Analysis of Sybase and its various product lines, such as Sybase IQ. Related subjects include:
Relational DBMS used to be fairly straightforward product suites, which boiled down to:
- A big SQL interpreter.
- A bunch of administrative and operational tools.
- Some very optional add-ons, often including an application development tool.
Now, however, most RDBMS are sold as part of something bigger.
- Oracle has hugely thickened its stack, as part of an Innovator’s Solution strategy — hardware, middleware, applications, business intelligence, and more.
- IBM has moved aggressively to a bundled “appliance” strategy. Even before that, IBM DB2 long sold much better to committed IBM accounts than as a software-only offering.
- Microsoft SQL Server is part of a stack, starting with the Windows operating system.
- Sybase was an exception to this rule, with thin(ner) stacks for both Adaptive Server Enterprise and Sybase IQ. But Sybase is now owned by SAP, and increasingly integrated as a business with …
- … SAP HANA, which is closely associated with SAP’s applications.
- Teradata has always been a hardware/software vendor. The most successful of its analytic DBMS rivals, in some order, are:
- Netezza, a pure appliance vendor, now part of IBM.
- Greenplum, an appliance-mainly vendor for most (not all) of its existence, and in particular now as a part of EMC Pivotal.
- Vertica, more of a software-only vendor than the others, but now owned by and increasingly mainstreamed into hardware vendor HP.
- MySQL’s glory years were as part of the “LAMP” stack.
- Various thin-stack RDBMS that once were or could have been important market players … aren’t. Examples include Progress OpenEdge, IBM Informix, and the various strays adopted by Actian.
The 2013 Gartner Magic Quadrant for Operational Database Management Systems is out. “Operational” seems to be Gartner’s term for what I call short-request, in each case the point being that OLTP (OnLine Transaction Processing) is dubious term when systems omit strict consistency, and when even strictly consistent systems may lack full transactional semantics. As is usually the case with Gartner Magic Quadrants:
- I admire the raw research.
- The opinions contained are generally reasonable (especially since Merv Adrian joined the Gartner team).
- Some of the details are questionable.
- There’s generally an excessive focus on Gartner’s perception of vendors’ business skills, and on vendors’ willingness to parrot all the buzzphrases Gartner wants to hear.
- The trends Gartner highlights are similar to those I see, although our emphasis may be different, and they may leave some important ones out. (Big omission — support for lightweight analytics integrated into operational applications, one of the more genuine forms of real-time analytics.)
Anyhow: Read more
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.)
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||8 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|
The cardinal rules of DBMS development
Rule 1: Developing a good DBMS requires 5-7 years and tens of millions of dollars.
That’s if things go extremely well.
Rule 2: You aren’t an exception to Rule 1.
- Concurrent workloads benchmarked in the lab are poor predictors of concurrent performance in real life.
- Mixed workload management is harder than you’re assuming it is.
- Those minor edge cases in which your Version 1 product works poorly aren’t minor after all.
DBMS with Hadoop underpinnings …
… aren’t exceptions to the cardinal rules of DBMS development. That applies to Impala (Cloudera), Stinger (Hortonworks), and Hadapt, among others. Fortunately, the relevant vendors seem to be well aware of this fact. Read more
|Categories: Aster Data, Cloudera, Columnar database management, Database compression, Hadapt, Hadoop, Hortonworks, IBM and DB2, MarkLogic, Netezza, NoSQL, QlikTech and QlikView, Structured documents, Sybase, Tableau Software, Teradata||25 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
Analyzing companies of any size is hard. Analyzing large ones, however, is harder yet.
- I get (much) less substance in an hour on the phone with a megacorp than I do when I talk with a smaller company.
- What large companies say is less reliable than what I hear from smaller ones.
- Large companies have policies, procedures, bureaucracy and attitudes that get in the way of communicating in the first place.
Such limitations should be borne in mind in connection with anything I write about, for example, Oracle, Microsoft, IBM, or SAP.
There are many reasons for large companies to communicate less usefully with analysts than smaller ones do. Some of the biggest are:
- For reasons of internal information flow, the people I talk with just know less than their counterparts at smaller companies. Similarly, what they do “know” is more often wrong, since different parts of the same company may not hold identical views.
- That’s when we talk about real issues at all, which can get crowded out by large companies’ voluminous efforts in complex positioning, messaging, and product names.
- Huge companies have huge bureaucracies, and they hurt.
- A small company C-level executive can make smart decisions about what to say or not say. A large company minion doesn’t have the same freedom.
- Just the process of getting access to even a mid-level spokesminion at a large company is harder than reaching a senior person at a smaller outfit.
- Large firms are clearest when communicating with their existing customers and those organizations’ key influencers. They’re less effective or clear when opening themselves up to competitive comparisons.
- If a company wants to behave unethically in its analyst dealings, there are economies of scale to doing so.
|Categories: About this blog, IBM and DB2, Microsoft and SQL*Server, Oracle, SAP AG, Sybase||5 Comments|
This is a draft entry for the DBMS2 analytic glossary. Please comment with any ideas you have for its improvement!
Note: Words and phrases in italics will be linked to other entries when the glossary is complete.
“In-database analytics” is a catch-all term for analytic capabilities, beyond standard SQL, running on the same machine as and under the management of an analytic DBMS. These can run in one or both of two modes:
- In-process or unfenced, i.e. in the same process as the DBMS itself. This option gives maximum performance, but any defects in the analytic code may crash the whole DBMS. Also, it generally requires that the code be in the same language as the DBMS, i.e. C++.
- Out-of-process or fenced, i.e. in a separate process. This option sacrifices performance, in favor of reliability and language flexibility.
In-database analytics may offer great performance and scalability advantages versus the alternative of extracting data and having it be processed on a separate server. This is particularly likely to be the case in MPP (Massively Parallel Processing) analytic DBMS environments.
Examples of in-database analytics include:
- Creating temporary data structures that persist past the life of a query.
- Creating temporary data structures that are non-tabular.
- Predictive modeling that uses all the same nodes in an MPP cluster where the data resides.
- Predictive analytics (scoring only).
Other common domains for in-database analytics include sessionization, time series analysis, and relationship analytics.
Notable products offering in-database analytics include:
- Teradata Aster SQL/MR.
- Multiple other analytic platforms, such as Sybase IQ, Vertica, or IBM Netezza. Indeed, in-database analytics are a defining feature of analytic platforms.
- Fuzzy Logix (for predictive analytics).
|Categories: Analytic glossary, Aster Data, Data warehousing, IBM and DB2, MapReduce, Netezza, Parallelization, Predictive modeling and advanced analytics, Sybase, Teradata, Vertica Systems||8 Comments|
In a call Monday with a prominent company, I was told:
- Teradata, Netezza, Greenplum and Vertica aren’t relational.
- Teradata, Netezza, Greenplum and Vertica are all data warehouse appliances.
That, to put it mildly, is not accurate. So I shall try, yet again, to set the record straight.
In an industry where people often call a DBMS just a “database” — so that a database is something that manages a database! — one may wonder why I bother. Anyhow …
1. The products commonly known as Oracle, Exadata, DB2, Sybase, SQL Server, Teradata, Sybase IQ, Netezza, Vertica, Greenplum, Aster, Infobright, SAND, ParAccel, Exasol, Kognitio et al. all either are or incorporate relational database management systems, aka RDBMS or relational DBMS.
2. In principle, there can be difficulties in judging whether or not a DBMS is “relational”. In practice, those difficulties don’t arise — yet. Every significant DBMS still falls into one of two categories:
- Was designed to do relational stuff* from the get-go, even if it now does other things too.
- Supports a lot of SQL.
- Was designed primarily to do non-relational things.*
- Doesn’t support all that much SQL.
*I expect the distinction to get more confusing soon, at which point I’ll adopt terms more precise than “relational things” and “relational stuff”.
3. There are two chief kinds of relational DBMS: Read more