Teradata’s major vertical markets in 2007
From a May, 2008 earnings conference call transcript:
- telecommunication, media and entertainment industry is 28%;
- financial services is 24%;
- retail is 19% of our revenues last year;
- manufacturing 9%;
- government 7%;
- travel and transportation 6%;
- and healthcare 5%.
| Categories: Application areas, Data warehouse appliances, Data warehousing, Telecommunications, Teradata | Leave a Comment |
Teradata/Netezza/Tesco kerfuffle
Netezza evidently put out a press release bragging of a competitive replacement of Teradata at UK retailing giant Tesco. That press release cannot be now found on Netezza’s site, but it lives on elsewhere. Meanwhile, Teradata has put out a press release in which Tesco is quoted emphatically contradicting what it is quoted as saying in the Netezza press release. While I haven’t discussed this with Netezza, my guess is that somebody there got a little overenthusiastic in advance of their user conference next week and thought they’d gotten a permission they really hadn’t.
Beyond that, I’d note that the Netezza quote made reference to around 25 heavy analytical users, while the Teradata quote talked of 8000 people across more than 2000 suppliers.
| Categories: Data warehouse appliances, Data warehousing, Memory-centric data management, Netezza, Oracle, Specific users, Teradata | 2 Comments |
The layered messaging marketing model as applied to Netezza
I just put up a post claiming that enterprise IT marketing arguments commonly boil down to one of two layered messaging templates. Let’s test how that claim applies to one of the most innovative technology companies of this decade: Netezza.
| Categories: Netezza | 2 Comments |
SANs vs. DAS in MPP data warehousing
Generally speaking:
- SANs (Storage Area Networks) are pulling ahead of DAS (Direct Attached Storage).
- Much of the growth in storage is due to data warehousing.
- MPP (Massively Parallel Processing) is pulling ahead of SMP (Symmetric MultiProcessing) for high-end data warehousing.
- MPP architectures are commonly shared-nothing.
- Shared-nothing entails DAS.
But if you think about it, those facts don’t exactly add up. Read more
| Categories: Calpont, Parallelization, Storage, Vertica Systems | 24 Comments |
Dividing the data warehousing work among MPP nodes
I talk with lots of vendors of MPP data warehouse DBMS. I’ve now heard enough different approaches to MPP architecture that I think it might be interesting to contrast some of the alternatives.
| Categories: Aster Data, Calpont, Exasol, Greenplum, Parallelization, Theory and architecture, Vertica Systems | 22 Comments |
Three different implementations of MapReduce
So far as I can see, there are three implementations of MapReduce that matter for enterprise analytic use – Hadoop, Greenplum’s, and Aster Data’s.* Hadoop has of course been available for a while, and used for a number of different things, while Greenplum’s and Aster Data’s versions of MapReduce – both in late-stage beta – have far fewer users.
*Perhaps Nokia’s Disco or another implementation will at some point join the list.
Earlier this evening I posted some Mike Stonebraker criticisms of MapReduce. It turns out that they aren’t all accurate across all MapReduce implementations. So this seems like a good time for me to stop stalling and put up a few notes about specific features of different MapReduce implementations. Here goes. Read more
| Categories: Aster Data, Greenplum, MapReduce | 3 Comments |
Mike Stonebraker’s counterarguments to MapReduce’s popularity
In response to recent posting I’ve done about MapReduce, Mike Stonebraker just got on the phone to give me his views. His core claim, more or less, is that anything you can do in MapReduce you could already do in a parallel database that complies with SQL-92 and/or has PostgreSQL underpinnnings. In particular, Mike says: Read more
| Categories: Data warehousing, MapReduce, Michael Stonebraker, PostgreSQL | 5 Comments |
More data on data warehouse sizes and issues
I spoke today with Paul Barth and Randy Bean of consultancy NewVantage Partners. The core of NewVantage’s business seems to be helping large enterprises (especially financial services) with their data warehouse strategies. Takeaways — none of which should shock regular readers of DBMS2 — included:
- Administrative cost and difficulty are often the single biggest issue in selecting analytic DBMS products.
- Oracle hits a wall around 10 terabytes of user data. The one customer NewVantage can think of with an Oracle data warehouse over 10 terabytes is fleeing Oracle for Netezza.
- NewVantage says that very specialized data warehouses on Oracle could conceivably be larger than that.
- NewVantage does have a customer on DB2/UDB in the 30-40 terabyte range. That customer does a lot of careful tuning to make it work.
- About 15% of NewVantage’s customers use Netezza. Few if any use newer analytic DBMS (but I got the sense more will soon). The rest rely on “traditional” DBMS, a group that includes Teradata.
| Categories: Data warehousing, IBM and DB2, Netezza, Oracle | 1 Comment |
Introduction to Aster Data and nCluster
I’ve been writing a lot about Greenplum since a recent visit. But on the same trip I met with Aster Data, and have talked with them further since. Let me now redress the balance and outline some highlights of the Aster Data story.
| Categories: Analytic technologies, Aster Data, Data warehousing, Parallelization, Specific users | 4 Comments |
Estimating user data vs. spinning disk
There’s a lot of confusion about how to measure data warehouse database size. Major complicating factors include:
- Indexes and temporary working space. That’s what I emphasized a couple of years ago in my post about Expansion Ratios.
- Compression. I write about database compression a lot.
- Disk redundancy. I usually gloss over that one, but I’ll try to make amends in this post.
- Replication other than that which is primarily designed for redundancy. I usually gloss over that one too, and I think it’s safe to continue doing so. That’s because data warehouse replication – at least in most of the system architectures I know of – generally divides into three categories:
- a lot like redundancy
- a lot like an index
- only a minor issue (e.g., when small fact tables are replicated across each node of an MPP cluster)
Greenplum’s CTO Luke Lonergan recently walked me through the general disk usage arithmetic for Greenplum’s most common configuration (Sun Thors*, configured to Raid 10). I found it pretty interesting, and a good guide to factors that also affect other systems, from other vendors.