Microsoft and SQL*Server

Microsoft’s efforts in the database management, analytics, and data connectivity markets. Related subjects include:

June 2, 2013

SQL-Hadoop architectures compared

The genesis of this post is:

I love my life.

Per Daniel (emphasis mine): Read more

May 20, 2013

Some stuff I’m working on

1. I have some posts up on Strategic Messaging. The most recent are overviews of messaging, pricing, and positioning.

2. Numerous vendors are blending SQL and JSON management in their short-request DBMS. It will take some more work for me to have a strong opinion about the merits/demerits of various alternatives.

The default implementation — one example would be Clustrix’s — is to stick the JSON into something like a BLOB/CLOB field (Binary/Character Large Object), index on individual values, and treat those indexes just like any others for the purpose of SQL statements. Drawbacks include:

IBM DB2 is one recent arrival to the JSON party. Unfortunately, I forgot to ask whether IBM’s JSON implementation was based on IBM DB2 pureXML when I had the chance, and IBM hasn’t gotten around to answering my followup query.

3. Nor has IBM gotten around to answering my followup queries on the subject of BLU, an interesting-sounding columnar option for DB2.

4. Numerous clients have asked me whether they should be active in DBaaS (DataBase as a Service). After all, Amazon, Google, Microsoft, Rackspace and salesforce.com are all in that business in some form, and other big companies have dipped toes in as well. Read more

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 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 — 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

November 29, 2012

Notes on Microsoft SQL Server

I’ve been known to gripe that covering big companies such as Microsoft is hard. Still, Doug Leland of Microsoft’s SQL Server team checked in for phone calls in August and again today, and I think I got enough to be worth writing about, albeit at a survey level only,

Subjects I’ll mention include:

One topic I can’t yet comment about is MOLAP/ROLAP, which is a pity; if anybody can refute my claim that ROLAP trumps MOLAP, it’s either Microsoft or Oracle.

Microsoft’s slides mentioned Yahoo refining a 6 petabyte Hadoop cluster into a 24 terabyte SQL Server “cube”, which was surprising in light of Yahoo’s history as an Oracle reference.

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

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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