Vertica Systems
Analysis of columnar data warehouse DBMS vendor Vertica Systems. Related subjects include:
Vertica update
Another TDWI conference approaches. Not coincidentally, I had another Vertica briefing. Primary subjects included some embargoed stuff, plus (at my instigation) outsourced data marts. But I also had the opportunity to follow up on a couple of points from February’s briefing, namely:
Vertica has about 35 paying customers. That doesn’t sound like a lot more than they had a quarter ago, but first quarters can be slow.
Vertica’s list price is $150K/terabyte of user data. That sounds very high versus the competition. On the other hand, if you do the math versus what they told me a few months ago — average initial selling price $250K or less, multi-terabyte sites — it’s obvious that discounting is rampant, so I wouldn’t actually assume that Vertica is a high-priced alternative.
Vertica does stress several reasons for thinking its TCO is competitive. First, with all that compression and performance, they think their hardware costs are very modest. Second, with the self-tuning, they think their DBA costs are modest too. Finally, they charge only for deployed data; the software that stores copies of data for development and test is free.
| Categories: Analytic technologies, Columnar database management, Data warehousing, Database compression, Pricing, Vertica Systems | 10 Comments |
Outsourced data marts
Call me slow on the uptake if you like, but it’s finally dawned on me that outsourced data marts are a nontrivial segment of the analytics business. For example:
- I was just briefed by Vertica, and got the impression that data mart outsourcers may be Vertica’s #3 vertical market, after financial services and telecom. Certainly it seems like they are Vertica’s #3 market if you bundle together data mart outsourcers and more conventional OEMs.
- When Netezza started out, a bunch of its early customers were credit data-based analytics outsourcers like Acxiom.
- After nagging DATAllegro for a production reference, I finally got a good one — TEOCO. TEOCO specializes in figuring out whether inter-carrier telcom bills are correct. While there’s certainly a transactional invoice-processing aspect to this, the business seems to hinge mainly around doing calculations to figure out correct charges.
- I was talking with Pervasive about Pervasive Datarush, a beta product that lets you do super-fast analytics on data even if you never load it into a DBMS in the first place. I challenged them for use cases. One user turns out to be an insurance claims rule-checking outsourcer.
- One of Infobright’s references is a French CRM analytics outsourcer, 1024 Degres.
- 1010data has built up a client base of 50-60, including a number of financial and retail blue-chippers, with a soup-to-nuts BI/analysis/columnar database stack.
- I haven’t heard much about Verix in a while, but their niche was combining internal sales figures with external point-of-sale/prescription data to assess retail (especially pharma) microtrends.
To a first approximation, here’s what I think is going on. Read more
PostgreSQL can be used in a lot of different ways
The relational DBMS industry is filled with startups. In some way or other, most of them are based on or make use of the open source project PostgreSQL. (Not all, of course; exceptions include DATAllegro and Infobright, which are based on Ingres and MySQL respectively.) But how they use PostgreSQL varies greatly. Read more
| Categories: EnterpriseDB and Postgres Plus, Greenplum, Open source, PostgreSQL, Vertica Systems | 10 Comments |
Mike Stonebraker may be oversimplifying data warehousing just a tad
Mike Stonebraker has now responded to the second post in my five-part database diversity series. Takeaways and rejoinders include: Read more
| Categories: Analytic technologies, Columnar database management, Data warehousing, Database diversity, Michael Stonebraker, Theory and architecture, Vertica Systems | 2 Comments |
Load speeds and related issues in columnar DBMS
Please do not rely on the parts of the post below that are about ParAccel. See our February 18 post about ParAccel instead.
I’ve already posted about a chat I had with Mike Stonebraker regarding Vertica yesterday. I naturally raised the subject of load speed, unaware that Mike’s colleague Stan Zlodnik had posted at length about load speed the day before. Given that post, it seems timely to go into a bit more detail, and in particular to address three questions:
- Can columnar DBMS do operational BI?
- Can columnar DBMS do ELT (Extract-Load-Transform, as opposed to ETL)?
- Are columnar DBMS’ load speeds a problem other than in issues #1 and #2?
Vertica update
I chatted with Andy Ellicott and Mike Stonebraker of Vertica today. Some of the content is embargoed until February 19 (for TDWI), but here are some highlights of the rest.
- Vertica now is “approaching” 50 paid customers, up from 15 or so in early November. (Compared to most of Vertica’s fellow data warehouse specialists, that’s a lot.) Many — perhaps most — of these customers are hedge funds or telcos.
- Vertica’s typical lag from sale to deployment is about one quarter.
- Vertica’s typical initial selling price is $250K. Or maybe it’s $100-150K. The Vertica guys are generally pretty forthcoming, but pricing is an exception. Whatever they charge, it’s strictly per terabyte of user data. They think they are competitive with other software vendors, and cheaper, all-in, than appliance vendors.
- One subject on which they’re totally non-forthcoming (lawyers’ orders) is the recent patent lawsuit filed by Sybase. They wouldn’t even say whether they thought it was bogus because they didn’t infringe, or whether they thought it was bogus because the patent shouldn’t have been granted.
- Average Vertica database size is a little under 10 terabytes of user data, with many examples in the 15-20 Tb range. Lots of customers plan to expand to 50-100 Tb.
- Vertica claims sustainable load speeds of 3-5 megabytes/sec/node, irrespective of database size. Data is sucked into RAM uncompressed, then written out a gig/node at a time, compressed. Gigabyte chunks are then merged on disk, which is superfast as it doesn’t involve sorting. (30 megabytes/second.) Mike insists this doesn’t compromise compression.
We also addressed the subject of Vertica’s schema assumptions, but I’ll leave that to another post.
| Categories: Analytic technologies, Data warehousing, Database compression, Investment research and trading, Michael Stonebraker, Sybase, Theory and architecture, Vertica Systems | 6 Comments |
A quick survey of data warehouse management technology
There are at least 16 different vendors offering appliances and/or software that do database management primarily for analytic purposes.* That’s a lot to keep up with,. So I’ve thrown together a little overview of the analytic data management landscape, liberally salted with links to information about specific vendors, products, or technical issues. In some ways, this is a companion piece to my prior post about data warehouse appliance myths and realities.
*And that’s just the tabular/alphanumeric guys. Add in text search and you run the total a lot higher.
Numerous data warehouse specialists offer traditional row-based relational DBMS architectures, but optimize them for analytic workloads. These include Teradata, Netezza, DATAllegro, Greenplum, Dataupia, and SAS. All of those except SAS are wholly or primarily vendors of MPP/shared-nothing data warehouse appliances. EDIT: See the comment thread for a correction re Kognitio.
Numerous data warehouse specialists offer column-based relational DBMS architectures. These include Sybase (with the Sybase IQ product, originally from Expressway), Vertica, ParAccel, Infobright, Kognitio (formerly White Cross), and Sand. Read more
ANALYTIC is the antonym of TRANSACTIONAL
In 1993, Ted Codd introduced the term OLAP (OnLine Analytic Processing) to describe data management that wasn’t optimized for OLTP (OnLine Transaction Processing). Later in the 1990s, Henry Morris of IDC introduced the term analytic applications to describe apps that weren’t transactional. Since then, no better word than “analytic” has emerged to cover the broad class of IT apps and technologies that aren’t focused on transactional processing.
In the latest incarnation, analytic appliances are coming to the fore. Read more
| Categories: Analytic technologies, Data warehouse appliances, Netezza, Vertica Systems | Leave a Comment |
Vertica update – HP appliance deal, customer information, and more
Vertica quietly announced an appliance bundling deal with HP and Red Hat today. That got me quickly onto the phone with Vertica’s Andy Ellicott, to discuss a few different subjects. Most interesting was the part about Vertica’s customer base, highlights of which included:
- Vertica’s claim to have “50” customers includes a bunch of unpaid licenses, many of them in academia.
- Vertica has about 15 paying customers.
- Based on conversations with mutual prospects, Vertica believes that’s more customers than DATAllegro has. (Of course, each DATAllegro sale is bigger than one of Vertica’s. Even so, I hope Vertica is wrong in its estimate, since DATAllegro told me its customer count was “double digit” quite a while ago.)
- Most Vertica customers manage over 1 terabyte of user data. A couple have bought licenses showing they intend to manage 20 terabytes or so.
- Vertica’s biggest customer/application category – existing customers and sales pipelines alike – is call detail records for telecommunications companies. (Other data warehouse specialists also have activity in the CDR area.). Major applications are billing assurance (getting the inter-carrier charges right) and marketing analysis. Call center uses are still in the future.
- Vertica’s other big market to date is investment research/tick history. Surely not coincidentally, this is a big area of focus for Mike Stonebraker, evidently at both companies for which he’s CTO. (The other, of course, is StreamBase.)
- Runners-up in market activity are clickstream analysis and general consumer analytics. These seem to be present in Vertica’s pipeline more than in the actual customer base.
| Categories: Analytic technologies, Business Objects, Data warehouse appliances, Data warehousing, DATAllegro, HP and Neoview, RDF and graphs, Vertica Systems | 5 Comments |
Vertica — just star and snowflake schemas?
One of the longest-running technotheological disputes I know of is the one pitting flat/normalized data warehouse architectures vs. cubes, stars, and snowflake schemas. Teradata, for example, is a flagwaver for the former camp; Microstrategy is firmly in the latter. (However, that doesn’t keep lots of retailers from running Microstrategy on Teradata boxes.) Attensity (a good Teradata partner) is in the former camp; text mining rival Clarabridge (sort of a Microstrategy spinoff) is in the latter. And so on.
Vertica is clearly in the star/snowflake camp as well. I asked them about this, and Vertica’s CTO Mike Stonebraker emailed a response. I’m reproducing it below, with light edits; the emphasis is also mine. Key points include:
- Almost everybody (that Vertica sees) wants stars and snowflakes, so that’s what Vertica optimizes for.
- Replicating small dimension tables across nodes is great for performance.
- Even so, Vertica is broadening its support for more general schemas as well.
Great question. This is something that we’ve thought a lot about and have done significant research on with large enterprise customers. … short answer is as follows:
Vertica supports star and snowflake schemas because that is the desired data structure for data warehousing. The overwhelming majority of the schemas we see are of this form, and we have highly optimized for this case. Read more