Webcast on database diversity Wednesday April 9 2 pm Eastern
Once or twice a year, EnterpriseDB sponsors a webcast for me. The last two were super well-attended. And most people stayed to the end, which is generally an encouraging sign!
The emphasis this time is on alternatives to the market-leading DBMS. I’ll highlight the advantages of both data warehousing specialists and general-purpose mid-range DBMS (naturally focusing on the latter, given who the sponsor is). The provocative title is taken from a January, 2008 post — What leading DBMS vendors don’t want you to realize. If you read every word of this blog, there probably won’t be much new for you. 🙂 But I’d love to have you listen in and perhaps ask a question anyway!
You can register on EnterpriseDB’s webcast page, which also has an archived webcast I did for them in October, 2007.
| Categories: Database diversity, EnterpriseDB and Postgres Plus, Mid-range | 1 Comment |
Netezza’s April Fool press release
Short and cute. Even makes a genuine marketing point (low power consumption), and ties into past marketing gimmicks (they’ve played Pimp My SPU in the past, with dramatic paint jobs).
Netezza Corporation (NYSE Arca: NZ), the global leader in data warehouse and analytic appliances, today introduced a limited-edition range of its award-winning Netezza system. Expected to become an instant industry collectible, the systems can now be purchased in a variety of color finishes – pink, blue, red or silver. The standard gun-metal gray unit will continue to be the default option for orders requiring eight or more units, to ensure availability.
Affectionately known as ‘the Netezza’ by customers and partners, the systems not only offer unparalleled processing performance, but the secret sauce of its innovative design is also leading the way in effective power and cooling management – making it a truly green option for any data center.
Not earth-shaking — even if it purports to be earth-saving — but unless I’ve overlooked a biggie, there isn’t much competition this rather lame April Fool’s year.
| Categories: Data warehouse appliances, Data warehousing, Humor, Netezza | 5 Comments |
Disruption versus chasm crossing in the database market
The 451 Group just released a report on open source DBMS adoption. In a blog post announcing same, Matthew Aslett wrote (emphasis mine):
you only have to look at the comparative revenues of the open source and proprietary vendors to see that there is a vast chasm to be crossed.
“Chasm” memes were introduced by Geoffrey Moore, founder of the Chasm Group and author of Crossing the Chasm. His defining example was Oracle, and the database market in general. The core insight was that platform markets get to tipping points, after which the leaders have tremendous advantages that make them tend to remain leaders for a good long time.
The sequel to “chasm” theory is Clayton Christensen’s “disruption” rubric, popularized in The Innovator’s Dilemma. I’ve argued previously that the DBMS market is being disrupted, in both the ways that Christensen records: Read more
| Categories: Data warehouse appliances, Open source | 1 Comment |
XML versus sparse columns in variable schemas
Simon Sabin makes an interesting point: If you can have 30,000 columns in a table without sparsity management blowing up, you can handle entities with lots of different kinds of attributes. (And in SQL Server you can now do just that.) The example he uses is products — different products can have different sets of possible colors, different kinds of sizes, and so on. An example I’ve used in the past is marketing information — different prospects can reveal different kinds of information, which may have been gathered via non-comparable marketing programs.
I’ve suggested this kind of variability as a reason to actually go XML — you’re constantly adding not just new information, but new kinds of information, so your fixed schema is never up to date. But I haven’t detected many actual application designers who agree with me …
| Categories: MySQL, Structured documents, Theory and architecture | 3 Comments |
The illuminate guys have a CTO blog
If you want to know more about illuminate’s data warehouse offerings, CTO Joe Foley has a blog. A good starting point might be the post on value-based storage. Two key points seem to be:
The VBS also provides some data access features that can not be duplicated in any other structure. A search can be executed starting with a data value in the pool. By going from the value pool back to the index, it is possible to quickly locate every use of the value wherever is may be used in the logical record structures.
which makes sense, and
This structure also enables our incremental query capability. As the result of a query, the database returns a set of instance identifiers rather than a set of records. This is because there are no records, only pointers and values. With the response being a set of pointers, it is a simple matter to perform the next query step and then get the union or difference between the two sets of pointers for the result of the second query step. This process can be continued indefinitely with the result set shrinking or growing as the new results are merged with the old.
which still sounds like gobbledygook to me. Read more
| Categories: Analytic technologies, Business intelligence, Data warehousing, illuminate Solutions | Leave a Comment |
iLuminate’s correlation/associative approach to data warehousing
illuminate Solutions (small “i”) is an interesting little company, still rough around the edges. (E.g., the Press Release Archive page at i-lluminate.com says, in its entirety, “We are in the process of loading our historical press releases. Please check back the second week in March!” And I only got that much when I corrected an obvious typo in the URL in the menu bar.) According to CTO Joe Foley, illuminate has 37 or so employees, and 40+ customers, ¾ of whom are in their home country of Spain and ½ the rest of whom are in Latin America. Now they’re entering the US.
illuminate’s basic idea is one I’ve heard before, but mainly from companies with more of a search orientation*, such as Attivio: Take a collection of tables, create a big inverted index on all the values in all columns at once, and do queries on that. This, illuminate claims, obviates all sorts of database design problems and similar hassles you otherwise might have. illuminate’s buzzword for all this is “CDBMS”, where the “C” stands for correlation. The actual CDBMS product is called iLuminate; related business intelligence tools are called iCorrelate and iAnalyze. What iLuminate actually indexes is a token that holds four pieces of information: Instance identifier, table identifier, column identifier, and value. Read more
| Categories: Analytic technologies, Business intelligence, Data warehousing, illuminate Solutions | 2 Comments |
Pervasive is also pursuing simplicity and SaaS integration
I blogged recently about Cast Iron Systems, a simplicity-oriented data integration appliance vendor that is increasingly focusing on the SaaS market. Well, Pervasive Software is doing something similar.
Via Data Integrator, Pervasive is a leader in the low-cost integration market, with revenue split about 50/25/25 between direct sales, ISVs, and SaaS. Pervasive fondly believes that its products cost half as much as Cast Iron’s, and wind up taking no more installation effort when you factor in Pervasive’s broader capabilities in areas such as workflow. However, there’s some doubt as to whether this is apples-to-apples. Cast Iron does include hardware, after all, and as Pervasive itself points out, Cast Iron will bundle some professional services into a sale if you ask nicely.
Two things are new. Read more
| Categories: Cloud computing, EAI, EII, ETL, ELT, ETLT, Pervasive Software, Software as a Service (SaaS) | 5 Comments |
Elastra launched today
At Elastra’s request, I didn’t write further about them back when I was interested in doing so. But you can go find out about them yourself. Basically, their secret sauce is that they write deployment instructions in a few hundred lines of two proprietary markup languages. They have ambitions beyond DBMS, and beyond the Amazon cloud.
According to their slides, they have 13 paying customers.
| Categories: Cloud computing, Elastra | 1 Comment |
The eBay analytics guys have a blog now
Oliver Ratzesberger and his crew have started a blog, focusing on xldb analytics. Naturally, one of the early posts gives a quick overview of their system stats. Highlights include:
Incoming data volumes exceed 40TB per day, with more than 10^11 new items/lines/records being added per day. Our analytical processing infrastructure exceeds 6PB of physical storage with over 2.9PB(1.4+1.5) in our largest cluster.
We leverage compression technologies wherever possible and are achieving compression ratios as high as 99% on our highest volume data feeds.
On any given day our massive parallel systems process more than 27PB of data, not factoring in various levels of caches that serve similar activities or processes and reduce the amount of physical IOs significantly.
We execute millions of requests on a daily basis, spanning from near realtime highly localized access to enormous jobs that span 100s of TB in a single or series of models.
| Categories: eBay, Specific users | Leave a Comment |
GridSQL: What EnterpriseDB is and is not doing in Postgres-based MPP data warehousing
While talking with EnterpriseDB about today’s Postgres Plus announcements, I took the chance to clear up a point of confusion. Somebody told Seth Grimes that EnterpriseDB is out to compete with Greenplum, but that person was wrong. EnterpriseDB fondly hopes to manage multi-terabyte data warehouses, just as Oracle and Microsoft do with their respective general-purpose DBMS. However, EnterpriseDB is not going after the 10s-100s of terabytes sized DBMS that are the province of specialists such as Greenplum, Teradata, Netezza, or columnar DBMS vendors.
Even so, in GridSQL EnterpriseDB does seem to be open-sourcing MPP shared-nothing basics. There’s a lightweight optimizer that does a little (but only a little) more to minimize data movement beyond just optimizing queries on each node. And GridSQL knows how to replicate small tables across each node, a key aspect of many MPP designs. (Partition your facts; replicate your dimensions.)