Sybase
Analysis of Sybase and its various product lines, such as Sybase IQ. Related subjects include:
- Data warehousing
- Columnar database management systems
- (in Text Technologies) Sybase’s Answers Anywhere language-response technology
- (in Software Memories) Historical notes about Sybase
Juggling analytic databases
I’d like to survey a few related ideas:
- Enterprises should each have a variety of different analytic data stores.
- Vendors — especially but not only IBM and Teradata — are acknowledging and marketing around the point that enterprises should each have a number of different analytic data stores.
- In addition to having multiple analytic data management technology stacks, it is also desirable to have an agile way to spin out multiple virtual or physical relational data marts using a single RDBMS. Vendors are addressing that need.
- Some observers think that the real essence of analytic data management will be in data integration, not the actual data management.
Here goes. Read more
Comments on the analytic DBMS industry and Gartner’s Magic Quadrant for same
This year’s Gartner Magic Quadrant for Data Warehouse Database Management Systems is out.* I shall now comment, just as I did on the 2010, 2009, 2008, 2007, and 2006 Gartner Data Warehouse Database Management System Magic Quadrants, to varying extents. To frame the discussion, let me start by saying:
- In general, I regard Gartner Magic Quadrants as a bad use of good research.
- Illustrating the uselessness of — or at least poor execution on — the overall quadrant metaphor, a large majority of the vendors covered are lined up near the line x = y, each outpacing the one below in both of the quadrant’s dimensions.
- I find fewer specifics to disagree with in this Gartner Magic Quadrant than in previous year’s versions. Two factors jump to mind as possible reasons:
- This year’s Gartner Magic Quadrant for Data Warehouse Database Management Systems is somewhat less ambitious than others; while it gives as much company detail as its predecessors, it doesn’t add as much discussion of overall trends. So there’s less to (potentially) disagree with.
- Merv Adrian is now at Gartner.
- Whatever the problems may be with Gartner’s approach, the whole thing comes out better than do Forrester’s failed imitations.
*As of February, 2012 — and surely for many months thereafter — Teradata is graciously paying for a link to the report.
Specific company comments, roughly in line with Gartner’s rough single-dimensional rank ordering, include: Read more
Terminology: Data mustering
I find myself in need of a word or phrase that means bring data together from various sources so that it’s ready to be used, where the use can be analysis or operations. The first words I thought of were “aggregation” and “collection,” but they both have other meanings in IT. Even “data marshalling” has a specific meaning different from what I want. So instead, I’ll go with data mustering.
I mean for the term “data mustering” to encompass at least three scenarios:
- Integrated (relational) data warehouse.
- Big bit bucket.
- Big bit stream.
Let me explain what I mean by each. Read more
| Categories: Complex event processing (CEP), Data warehousing, Investment research and trading, Sybase, Teradata | 10 Comments |
Exasol update
I last wrote about Exasol in 2008. After talking with the team Friday, I’m fixing that now. 
The general theme was as you’d expect: Since last we talked, Exasol has added some new management, put some effort into sales and marketing, got some customers, kept enhancing the product and so on.
Top-level points included:
- Exasol’s technical philosophy is substantially the same as before, albeit not with as extreme a focus on fitting everything in RAM.
- Exasol believes its flagship DBMS EXASolution has great performance on a load-and-go basis.
- Exasol has 25 EXASolution customers, all in Germany.*
- 5 of those are “cloud” customers, at hosting providers engaged by Exasol.
- EXASolution database sizes now range from the low 100s of gigabytes up to 30 terabytes.
- Pretty much the whole company is in Nuremberg.
Very brief CEP/streaming catchup
When I agreed to launch the StreamBase LiveView product via DBMS 2, I planned to catch up on the whole CEP/streaming area first. Due to the power and internet outages last week, that didn’t entirely happen. So I’ll do a bit of that now, albeit more cryptically than I hoped and intended.
- The upshot of my what to call CEP thread in August was that “streaming” and “event processing” are not the same concept, but it so happens that they have the most traction where they intersect. That said, I both observe and endorse an apparent shift from “event” to “stream” as the core of the terminology, in a reversal of my opinion of several years ago.
- IBM continues to throw a lot of resources at its System S/ InfoSphere Streams product, but I haven’t heard yet of much marketplace success. That said, I believe IBM is still pretty serious about Streams, as one would expect from an effort whose code name so cheekily references System R. In particular, Streams shows up prominently on IBM’s top-level analytic architecture slide.
- Sybase recently released its ESP (Event Stream Processor) 5.0, which it says is the full merger of the Aleri and Coral8 predecessors. You can still get Sybase ESP without buying into the full Sybase RAP stack, and Sybase has no plans to change that.
- Sybase has discontinued all the business intelligence types of products Aleri and Coral8 were developing. Rather, Sybase is OEMing Panopticon, which it reports has been well received. Other than the discontinuation of the BI efforts, there seem to be few Aleri or Coral8 features missing from the merged Sybase ESP product.
- Truviso continues to be out of the picture.
- I have more to say about StreamBase separately.
- I have more to say about Sybase and IBM, which I’ll get to when I can.
- I have nothing new on Progress Apama. I also know little about any of the open source efforts.
Meanwhile, if you want to see technically nitty-gritty posts about the CEP/streaming area, you may want to look at my CEP/streaming coverage circa 2007-9, based on conversations with (among others) Mike Stonebraker, John Bates, and Mark Tsimelzon.
| Categories: Business intelligence, Complex event processing (CEP), IBM and DB2, StreamBase, Sybase, Truviso | 2 Comments |
Compression in Sybase ASE 15.7
Sybase recently came up with Adaptive Server Enterprise 15.7, which is essentially the “Make SAP happy” release. Features that were slated for 2012 release, but which SAP wanted, were accelerated into 2011. Features that weren’t slated for 2012, but which SAP wanted, were also brought into 2011. Not coincidentally, SAP Business Suite will soon run on Sybase Adaptive Server Enterprise 15.7.
15.7 turns out to be the first release of Sybase ASE with data compression. Sybase fondly believes that it is matching DB2 and leapfrogging Oracle in compression rate with a single compression scheme, namely page-level tokenization. More precisely, SAP and Sybase seem to believe that about compression rates for actual SAP application databases, based on some degree of testing. Read more
| Categories: Database compression, Sybase | 5 Comments |
Virtual data marts in Sybase IQ
I made a few remarks about Sybase IQ 15.3 when it became generally available in July. Now that I’ve had a current briefing, I’ll make a few more.
The key enhancement in Sybase IQ 15.3 is distributed query — what others might call parallel query — aka PlexQ. A Sybase IQ query can now be distributed among many nodes, all talking to the same SAN (Storage-Area Network). Any Sybase IQ node can take the responsibility of being the “leader” for that particular query.
In itself, this isn’t that impressive; all the same things could have been said about pre-Exadata Oracle.* But PlexQ goes somewhat further than just removing a bottleneck from Sybase IQ. Notably, Sybase has rolled out a virtual data mart capability. Highlights of the Sybase IQ virtual data mart story include: Read more
| Categories: Columnar database management, Data warehousing, Oracle, Parallelization, Sybase, Theory and architecture, Workload management | 1 Comment |
Sybase IQ soundbites
Sybase made a total hash of the timing of this week’s press release. I got annoyed after they promised to inform me of the new embargo time, then broke the promise. Other people got annoyed earlier than that.
So be it. Below is the draft of a post I was holding, with brackets added around one word that is no longer accurate.
I don’t write enough about Sybase IQ. That said, I offered a couple of quotes to a reporter [yesterday] in connection with the general availability of Sybase IQ 15.3. Lightly edited, they go:
- “Shared-everything MPP” isn’t a total contradiction in terms. It’s great for adding in concurrent users. And there’s little doubt that Sybase IQ can support robust access to databases 10s of terabytes in size.
- As I first noted a couple of years ago, virtual data marts are a good idea. Too few vendors are making it easy to spin them out. They let departments start doing analytics very quickly, yet allow IT to keep partial control.
Beyond that, I should note:
- Sybase IQ is the classic choice for what I call traditional data marts.
- Sybase IQ is a leader in temporal functionality, which is not coincidental to its presence in the financial services market.
| Categories: Columnar database management, Data warehousing, Parallelization, Sybase, Theory and architecture | Leave a Comment |
Eight kinds of analytic database (Part 1)
Analytic data management technology has blossomed, leading to many questions along the lines of “So which products should I use for which category of problem?” The old EDW/data mart dichotomy is hopelessly outdated for that purpose, and adding a third category for “big data” is little help.
Let’s try eight categories instead. While no categorization is ever perfect, these each have at least some degree of technical homogeneity. Figuring out which types of analytic database you have or need — and in most cases you’ll need several — is a great early step in your analytic technology planning. Read more
Temporal data, time series, and imprecise predicates
I’ve been confused about temporal data management for a while, because there are several different things going on.
- Date arithmetic. This of course has been around for a very long — er, for a very long time.
- Time-series-aware compression. This has been around for quite a while too.
- “Time travel”/snapshotting — preserving the state of the database at previous points in time. This is a matter of exposing (and not throwing away) the information you capture via MVCC (Multi-Version Concurrency Control) and/or append-only updates (as opposed to update-in-place). Those update strategies are increasingly popular for pretty much anything except update-intensive OLTP (OnLine Transaction Processing) DBMS, so time-travel/snapshotting is an achievable feature for most vendors.
- Bitemporal data access. This occurs when a fact has both a transaction timestamp and a separate validity duration. A Wikipedia article seems to cover the subject pretty well, and I touched on Teradata’s bitemporal plans back in 2009.
- Time series SQL extensions. Vertica explained its version of these to me a few days ago. I imagine Sybase IQ and other serious financial-trading market players have similar features.
In essence, the point of time series/event series SQL functionality is to do SQL against incomplete, imprecise, or derived data.* Read more