obvious implication is differences in the choice of parts, and of their ratio. Also, in the new Aster/Hadoop appliance, Carson is content to skate by with RAID 5 rather than RAID 1.

I think Carson’s views about flash memory can be reasonably summarized as: Read more

October 17, 2012

Hadoop/RDBMS integration: Aster SQL-H and Hadapt

Two of the more interesting approaches for integrating Hadoop and MapReduce with relational DBMS come from my clients at Teradata Aster (via SQL/MR and SQL-H) and Hadapt. In both cases, the story starts:

Of course, there are plenty of differences. Those start: Read more

October 17, 2012

The Teradata Aster Big Analytics Aster/Hadoop appliance

My clients at Teradata are introducing a mix-em/match-em Aster/Hadoop box, officially called the Teradata Aster Big Analytics Appliance. Basics include:

My views on the Teradata Aster Big Analytics Appliance start: Read more

September 27, 2012

Hoping for true columnar storage in Oracle12c

I was asked to clarify one of my July comments on Oracle12c,

I wonder whether Oracle will finally introduce a true columnar storage option, a year behind Teradata. That would be the obvious enhancement on the data warehousing side, if they can pull it off. If they can’t, it’s a damning commentary on the core Oracle codebase.

by somebody smart who however seemed to have half-forgotten my post comparing (hybrid) columnar compression to (hybrid) columnar storage.

In simplest terms:

August 26, 2012

How immediate consistency works

This post started as a minor paragraph in another one I’m drafting. But it grew. Please also see the comment thread below.

Increasingly many data management systems store data in a cluster, putting several copies of data — i.e. “replicas” — onto different nodes, for safety and reliable accessibility. (The number of copies is called the “replication factor”.) But how do they know that the different copies of the data really have the same values? It seems there are three main approaches to immediate consistency, which may be called:

I shall explain.

Two-phase commit has been around for decades. Its core idea is:

Unless a piece of the system malfunctions at exactly the wrong time, you’ll get your consistent write. And if there indeed is an unfortunate glitch — well, that’s what recovery is for.

But 2PC has a flaw: If a node is inaccessible or down, then the write is blocked, even if other parts of the system were able to accept the data safely. So the NoSQL world sometimes chooses RYW consistency, which in essence is a loose form of 2PC: Read more

August 19, 2012

In-database analytics — analytic glossary draft entry

This is a draft entry for the DBMS2 analytic glossary. Please comment with any ideas you have for its improvement!

Note: Words and phrases in italics will be linked to other entries when the glossary is complete.

“In-database analytics” is a catch-all term for analytic capabilities, beyond standard SQL, running on the same machine as and under the management of an analytic DBMS. These can run in one or both of two modes:

In-database analytics may offer great performance and scalability advantages versus the alternative of extracting data and having it be processed on a separate server. This is particularly likely to be the case in MPP (Massively Parallel Processing) analytic DBMS environments.

Examples of in-database analytics include:

Other common domains for in-database analytics include sessionization, time series analysis, and relationship analytics.

Notable products offering in-database analytics include:

August 19, 2012

Data warehouse appliance — analytic glossary draft entry

This is a draft entry for the DBMS2 analytic glossary. Please comment with any ideas you have for its improvement!

Note: Words and phrases in italics will be linked to other entries when the glossary is complete.

A data warehouse appliance is a combination of hardware and software that includes an analytic DBMS (DataBase Management System). However, some observers incorrectly apply the term “data warehouse appliance” to any analytic DBMS.

The paradigmatic vendors of data warehouse appliances are:

Further, vendors of analytic DBMS commonly offer — directly or through partnerships — optional data warehouse appliance configurations; examples include:

Oracle Exadata is sometimes regarded as a data warehouse appliance as well, despite not being solely focused on analytic use cases.

Data warehouse appliances inherit marketing claims from the category of analytic DBMS, such as: Read more

August 7, 2012

Notes on some basic database terminology

In a call Monday with a prominent company, I was told:

That, to put it mildly, is not accurate. So I shall try, yet again, to set the record straight.

In an industry where people often call a DBMS just a “database” — so that a database is something that manages a database! — one may wonder why I bother. Anyhow …

1. The products commonly known as Oracle, Exadata, DB2, Sybase, SQL Server, Teradata, Sybase IQ, Netezza, Vertica, Greenplum, Aster, Infobright, SAND, ParAccel, Exasol, Kognitio et al. all either are or incorporate relational database management systems, aka RDBMS or relational DBMS.

2. In principle, there can be difficulties in judging whether or not a DBMS is “relational”. In practice, those difficulties don’t arise — yet. Every significant DBMS still falls into one of two categories:

*I expect the distinction to get more confusing soon, at which point I’ll adopt terms more precise than “relational things” and “relational stuff”.

3. There are two chief kinds of relational DBMS: Read more

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