Analysis of columnar data warehouse DBMS vendor Vertica Systems. Related subjects include:
It took me a bit of time, and an extra call with Vertica’s long-time R&D chief Shilpa Lawande, but I think I have a decent handle now on Vertica 7, code-named Crane. The two aspects of Vertica 7 I find most interesting are:
- Flex Zone, a schema-on-need technology very much like Hadapt’s (but of course with access to Vertica performance).
- What sounds like an alternate query execution capability for short-request queries, the big point of which is that it saves them from being broadcast across the whole cluster, hence improving scalability. (Adding nodes of course doesn’t buy you much for the portion of a workload that’s broadcast.)
Other Vertica 7 enhancements include:
- A lot of Bottleneck Whack-A-Mole.
- “Significant” improvements to the Vertica management console.
- Security enhancements (Kerberos), Hadoop integration enhancements (HCatalog), and enhanced integration with Hadoop security (Kerberos again).
- Some availability hardening. (“Fault groups”, which for example let you ensure that data is replicated not just to 2+ nodes, but also that the nodes aren’t all on the same rack.)
- Java as an option to do in-database analytics. (Who knew that feature was still missing?)
- Some analytic functionality. (Approximate COUNT DISTINCT, but not yet Approximate MEDIAN.)
Overall, two recurring themes in our discussion were:
- Load and ETL (Extract/Transform/Load) performance, and/or obviating ETL.
- Short-request performance, in the form of more scalable short-request concurrency.
Much of modern analytic technology deals with what might be called an entity-centric sequence of events. For example:
- You receive and open various emails.
- You click on and look at various web sites and pages.
- Specific elements are displayed on those pages.
- You study various products, and even buy some.
Analytic questions are asked along the lines “Which sequences of events are most productive in terms of leading to the events we really desire?”, such as product sales. Another major area is sessionization, along with data preparation tasks that boil down to arranging data into meaningful event sequences in the first place.
A number of my clients are focused on such scenarios, including WibiData, Teradata Aster (e.g. via nPath), Platfora (in the imminent Platfora 3), and others. And so I get involved in naming exercises. The term entity-centric came along a while ago, because “user-centric” is too limiting. (E.g., the data may not be about a person, but rather specifically about the actions taken on her mobile device.) Now I’m adding the term event series to cover the whole scenario, rather than the “event sequence(s)” I might appear to have been hinting at above.
I decided on “event series” earlier this week, after noting that: Read more
|Categories: Aster Data, Business intelligence, Data warehousing, EAI, EII, ETL, ELT, ETLT, Platfora, Predictive modeling and advanced analytics, Teradata, Vertica Systems, Web analytics, WibiData||10 Comments|
Some subjects just keep coming up. And so I keep saying things like:
Most generalizations about “Big Data” are false. “Big Data” is a horrific catch-all term, with many different meanings.
Most generalizations about Hadoop are false. Reasons include:
- Hadoop is a collection of disparate things, most particularly data storage and application execution systems.
- The transition from Hadoop 1 to Hadoop 2 will be drastic.
- For key aspects of Hadoop — especially file format and execution engine — there are or will be widely varied options.
Hadoop won’t soon replace relational data warehouses, if indeed it ever does. SQL-on-Hadoop is still very immature. And you can’t replace data warehouses unless you have the power of SQL.
Note: SQL isn’t the only way to provide “the power of SQL”, but alternative approaches are just as immature.
Most generalizations about NoSQL are false. Different NoSQL products are … different. It’s not even accurate to say that all NoSQL systems lack SQL interfaces. (For example, SQL-on-Hadoop often includes SQL-on-HBase.)
I’m not having a productive week, part of the reason being a hard drive crash that took out early drafts of what were to be last weekend’s blog posts. Now I’m operating from a laptop, rather than my preferred dual-monitor set-up. So please pardon me if I’m concise even by comparison to my usual standards.
- My recent posts based on surveillance news have been partly superseded by – well, by more news. Some of that news, along with some good discussion, may be found in the comment threads.
- The same goes for my recent Hadoop posts.
- The replay for my recent webinar on real-time analytics is now available. My part ran <25 minutes.
- One of my numerous clients using or considering a “real-time analytics” positioning is Sqrrl, the company behind the NoSQL DBMS Accumulo. Last month, Derrick Harris reported on a remarkable Accumulo success story – multiple US intelligence instances managing 10s of petabytes each, and supporting a variety of analytic (I think mainly query/visualization) approaches.
- Several sources have told me that MemSQL’s Zynga sale is (in part) for Membase replacement. This is noteworthy because Zynga was the original pay-for-some-of-the-development Membase customer.
- More generally, the buzz out of Couchbase is distressing. Ex-employees berate the place; job-seekers check around and then decide not to go there; rivals tell me of resumes coming out in droves. Yes, there’s always some of that, even at obviously prospering companies, but this feels like more than the inevitable low-level buzz one hears anywhere.
- I think the predictive modeling state of the art has become:
- Cluster in some way.
- Model separately on each cluster.
- And if you still want to do something that looks like a regression – linear or otherwise – then you might want to use a tool that lets you shovel training data in WITHOUT a whole lot of preparation* and receive a model back out. Even if you don’t accept that as your final model, it can at least be a great guide to feature selection (in the statistical sense of the phrase) and the like.
- Champion/challenger model testing is also a good idea, at least if you’re in some kind of personalization/recommendation space, and have enough traffic to test like that.**
- Most companies have significant turnover after being acquired, perhaps after a “golden handcuff” period. Vertica is no longer an exception.
- Speaking of my clients at HP Vertica – they’ve done a questionable job of communicating that they’re willing to price their product quite reasonably. (But at least they allowed me to write about $2K/terabyte for hardware/software combined.)
- I’m hearing a little more Amazon Redshift buzz than I expected to. Just a little.
- StreamBase was bought by TIBCO. The rumor says $40 million.
*Basic and unavoidable ETL (Extract/Transform/Load) of course excepted.
**I could call that ABC (Always Be Comparing) or ABT (Always Be Testing), but they each sound like – well, like The Glove and the Lions.
Edit: Please see the comment thread below for updates. Please also see a follow-on post about how the surveillance data is actually used.
US government surveillance has exploded into public consciousness since last Thursday. With one major exception, the news has just confirmed what was already thought or known. So where do we stand?
My views about domestic data collection start:
- I’ve long believed that the Feds — specifically the NSA (National Security Agency) — are storing metadata/traffic data on every telephone call and email in the US. The recent news, for example Senator Feinstein’s responses to the Verizon disclosure, just confirms it. That the Feds sometimes claim this has to be “foreign” data or they won’t look at it hardly undermines my opinion.
- Even private enterprises can more or less straightforwardly buy information about every credit card purchase we make. So of course the Feds can get that as well, as the Wall Street Journal seems to have noticed. More generally, I’d assume the Feds have all the financial data they want, via the IRS if nothing else.
- Similarly, many kinds of social media postings are aggregated for anybody to purchase, or can be scraped by anybody who invests in the equipment and bandwidth. Attensity’s service is just one example.
- I’m guessing that web use data (http requests, search terms, etc.) is not yet routinely harvested by the US government.* Ditto deanonymization of same. I guess that way basically because I’ve heard few rumblings to the contrary. Further, the consumer psychographic profiles that are so valuable to online retailers might be of little help to national security analysts anyway.
- Video surveillance seems likely to grow, from fixed cameras perhaps to drones; note for example the various officials who called for more public cameras after that Boston Marathon bombing. But for the present discussion, that’s of lesser concern to me, simply because it’s done less secretively than other kinds of surveillance. If there’s a camera that can see us, often we can see it too.
*Recall that these comments are US-specific. Data retention legislation has been proposed or passed in multiple countries to require recording of, among other things, all URL requests, with the stated goal of fighting either digital piracy or child pornography.
As for foreign data: Read more
|Categories: Hadoop, HP and Neoview, Petabyte-scale data management, Pricing, Surveillance and privacy, Telecommunications, Text, Vertica Systems, Web analytics||10 Comments|
My quick reaction to the Actian/ParAccel deal was negative. A few challenges to my views then emerged. They didn’t really change my mind.
Amazon did a deal with ParAccel that amounted to:
- Amazon got a very cheap license to a limited subset of ParAccel’s product …
- … so that it could launch a service called Amazon Redshift.
- Amazon also invested in ParAccel.
Some argue that this is great for ParAccel’s future prospects. I’m not convinced.
No doubt there are and will be Redshift users, evidently including Infor. But so far as I can tell, Redshift uses very standard SQL, so it doesn’t seed a ParAccel market in terms of developer habits. The administration/operation story is similar. So outside of general validation/bragging rights, Redshift is not a big deal for ParAccel.
OEMs and bragging rights
It’s not just Amazon and Infor; there’s also a MicroStrategy deal to OEM ParAccel — I think it’s the real ParAccel software in that case — for a particular service, MicroStrategy Wisdom. But unless I’m terribly mistaken, HP Vertica, Sybase IQ and even Infobright each have a lot more OEMs than ParAccel, just as they have a lot more customers than ParAccel overall.
This OEM success is a great validation for the idea of columnar analytic RDBMS in general, but I don’t see where it’s an advantage for ParAccel vs. the columnar leaders. Read more
|Categories: Actian and Ingres, Amazon and its cloud, Columnar database management, HP and Neoview, Market share and customer counts, ParAccel, Sybase, VectorWise, Vertica Systems||7 Comments|
Actian, which already owns VectorWise, is also buying ParAccel. The argument for why this kills VectorWise is simple. ParAccel does most things VectorWise does, more or less as well. It also does a lot more:
- ParAccel scales out.
- ParAccel has added analytic platform capabilities.
- I don’t know for sure, but I’d guess ParAccel has more mature management/plumbing capabilities as well.
One might conjecture that ParAccel is bad at highly concurrent, single-node use cases, and VectorWise is better at them — but at the link above, ParAccel bragged of supporting 5,000 concurrent connections. Besides, if one is just looking for a high-use reporting server, why not get Sybase IQ?? Anyhow, Actian hasn’t been investing enough in VectorWise to make it a major market player, and they’re unlikely to start now that they own ParAccel as well.
But I expect ParAccel to fail too. Reasons include:
- ParAccel’s small market share and traction.
- The disruption of any acquisition like this one.
- My general view of Actian as a company.
|Categories: Actian and Ingres, Columnar database management, Data warehousing, HP and Neoview, ParAccel, Sybase, VectorWise, Vertica Systems||10 Comments|
Comments on Gartner’s 2012 Magic Quadrant for Data Warehouse Database Management Systems — evaluations
To my taste, the most glaring mis-rankings in the 2012/2013 Gartner Magic Quadrant for Data Warehouse Database Management are that it is too positive on Kognitio and too negative on Infobright. Secondarily, it is too negative on HP Vertica, and too positive on ParAccel and Actian/VectorWise. So let’s consider those vendors first.
Gartner seems confused about Kognitio’s products and history alike.
- Gartner calls Kognitio an “in-memory” DBMS, which is not accurate.
- Gartner doesn’t remark on Kognitio’s worst-in-class* compression.
- Gartner gives Kognitio oddly high marks for a late, me-too Hadoop integration strategy.
- Gartner writes as if Kognitio’s next attempt at the US market will be the first one, which is not the case.
- Gartner says that Kognitio pioneered data warehouse SaaS (Software as a Service), which actually has existed since the pre-relational 1970s.
Gartner is correct, however, to note that Kognitio doesn’t sell much stuff overall.
In the cases of HP Vertica, Infobright, ParAccel, and Actian/VectorWise, the 2012 Gartner Magic Quadrant for Data Warehouse Database Management’s facts are fairly accurate, but I dispute Gartner’s evaluation. When it comes to Vertica: Read more
Merv Adrian and Doug Henschen both reported more details about Amazon Redshift than I intend to; see also the comments on Doug’s article. I did talk with Rick Glick of ParAccel a bit about the project, and he noted:
- Amazon Redshift is missing parts of ParAccel, notably the extensibility framework.
- ParAccel did some engineering to make its DBMS run better in the cloud.
- Amazon did some engineering in the areas it knows better than ParAccel — cloud provisioning, cloud billing, and so on.
“We didn’t want to do the deal on those terms” comments from other companies suggest ParAccel’s main financial take from the deal is an already-reported venture investment.
The cloud-related engineering was mainly around communications, e.g. strengthening error detection/correction to make up for the lack of dedicated switches. In general, Rick seemed more positive on running in the (Amazon) cloud than analytic RDBMS vendors have been in the past.
So who should and will use Amazon Redshift? For starters, I’d say: Read more
|Categories: Amazon and its cloud, Business intelligence, Cloud computing, Data mart outsourcing, Data warehousing, Infobright, ParAccel, Predictive modeling and advanced analytics, Pricing, Vertica Systems||4 Comments|
I was asked to clarify one of my July comments on Oracle12c,
I wonder whether Oracle will finally introduce a true columnar storage option, a year behind Teradata. That would be the obvious enhancement on the data warehousing side, if they can pull it off. If they can’t, it’s a damning commentary on the core Oracle codebase.
by somebody smart who however seemed to have half-forgotten my post comparing (hybrid) columnar compression to (hybrid) columnar storage.
In simplest terms:
- Columnar storage and columnar compression are two different things. The main connections are:
- Columnar storage can make columnar compression more effective.
- In different ways, both technologies reduce I/O.
- EMC Greenplum, Teradata Aster, and Teradata Classic are all originally row-based systems that have gone hybrid columnar.
- Vertica is an originally column-based system that has gone hybrid columnar.
|Categories: Aster Data, Columnar database management, Data warehousing, Database compression, Greenplum, Oracle, Teradata, Vertica Systems||4 Comments|