Data warehousing
Analysis of issues in data warehousing, with extensive coverage of database management systems and data warehouse appliances that are optimized to query large volumes of data. Related subjects include:
Departmental analytics — best practices
I believe IT departments should support and encourage departmental analytics efforts, where “support” and “encourage” are not synonyms for “control”, “dominate”, “overwhelm”, or even “tame”. A big part of that is:
Let, and indeed help, departments have the data they want, when they want it, served with blazing performance.
Three things that absolutely should NOT be obstacles to these ends are:
- Corporate DBMS standards.
- Corporate data governance processes.
- The difficulties of ETL.
| Categories: Business intelligence, Data mart outsourcing, Data warehousing, EAI, EII, ETL, ELT, ETLT, Predictive modeling and advanced analytics | 3 Comments |
Microsoft SQL Server 2012 and enterprise database choices in general
Microsoft is launching SQL Server 2012 on March 7. An IM chat with a reporter resulted, and went something like this.
Reporter: [Care to comment]?
CAM: SQL Server is an adequate product if you don’t mind being locked into the Microsoft stack. For example, the ColumnStore feature is very partial, given that it can’t be updated; but Oracle doesn’t have columnar storage at all.
Reporter: Is the lock-in overall worse than IBM DB2, Oracle?
CAM: Microsoft locks you into an operating system, so yes.
Reporter: Is this release something larger Oracle or IBM shops could consider as a lower-cost alternative a co-habitation scenario, in the event they’re mulling whether to buy more Oracle or IBM licenses?
CAM: If they have a strong Microsoft-stack investment already, sure. Otherwise, why?
Reporter: [How about] just cost?
CAM: DB2 works just as well to keep Oracle honest as SQL Server does, and without a major operating system commitment. For analytic databases you want an analytic DBMS or appliance anyway.
Best is to have one major vendor of OTLP/general-purpose DBMS, a web DBMS, a DBMS for disposable projects (that may be the same as one of the first two), plus however many different analytic data stores you need to get the job done.
By “web DBMS” I mean MySQL, NewSQL, or NoSQL. Actually, you might need more than one product in that area.
| Categories: Data warehousing, IBM and DB2, Microsoft and SQL*Server, Mid-range, MySQL, NoSQL, Oracle | 6 Comments |
Departmental analytics — general observations
Department-level adoption of analytic technology isn’t the exception; it’s the norm. Reasons include:
- Many analytic challenges are inherently departmental.
- In many cases, central IT control of analytics isn’t needed.
- Departments move ahead without central approval or involvement because they can.
That said, arguments for centralizing analytic technology include:
- A lot of data is used by more than one department, for example:
- Financial transactions (one or more affected departments and also the central accounting group).
- Web logs (marketing and IT/web operations).
- Departments may not have the requisite technical expertise (and it may be redundant/cost-ineffective for them to acquire it).
What’s more, there are IT best practices to support department-level analytics. Some of the key ones boil down to:
- Be flexible in your analytic DBMS support.
- Be responsive to requests for ETL.
My conclusion is that central IT should encourage (and aid) departmental analytics. Let’s look at some details.
| Categories: Data warehousing | Leave a Comment |
Splunk update
Splunk is announcing the Splunk 4.3 point release. Before discussing it, let’s recall a few things about Splunk, starting with:
- Splunk is first and foremost an analytic DBMS …
- … used to manage logs and similar multistructured data.
- Splunk’s DML (Data Manipulation Language) is based on text search, not on SQL.
- Splunk has extended its DML in natural ways (e.g., you can use it to do calculations and even some statistics).
- Splunk bundles some (very) basic, Splunk-specific business intelligence capabilities.
- The paradigmatic use of Splunk is to monitor IT operations in real time. However:
- There also are plenty of non-real-time uses for Splunk.
- Splunk is proudest of its growth in non-IT quasi-real-time uses, such as the marketing side of web operations.
As in any release, a lot of Splunk 4.3 is about “Oh, you didn’t have that before?” features and Bottleneck Whack-A-Mole performance speed-up. One performance enhancement is Bloom filters, which are a very hot topic these days. More important is a switch from Flash to HTML5, so as to accommodate mobile devices with less server-side rendering. Splunk reports that its users — especially the non-IT ones — really want to get Splunk information on the tablet devices. While this somewhat contradicts what I wrote a few days ago pooh-poohing mobile BI, let me hasten to point out:
- Splunk is used for a lot of (quasi) real-time monitoring.
- Splunk’s desktop user interfaces are, by BI standards, quite primitive.
That’s pretty much the ideal scenario for mobile BI: Timeliness matters and prettiness doesn’t.
| Categories: Business intelligence, Data models and architecture, Data warehousing, Log analysis, Specific users, Splunk, Structured documents, Web analytics | 3 Comments |
Big data terminology and positioning
Recently, I observed that Big Data terminology is seriously broken. It is reasonable to reduce the subject to two quasi-dimensions:
- Bigness — Volume, Velocity, size
- Structure — Variety, Variability, Complexity
given that
- High-velocity “big data” problems are usually high-volume as well.*
- Variety, variability, and complexity all relate to the simply-structured/poly-structured distinction.
But the conflation should stop there.
*Low-volume/high-velocity problems are commonly referred to as “event processing” and/or “streaming”.
When people claim that bigness and structure are the same issue, they oversimplify into mush. So I think we need four pieces of terminology, reflective of a 2×2 matrix of possibilities. For want of better alternatives, my suggestions are:
- Relational big data is data of high volume that fits well into a relational DBMS.
- Multi-structured big data is data of high volume that doesn’t fit well into a relational DBMS. Alternative: Poly-structured big data.
- Conventional relational data is data of not-so-high volume that fits well into a relational DBMS. Alternatives: Ordinary/normal/smaller relational data.
- Smaller poly-structured data is data for which dynamic schema capabilities are important, but which doesn’t rise to “big data” volume.
Terminology: Data mustering
I find myself in need of a word or phrase that means bring data together from various sources so that it’s ready to be used, where the use can be analysis or operations. The first words I thought of were “aggregation” and “collection,” but they both have other meanings in IT. Even “data marshalling” has a specific meaning different from what I want. So instead, I’ll go with data mustering.
I mean for the term “data mustering” to encompass at least three scenarios:
- Integrated (relational) data warehouse.
- Big bit bucket.
- Big bit stream.
Let me explain what I mean by each. Read more
| Categories: Complex event processing (CEP), Data warehousing, Investment research and trading, Sybase, Teradata | 10 Comments |
Some big-vendor execution questions, and why they matter
When I drafted a list of key analytics-sector issues in honor of look-ahead season, the first item was “execution of various big vendors’ ambitious initiatives”. By “execute” I mean mainly:
- “Deliver products that really meet customers’ desires and needs.”
- “Successfully convince them that you’re doing so …”
- “… at an attractive overall cost.”
Vendors mentioned here are Oracle, SAP, HP, and IBM. Anybody smaller got left out due to the length of this post. Among the bigger omissions were:
- salesforce.com (multiple subjects).
- SAS HPA.
- The evolution of Hadoop.
Analytic trends in 2012: Q&A
As a new year approaches, it’s the season for lists, forecasts and general look-ahead. Press interviews of that nature have already begun. And so I’m working on a trilogy of related posts, all based on an inquiry about hot analytic trends for 2012.
This post is a moderately edited form of an actual interview. Two other posts cover analytic trends to watch (planned) and analytic vendor execution challenges to watch (already up).
Clarifying SAND’s customer metrics, positioning and technical story
Talking with my clients at SAND can be confusing. That said:
- I need to revise my figures for SAND’s customer count way downward.
- SAND finally has a reasonably clear positioning.
- SAND’s product actually seems to have a lot of features.
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.
Exasol update
I last wrote about Exasol in 2008. After talking with the team Friday, I’m fixing that now. 
The general theme was as you’d expect: Since last we talked, Exasol has added some new management, put some effort into sales and marketing, got some customers, kept enhancing the product and so on.
Top-level points included:
- Exasol’s technical philosophy is substantially the same as before, albeit not with as extreme a focus on fitting everything in RAM.
- Exasol believes its flagship DBMS EXASolution has great performance on a load-and-go basis.
- Exasol has 25 EXASolution customers, all in Germany.*
- 5 of those are “cloud” customers, at hosting providers engaged by Exasol.
- EXASolution database sizes now range from the low 100s of gigabytes up to 30 terabytes.
- Pretty much the whole company is in Nuremberg.