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:
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: Data warehousing, Investment research and trading, Streaming and complex event processing (CEP), Sybase, Teradata | 12 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.
StreamBase LiveView — push-based real-time BI
My clients at StreamBase are coming out with a new product line called LiveView, and I agreed they could launch it via this blog. Key points about StreamBase LiveView Version 1.0 include:
- LiveView is a business intelligence and alerting suite built on/in the rest of StreamBase’s technology, meant to operate on streaming data.
- LiveView is positioned by StreamBase as having a true push event-driven architecture rather than pull/poll.
- StreamBase LiveView is designed to query in-memory data and then have the results change in real time as the data set changes.
- The LiveView user interface is a rapidly changing work in progress.
- LiveView has other Version 1 limitations as well
- LiveView is targeted squarely at StreamBase’s financial trading core market until some of the Version 1 limitations are lifted.
The basic StreamBase LiveView pipeline goes something like: Read more
| Categories: Business intelligence, Data warehousing, Memory-centric data management, StreamBase, Streaming and complex event processing (CEP) | 2 Comments |
What those nested data structures are about
As I’ve noted before, the very big web companies have an issue with nested data structures. The subject came up in XLDB talks yesterday too, so my big goal for lunch was to finally understand what was being talked about. Sitting at a table full of eBay and LinkedIn folks turned out to be a good tactic.
The explanation was led by Oliver Ratzesberger, late of eBay* and progenitor of eBay’s Singularity project. In simplest terms, one event can spawn a lot of event attribute information, perhaps in the form of name-value pairs, which it then makes sense to store together in some way. The example Oliver dwelled on was that, on any given web page, there can be 100+ pieces of information to record, including:
- All 50 search results you were shown, and their positions in the search rankings.
- Every ad, image, or graphical element.
- An ID as to which test you were participating in (every page you see on eBay has some element being tested).
*Edit: Oliver subsequently moved on to Sears and then Teradata.
There are several reasons why one might wish to store this information in ways that grieve relational purists. First, reconstructing all this information via joins would be brutally expensive. What’s more, reconstructing all this information via joins could be impractical. Some comes from third party ad servers, which might not reproduce the same ads upon demand. Other is in the form of rankings, which can’t always be reliably reproduced from one query to the next. (That’s just one of several reasons text search and relational DBMS are an awkward fit.)
Also, there’s a strong dynamic schema flavor to these databases. The list of attributes for one web click might be very different in kind from the list for the next page. Forcing that kind of variability into a fixed relational schema, while theoretically possible, doesn’t necessarily make a lot of sense.
| Categories: Data models and architecture, Data warehousing, eBay, Log analysis, Web analytics | 7 Comments |
Commercial software for academic use
As Jacek Becla explained:
- Academic scientists like their software to be open source, for reasons that include both free-like-speech and free-like-beer.
- What’s more, they like their software to be dead-simple to administer and use, since they often lack the dedicated human resources for anything else.
Even so, I think that academic researchers, in the natural and social sciences alike, commonly overlook the wealth of commercial software that could help them in their efforts.
I further think that the commercial software industry could do a better job of exposing its work to academics, where by “expose” I mean:
- Give your stuff to academics for free.
- Call their attention to your free offering.
Reasons to do so include:
- Public benefit. Scientific research is important.
- Training future customers. There’s huge academic/commercial crossover, especially as students join the for-profit workforce.
| Categories: Business intelligence, Data warehousing, Infobright, Petabyte-scale data management, Predictive modeling and advanced analytics, Scientific research | 7 Comments |
Text data management, Part 3: Analytic and progressively enhanced
This is Part 3 of a three post series. The posts cover:
- Confusion about text data management.
- Choices for text data management (general and short-request).
- Choices for text data management (analytic).
I’ve gone on for two long posts about text data management already, but even so I’ve glossed over a major point:
Using text data commonly involves a long series of data enhancement steps.
Even before you do what we’d normally think of as “analysis”, text markup can include steps such as:
- Figure out where the words break.
- Figure out where the clauses and sentences break.
- Figure out where the paragraphs, sections, and chapters break.
- (Where necessary) map the words to similar ones — spelling correction, stemming, etc.
- Figure out which words are grammatically which parts of speech.
- Figure out which pronouns and so on refer to which other words. (Technical term: Anaphora resolution.)
- Figure out what was being said, one clause at a time.
- Figure out the emotion — or “sentiment” — associated with it.
Those processes can add up to dozens of steps. And maybe, six months down the road, you’ll think of more steps yet.
| Categories: Data warehousing, Hadoop, NoSQL, Text | 4 Comments |