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:
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 |
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 |
Kickfire’s FPGA-based technical strategy
Kickfire’s basic value proposition is that, if you have a data warehouse in the 100s of gigabytes, they’ll sell you – for $32,000 – a tiny box that solves all your query performance problems, as per the Kickfire spec sheet. And Kickfire backs that up with a pretty cool product design. However, thanks in no small part to what was heretofore Kickfire’s penchant for self-defeating secrecy, the Kickfire story is not widely appreciated.
Fortunately, Kickfire is getting over its secrecy kick. And so, here are some Kickfire technical basics.
- Kickfire is MySQL-based, with all the SQL functionality and lack of functionality that entails.
- The Kickfire/MySQL DBMS is columnar, with the usual benefits in compression and I/O reduction.
- Kickfire is based on FPGAs (Field-Programmable Gate Arrays).
- The Kickfire DBMS is ACID-compliant.
- Kickfire runs only as a single-box appliance.
- While Kickfire earlier estimated that, at least for data sets that compressed well, a Kickfire box could hold 3-10 terabytes of user data, more recent figures I’ve heard from Kickfire have been in the 1-1 /2 terabyte range. (Edit: Karl Van Der Bergh subsequently wrote in to say that the 1 1/2 TB is raw disk figure, not user data.)
The new information there is that Kickfire relies on an FPGA; Read more
| Categories: Analytic technologies, Columnar database management, Data warehouse appliances, Data warehousing, Database compression, Kickfire, MySQL, Theory and architecture | 16 Comments |
Sorting out Netezza and Oracle Exadata data warehouse appliance pricing
Netezza recently announced a new generation of data warehouse appliance called TwinFin. TwinFin’s clearest stated list price is “a little under $20,000 per terabyte of user data,” which in my opinion immediately became the new industry reference point for discussing prices in the data warehouse appliance category. Vigorous discussion ensued, especially in the comment thread to the first of the two posts linked above. Here’s some followup.
Netezza should not have claimed a “10-15X price/performance improvement,” based on a 3-5X performance improvement and a 3X decrease in price/terabyte, and I should have grilled Netezza harder when it first made the claim. In fact, there is no unit of performance that you can, in a reasonable blended average, get 10-15X more of per dollar in TwinFin than you can in the predecessor NPS series.
| Categories: Data warehousing, Exadata, Netezza, Oracle, Pricing | 19 Comments |
What does Netezza do in the FPGAs anyway, and other questions
The news of Netezza’s new TwinFin product family has generated a lot of comments and questions, some pretty reasonable, some quite silly. E.g., I’ve seen it suggested privately or publicly that
- Netezza’s older products only handle one query at a time (nonsense, and I’m going to loyally protect the identity of the person who emailed that odd suggestion to me)
- A Netezza node can be a single point of failure (also nonsense, although performance degradation from a node failure might be considerable)
- Netezza has a cache consistency problem (also hardly true, except insofar as it’s an issue to overcome in future development as Netezza moves toward parallelizing bulk loads, transactional updates, and/or trickle feeds).
Netezza’s Phil Francisco addressed some points of this nature in a recent blog post.
More reasonable is the question:
Now that Netezza has changed its architecture, what are all those FPGAs (Field-Programmable Gate Arrays) being used for anyway?
The short answer is: Read more
| Categories: Data warehouse appliances, Data warehousing, Netezza, Theory and architecture | 6 Comments |
Dataupia is officially for sale
Dataupia marketing VP Samantha Stone — who by the way has been one heck of a trooper through Dataupia’s troubles — is joining the exodus from the company. General graciousness aside, the heart of Samantha’s farewell email reads:
Unfortunately, we have had to reduce our burn rate as we seek an acquirer for our technology.
We have a group of loyal employees remaining on staff focused on current production customers and the acquisition efforts.
As part of the most recent staff reductions I will be leaving Dataupia.
Two years ago I wrote:
[Dataupia would] make a great acquisition for a BI company or DBMS vendor who could then say “Oh, no, this isn’t a DBMS appliance – it’s merely a data warehouse accelerator.” When you look at it that way, their chances of prospering look distinctly higher.
But at this point I think there probably would be more appealing ways for those vendors to meet the same needs.
| Categories: Data warehouse appliances, Data warehousing, Dataupia, Emulation, transparency, portability | 14 Comments |
FlexStore and the rest of Vertica 3.5
Today, Vertica is announcing its 3.5 release, timed in line with a TDWI conference. Vertica 3.5 is scheduled to go into beta test in mid-August and be released to general availability in early October. Vertica 3.5 highlights include:
- Vertica/MapReduce integration, which I’m covering in a separate post.
- A new storage architecture called Vertica FlexStore, which seems to boil down essentially to three things:
- A sort of row/column hybridization — Vertica would probably prefer to call it something like a column clustering feature — that I’m also covering in a separate post.
- The beginnings of a multi-temperature capability, somewhat akin to Teradata Virtual Storage.
- Enhancements to Vertica’s WOS (Write-Optimized Store, the in-memory part of Vertica that first receives updates). I don’t understand WOS architecture well enough to write about that yet.
- Load-balancing, to route queries evenly among Vertica nodes — probably just round-robin — rather than having them just be processed by whichever node happens to receive them.
PAX Analytica? Row- and column-stores begin to come together
Column-store proponents are prone to argue, in effect, that the only reason to implement an analytic DBMS with row-based storage is laziness. Their case generally runs along the lines:
- Analytic queries commonly return only a fraction of all possible columns.
- Only returning the columns needed
- Saves I/O
- Saves cache space
- Reduces processing
- Facilitates compression
- Presumably all those row-based MPP vendors just went row-based because they had a fine row-based DBMS (usually but not always PostgreSQL) to build on.
Pushbacks to this argument from row-based vendors include:
- Yes, but it’s harder to update a column store
- Yes, but there are more steps to retrieving a bunch of columns than there are to retrieving the same information from row stores
| Categories: Analytic technologies, Columnar database management, Data warehousing, Theory and architecture, VectorWise, Vertica Systems | 11 Comments |
Vertica’s version of MapReduce integration
I talked with Omer Trajman of Vertica Monday night about Vertica’s MapReduce integration, part of its Vertica 3.5 release. Highlights included:
- By “integrating Vertica and MapReduce,” Vertica means “integrating Vertica and Hadoop.”
- Vertica’s Hadoop integration is based on Cloudera’s DBInputFormat.
- Omer called out for me several features of Vertica’s Hadoop integration that didn’t just come from Cloudera, namely:
- Cloudera’s DBInputFormat assumes the database runs on a single computer, or a single head node of an MPP system. Vertica’s technology, however, runs on peer parallel nodes with no head, and so Vertica adapted the DBInputFormat technology accordingly.
- Vertica lets you push down Map functions to the database. Omer reports a roughly even division among users and prospects between those who want to do this and ones who don’t.
- Vertica lets you do Reduce functions (or Map functions, if you don’t push them down to the database) on a separate cluster than you run the database software. Vertica asserts that its customers and prospects all want to do this. Right here is the big difference between Vertica’s MapReduce integration and Aster’s or Greenplum’s. (Aster would also say that Vertica’s weaker MapReduce/SQL programming integration is a big difference as well.)
- Indeed, Vertica lets you Reduce into a different DBMS than Vertica, if you choose.
- Vertica gives you flexibility on the size of the Map and Reduce clusters. Omer agreed with me when I said there were some limits on how fast one can add or subtract nodes in a Vertica grid, because there’s data redistribution involved. But one can add/change/delete Hadoop clusters extremely quickly.
Apparently, the use cases for Vertica/Hadoop integration to date lie in algorithmic trading and two kinds of web analytics. Specifically: Read more