Analysis of VectorWise and its columnar analytic DBMS efforts. Related subjects include:
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
I put up 14 posts over the past week, so perhaps you haven’t had a chance yet to read them all. Highlights included:
- My most important post of the week was a general guide to IT vendor strategy. That one has already spawned discussion at many companies, from the tiny to the multi-billion-dollar.
- The best comment thread of the week was probably on my post about scale-out relational OLTP choices, in which people discussed the merits of various particular alternatives.
- I recommended that people strongly consider attending XLDB 5 in Menlo Park on October 18-19.
Most of the posts, however, were reactions to news events. In particular:
- Teradata announced that Teradata 14 will be hybrid-columnar, more in Vertica’s way than in Greenplum’s or Aster Data’s. (Pay no attention to the Wall Street Journal’s apparent belief that no other analytic DBMS is hybrid-columnar at all.)
- Aster announced the unsurprising news that there will be a Teradata Aster appliance. Also, Aster talked about greater analytic flexibility in the forthcoming Aster 5.0.
- With Oracle OpenWorld coming up, Oracle decided to get some of its announcing out of the way early. In particular, it announced the Oracle Database Appliance, which is small-business-friendly hardware for running the Oracle DBMS. However, the Oracle Database Appliance doesn’t seem to do much about the complexity of running the Oracle DBMS software.
- In a catch-all Hadoop post, I noted that:
- Oracle has now clearly said it has a Hadoop appliance coming, no doubt next week at OpenWorld.
- I still can’t see why Hadoop appliances would succeed, but a lot of smart folks seem to disagree with me.
- Greenplum announced what looks like a nice but unimportant little product upgrade.
- It’s a really good thing that previously reported plans to revamp Hadoop are underway.
- DataStax announced that it really is a Cassandra company after all. Pay no attention to previous marketing that seemed to put DataStax in the same Hadoop-alternative category as, say, MapR.
- Ingres has changed its name to Actian. The announcement seems like a confession that Ingres and VectorWise are going nowhere.
|Categories: Actian and Ingres, Aster Data, Data warehousing, DataStax, Greenplum, Hadoop, Teradata, VectorWise||Leave a Comment|
Ingres, the company, is:
- Changing its name to Actian.
- Deemphasizing Ingres, the product.
- Emphasizing a set of products that don’t exist yet (or at least aren’t shipping), namely lightweight mobile apps that are business-intelligence-plus-an-action, and technology for building them. These are called “Action Apps”, and are discussed on the Actian company blog.
- Positioning all this as something to do with “big data” (what a shock).
It turns out that Actian was the name of an ancient athletic competition commemorating Augustus’ defeat of Anthony at Actium, a battle that was more recently memorialized in the movie Cleopatra. Frankly, I think Cleopatra Software might have been a more interesting company name, although that could mean execs would have to arrive at sales calls rolled up in a carpet.
|Categories: Actian and Ingres, Business intelligence, Hadapt, Market share and customer counts, VectorWise||10 Comments|
I met with the Hadapt guys today. I think I can be a bit crisper than before in positioning Hadapt and its use cases, namely:
- Hadapt is additional software on a cluster that also runs fully functional Hadoop/HDFS. (Cloudera Hadoop more than straight-from-Apache Hadoop to date, but that’s not a requirement.)
- The cluster also runs a DBMS on every node, such as PostgreSQL or one of Infobright/Vectorwise.
- Hadapt’s software manages parallel SQL queries by distributing them to the DBMS living on each node. Hadapt says that the resulting query performance far outshines Hive’s.
- Hadapt further says that, by exploiting the partner DBMS, its SQL functionality outpaces Hive’s as well.
- Target Hadapt use cases are centered around keeping machine-generated or other poly-structured data in Hadoop, and extracting, enhancing, or otherwise deriving some of it to live in the relational store.
- In particular, Hadapt seems like an interesting choice when you want to use that relational data as you work on other data that’s still in HDFS, or if you want to keep using the relational data in other kinds of MapReduce jobs.
- That all fits well with my thoughts about the importance of derived data.
Other evolution from what I wrote about Hadapt a few months ago includes:
- Hadapt is in beta now.
- Hadapt has added adult supervision in the form of Philip Wickline, late of Endeca.
In other news, Hadapt is our newest client.
|Categories: Analytic technologies, Cloudera, Data models and architecture, Data warehousing, Hadapt, Hadoop, Infobright, MapReduce, Open source, PostgreSQL, SQL/Hadoop integration, VectorWise||Leave a Comment|
The HadoopDB company Hadapt is finally launching, based on the HadoopDB project, albeit with code rewritten from scratch. As you may recall, the core idea of HadoopDB is to put a DBMS on every node, and use MapReduce to talk to the whole database. The idea is to get the same SQL/MapReduce integration as you get if you use Hive, but with much better performance* and perhaps somewhat better SQL functionality.** Advantages vs. a DBMS-based analytic platform that includes MapReduce — e.g. Aster Data — are less clear. Read more
|Categories: Analytic technologies, Data warehousing, Hadapt, Hadoop, MapReduce, MySQL, Open source, Parallelization, PostgreSQL, SQL/Hadoop integration, Theory and architecture, VectorWise||12 Comments|
After working through problems w/ travel, cell phones, and so on, Peter Boncz of VectorWise finally caught up with me for a regrettably brief call. Peter gave me the strong impression that what I’d written in the past about VectorWise had been and remained accurate, so I focused on filling in the gaps. Highlights included: Read more
|Categories: Actian and Ingres, Analytic technologies, Benchmarks and POCs, Columnar database management, Data warehousing, Database compression, Open source, VectorWise||2 Comments|
Ingres forgot to prebrief me on the VectorWise announcement, and despite valiant efforts hasn’t succeeded in connecting with me since they realized the lapse. Meanwhile, I took a look at the VectorWise press release, and found the quotes to be somewhat amusing.
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|