Applix – Three huge opportunities Cognos will probably ignore
If I weren’t on a snorkeling vacation,* this might be a good time to write about why I once called Cognos “The Gang That Couldn’t Shoot Straight,” how Ron Zambonini used that label to help him gain the company’s top spot, why he’s such a big fan of mine, why I got my highest ever per-minute speaking fee to attend a Cognos sales kickoff event, why I went for a midnight touristing stroll in downtown Ottawa in zero degree Fahrenheit weather, or how I managed, while attending the aforementioned Cognos sales kickoff, to get snowed in for three days in, of all places, Dallas, Texas. But the wrasses and jacks await, so I’ll get straight to the point.
*Albeit fairly snorkel-free so far, thanks to Hurricane Felix. 🙁
As I discussed at considerable length in a white paper, Applix’s core technology is fully-featured, memory-centric MOLAP. This is certainly cool technology, and I think it is actually unique. That it’s historically been positioned as the engine for a mid-range set of performance management tools is a travesty, a shame, the result of a prior merger – and also the quite understandable consequence of RAM limitations. However, RAM is ever cheaper and Applix’s technology is now 64-bit, so the RAM barriers have been relaxed. Cognos can take Applix’s TM1 engine high-end if it wants to. And boy, should Cognos ever want to. Indeed, there are three different great ways Cognos could package and position TM1:
- As a no-data-warehouse-design quick-start analytics engine analogous to QlikView (the fastest-growing and most important newish BI suite, open source perhaps excepted);
- As the most sophisticated and versatile planning tool this side of SAP’s APO (and while APO’s sophistication is not in dispute, its versatility is questionable anyway);
- As the processing hub for dashboards-done-right.
| Categories: Analytic technologies, Business intelligence, Cognos, Memory-centric data management, MOLAP | 6 Comments |
Philip Howard likes Calpont — again
The ratio of Philip Howard plaudits about Calpont to shipping products from Calpont has now doubled. Yet it also has remained the same. This is because it is a countably infinite number, namely a quotient whose denominator is zero. Last time around, he seemed to like their hardware strategy. This time around, he seems to like their lack of a hardware strategy. Be that as it may, the previously discussed nature of Calpont’s website hasn’t changed — one page, content-free, and misleading even so.
Oh, and it appears he broke the embargo on Paraccel. Bad Philip. Spank him, Kim.
| Categories: Calpont, Data warehouse appliances, Data warehousing, Emulation, transparency, portability | 1 Comment |
Big stuff coming from DATAllegro
In the literal sense, that is. While the details on what I wrote about this a few weeks ago* are still embargoed, I’m at liberty to drop a few more hints.
*Please also see DATAllegro CEO Stuart Frost’s two comments added today to that thread.
DATAllegro systems these days basically consist of Dell servers talking to EMC disk arrays, with Cisco Infiniband to provide fast inter-server communication without significant CPU load. Well, if you decrease the number of Dell servers per EMC box, and increase the number of disks per EMC box, you can slash your per-terabyte price (possibly at the cost of lowering performance).
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| Categories: Data warehouse appliances, Data warehousing, Database compression, DATAllegro | Leave a Comment |
Even Robin Bloor can get snookered once in a while
Robin Bloor is one of the best analysts around — which doesn’t say much about his eponymous firm, since he no longer works there, but I digress. Even so, he evidently got snookered by a Truviso spokesperson, as evidenced by this article.
Apparently, Truviso convinced him that other CEP firms execute one query at a time, while Truviso executes a bunch of queries at once. Well, the latter part of that is presumably true, but it’s hardly the big differentiatior for Truviso Robin would have one believe. That’s what everybody else — StreamBase, Coral8, Progress Apama, et al. — do too. I wouldn’t be surprised if Truviso had a somewhat different architecture for doing it (each vendor describes its approach in rather different language), or even if this were a particular focus and strongpoint of theirs. But fundamentally, all the CEP vendors are doing the same thing.
| Categories: Memory-centric data management, Streaming and complex event processing (CEP), Truviso | Leave a Comment |
And then there is predictability
Coral8 at the time of a recent product release stated that it was improving the predictability of its queries. While this may sound like it has something to do with determinism, it doesn’t. Rather, it’s a matter of making what actually happens as a query result be more in line with what one would think will happen when one reads the query.
Coral8 CTO Mark Tsimelzon goes on to note:
But remember, we are really talking about a corner case — highly complex queries involving loops. We only had a couple of customers who were occasionally hitting queries that complex. The beauty of our SQL-based language is that the vast majority of queries, perhaps 99%, are very easy to understand, and their behavior is exactly what you’d expect based on your SQL experience.
| Categories: Aleri and Coral8, Memory-centric data management, Streaming and complex event processing (CEP) | Leave a Comment |
Applications for not-so-low-latency CEP
The highest-profile applications for complex event/stream processing are probably the ones that require super-low latency, especially in financial trading. However, as I already noted in writing about StreamBase and Truviso, there are plenty of other CEP apps with less extreme latency requirements.
Commonly, these are data reduction apps – i.e., there’s a gushing stream of inputs, and the CEP engine filters and “enhances” it, so that only a small, modified subset is sent forward. In other cases, disk-based systems could do the job perfectly well from a performance standpoint, but the pattern matching and filtering requirements are just a better fit for the CEP paradigm.
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| Categories: Aleri and Coral8, IBM and DB2, Memory-centric data management, StreamBase, Streaming and complex event processing (CEP), Structured documents | 3 Comments |
Applications for super-low-latency CEP
Complex event/stream processing vendors compete fiercely on the basis of low latency, down to the single-digit number of milliseconds, or even sub-millisecond levels. A question naturally springs to mind: When does this extreme low latency matter?
I think I’ve come up with a concise yet fairly accurate answer: Super-low latency matters when the application includes direct competition against a similarly fast opponent. The best example is automated stock trading – if you can exploit a market inefficiency 1 millisecond before your competition, you make money.
Other examples might arise in network security or battlefield systems, but I don’t know of any specific real-life cases. Instead, other applications for complex event/stream processing tend to be content with latencies that are easier to achieve. E.g., 100 milliseconds (1/10 of second) is likely to be plenty fast enough.
| Categories: Investment research and trading, Memory-centric data management, Streaming and complex event processing (CEP) | 2 Comments |
Coral8 versus StreamBase
Besides talking about what Coral8 and StreamBase (and other CEP vendors) have in common, Mark Tsimelzon and I talked quite a bit about what he sees as some of the important differences. There were a lot, of course, but three in particular stood out.
1. Mark believes Coral8 has significantly lower latency than StreamBase. E.g., the Wombat/Coral8 combo achieves sub-millisecond latency, with Coral8 itself consuming less than a tenth of that. The best comparable figures from StreamBase that I currently know of are almost an order of magnitude slower.
Top-end speed aside, Mark believes that Coral8 is fundamentally better suited for complex queries and pattern recognition, while StreamBase works well with simpler queries. For example, his other performance claims notwithstanding, he concedes that StreamBase is at least comparable to Coral8 in its throughput for huge numbers of simple queries. (The number he mentioned was ½ million queries/second.) Indeed, while we barely talked about customer/marketing issues, Mark asserts that the companies’ respective customer bases reflect this complex/simple distinction.*
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| Categories: Aleri and Coral8, Memory-centric data management, Progress, Apama, and DataDirect, StreamBase, Streaming and complex event processing (CEP) | 5 Comments |
The essence of CEP according to Coral8
Last week, I complained that my first briefing with Coral8 wasn’t very technical. Wednesday I had a call with Mark Tsimelzon, CTO and founder of Coral8, and he made up for that in spades. In this post I’ll cover some of his general comments. Others will touch on more Coral8-specific topics, and his view of the Coral8/StreamBase comparison.
As Mark describes it, the big difference between a DBMS – even an in-memory DBMS – and a complex event processing engine is this: CEP engines do instantaneous incremental processing. He commonly refers to this as registering queries and operators for incremental evaluation. For example, suppose you need to maintain the sum of some data stream over the past 10 minutes. Then each second (or other short unit of time), the system adds in all the values that arrived in the past second, and subtracts all those that arrived 600-601 seconds ago. Voila! The sum is incrementally updated.
Now, rolling sums may not sound very interesting – but where you have rolling sums, you trivially also have rolling averages (just divide the sum by the count) and rolling standard deviations (same idea, with some squares and square roots mixed in). Those, of course, are primitives in Coral8 too. Ditto rolling maxima and minima. Ditto rolling joins (which are updated a lot like materialized views).
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| Categories: Aleri and Coral8, Memory-centric data management, Streaming and complex event processing (CEP) | 2 Comments |
Competitive claims in CEP
For the most part, the vendors I talk with in complex event/stream processing like and speak well of each other (most of the exceptions seem to involve StreamBase). Even so, there are a lot of interesting competitive claims and counterclaims in this market. Prior posts and comment threads have covered Apama/StreamBase jousting on the subjects of who has more business and how many financial data feeds StreamBase supports. Other areas that generate interesting sparks are performance, parallelism, and determinism. Read more