Teradata and Netezza are doing MapReduce too
Netezza told me a while ago that it planned to introduce MapReduce, and agreed yesterday this was no longer NDAed. Stephen Brobst of Teradata* let slip at XLDB that Teradata has MapReduce too, apparently implemented but not yet generally available.
I don’t have details in either case. Netezza and Teradata evidently aren’t taking MapReduce as seriously as Aster Data, or even Greenplum or Vertica. But MapReduce has become pretty much of a “checkmark” item for large-database analytic DBMS vendors even so.
*Technically, Brobst is not and never has been a Teradata employee — but he’s widely and correctly regarded as being “of Teradata” even so. 🙂
| Categories: Data warehousing, MapReduce, Netezza, Teradata | 6 Comments |
SAS on Netezza and other Netezza extensibility
I chatted with SAS CTO Keith Collins yesterday about the new SAS/Netezza in-database parallel data mining scoring offering. My impression is that this is very similar to SAS’ current Teradata support, notwithstanding SAS’ and Teradata’s apparent original intention of offering in-database modeling by now as well.
I gather this is a big performance-enhancing deal, just as it is for SPSS or Oracle’s own data mining over Oracle. However, I must confess to not yet understanding why. That is, I don’t know what’s so complicated about data mining scoring algorithms that makes hand-coding them in SQL particularly forbidding. My naive view of data mining is that you do a big regression to get a bunch of weights, and the resulting scoring algorithm is a linear combination of a few dozen variables. Evidently, that’s not quite right.
Anyhow, it turns out that SAS held off on this work until it could be done for TwinFin. That’s largely because TwinFin lets partners write code on Intel CPUs, while previously they had to write in C for Netezza’s FPGAs. I got a similar sense from at least one other Netezza partner as well.
| Categories: Data warehouse appliances, Data warehousing, Netezza, Predictive modeling and advanced analytics, SAS Institute | 5 Comments |
Oracle Exadata hybrid columnar compression
Oracle Database 11g Release 2 is out, and as usual I wasn’t briefed — perhaps because Oracle is more scared than its competitors are of hard questions, perhaps for some other reason entirely.* Anyhow, Oracle Database 11 Release 2 contains an Exadata-only feature called hybrid columnar compression. The Oracle Database 11g Release 2 white paper says “data is grouped, ordered, and stored one column at a time.” But Kevin Closson clarifies:
The word hybrid is important.
Rows are still used. They are stored in an object called a Compression Unit. Compression Units can span multiple blocks. Like values are stored in the compression unit with metadata that maps back to the rows.
So, “hybrid” is the word. But, none of that matters as much as the effectiveness. This form of compression is extremely effective.
That sounds a whole lot like PAX. Specifically, in Oracle’s case I would guess “hybrid columnar compression” provides the compression benefits of column stores, but not column stores’ I/O benefits, and also not any kind of in-memory compression. Read more
| Categories: Columnar database management, Data warehousing, Database compression, Exadata, Oracle, Theory and architecture | 20 Comments |
Teradata has over 100 appliances in production
I recently wrote that Teradata had gotten serious about appliance product lines, and had non-trivial sales figures for them. In a press release today, Teradata is now explicitly saying (emphasis mine):
Teradata now has more than 100 appliances in production, including the Data Mart Appliance 551, the Data Warehouse Appliance 2550, and the Extreme Data Appliance 1550, which complement the core platform, the Teradata Active Enterprise Data Warehouse 5550.
The breakdowns on that are NDA, and anyhow I can’t find them immediately in my notes.* But if memory serves — while a lot of those appliances are used for test and development, a whole other lot of them are used to do actual production query-answering work. (Edit: Memory turned out to be wrong.) Read more
| Categories: Data warehouse appliances, Data warehousing, Market share and customer counts, Teradata | 2 Comments |
Sybase IQ technical highlights
General highlights of the Sybase IQ technical story include:
- Sybase IQ is an analytic DBMS with a columnar/column-store architecture
- Unlike most analytic DBMS, Sybase IQ has a shared-disk architecture.
- The Sybase IQ indexing story is a bit complicated, with a bunch of different index kinds. Most are focused on columns with low cardinality, and it least in some cases are a lot like bitmaps. (Sybase IQ when first introduced was a pure bitmap index product, with a single index type “Fast Project”.) But one index kind, “High Group” — designed for columns with high cardinality – is an exception to most generalities about other Sybase IQ index kinds, and instead is more akin to a b-tree.
- Unlike Vertica, Sybase stores each column of data only once. I don’t see how it would make sense to have multiple indexes on the same column, but I didn’t actually ask whether doing so is possible or common.
- Sybase estimates that Sybase IQ requires ¼ the DBA effort of, say, Oracle. (Frankly, that’s not a particularly good figure.) Obviously, this is just a broad-brush average.
- Sybase recently repurposed an acquired ETL tool to be focused on Sybase IQ. IQ of course also works with various third-party tools, certified or otherwise.
- Sybase’s Power Designer CASE (Computer-Aided Software Engineering)/database design tool works with Sybase IQ.
- Sybase is proud of Sybase IQ’s new in-database analytics capabilities, but I haven’t yet grasped what, if anything, is differentiated about them.
- Sybase has an ILM (Information Lifecycle Management) story built around the point that different columns can be stored on different kinds of media.
Highlights of the Sybase IQ compression story include: Read more
| Categories: Analytic technologies, Columnar database management, Data warehousing, Database compression, EAI, EII, ETL, ELT, ETLT, Sybase, Theory and architecture | 11 Comments |
Sybase IQ business notes
As specialized analytic DBMS go, Sybase is near the top of the charts both in age (Sybase IQ was first introduced in the mid 1990s) and adoption. That’s even more true, of course, if we restrict the discussion strictly to columnar DBMS, aka column stores. Basic Sybase IQ adoption claims include:
- >1500 users
- >3000 installations (Sybase has variously cited 2.1 and 2.5+ as the installation/user ratio)
- At least ~50-60 installations with >5 terabytes of user data
Note that 98% of Sybase IQ installations are under 5 terabytes; the heart of Sybase IQ’s business is the sub-terabyte data warehouse market.* Read more
| Categories: Analytic technologies, Data mart outsourcing, Data warehousing, Investment research and trading, Sybase | 3 Comments |
Teradata highlights some analytic use cases
A couple of slides in my recent briefing on Teradata’s Active Enterprise Data Warehouse Story contained long lists of analytic use cases, at a finer level of granualarity than I’m focusing on for a September speaking tour. I think they’re interesting to pass along. Read more
| Categories: Analytic technologies, Data warehousing, Teradata | 2 Comments |
Teradata’s Active Enterprise Data Warehouse story
Teradata used to tell a one-size-fits-all Enterprise Data Warehouse (EDW) story. That’s no longer the case. Last year, Teradata introduced a range of products. I think Teradata is serious about selling its full product range, and by now has achieved buy-in from its sales force for that strategy. I base these beliefs on data points such as:
- Teradata says so, repeatedly and persuasively.
- At least in passing, Teradata cites non-trivial sales figures for the appliance product lines.
- Competitors are less unanimous in asserting that Teradata’s lower-end products are presented on just a bait-and-switch basis.
But that raises the question: How does Teradata pitch the advantages of its top-end product line these days? At least at the corporate level, the answer seems to focus less on the “EDW” concept than it used to, and more on “Active.” Teradata – which actually has been talking about “Active Data Warehousing” for about a decade — indeed calls its top-end 55xx series the “Teradata Active Enterprise Data Warehouse.”
Teradata proudly told me that it has >100 customers who have truly adopted an “Active” EDW. When we discussed what that meant, supported by a whole lot of named examples, it became clear that “Active” data warehousing: Read more
| Categories: Analytic technologies, Data warehousing, Teradata | 6 Comments |
Social network analysis, aka relationship analytics
A number of applications lend themselves to graph-oriented analytics, including:
- Finding bad guys (national intelligence)
- Finding bad guys (anti-fraud)
- Data mining the social graph (e.g., for advertising optimization on social networks, or to identify influencers)
There are plenty more graph-oriented applications, of course, such as the identification of biochemical pathways. But I want to focus for now on ones like those on my list. My key points are:
- There are Big Data problems that lend themselves to graphical data models.
- So far as I can tell, the database management community isn’t doing enough to address them. (If I’m wrong about that, please tell me. I plan to arrive in Lyon for VLDB/XLDB Wednesday of next week, and of course I can always be reached by email.)
Here’s what I mean. Read more
| Categories: Analytic technologies, Cogito and 7 Degrees, Data models and architecture, Data types, RDF and graphs, Theory and architecture | 21 Comments |
Bottleneck Whack-A-Mole
Developing a good software product is often a process of incremental improvement. Obviously, that can happen in the case of feature addition or bug-fixing. Less obviously, there’s also great scope for incremental improvement in how the product works at its core.
And it goes even further. For example, I was told by a guy who is now a senior researcher at Attivio: “How do you make a good speech recognition product? You start with a bad one and keep incrementally improving it.”
In particular, I’ve taken to calling the process of enhancing a product’s performance across multiple releases “Bottleneck Whack-A-Mole” (rhymes with guacamole). This is a reference to the Whack-A-Mole arcade game,* the core idea of which is:
- An annoying mole pops its head up.
- You whack it with a mallet.
- Another pops its head up.
- You whack that one.
- Repeat, as mole_count increments to a fairly large integer.
| Categories: Data warehousing, Exadata, Fun stuff, Netezza, Oracle, Theory and architecture | 24 Comments |