StreamBase rebuts
In my post Monday about Apama, I complained that StreamBase hadn’t offered a rebuttal to some of Apama’s claims. This has now been fixed. 🙂 Bill Hobbib, StreamBase’s VP of Marketing wrote in. Part of what he had to say was the following.
Adapters to Data Feeds
Your blog comment that adapters doesn’t seem like a key competitive differentiator is accurate, and since adapters are so straightforward to develop with StreamBase as part of a customer engagement, we’ve never found adapters to be a key competitive differentiator. The comment by a competitor that their advantage over StreamBase comes from their having developed more adapters suggests they cannot distinguish themselves based on the other functional capabilities that are important to customers. In reality, our speed/performance and scalability are orders of magnitude superior to competitors, as is the speed with which StreamBase applications are developed, deployed, and modified when business needs change. (If it were easy to develop applications with certain competitive systems, then one might assume they would make free evaluation versions of their product available for download from their websites!)
That being said, StreamBase offers adapters to a broad array of data feeds. Most of these are offered out-of-the-box by StreamBase, including the following:
* Financial Market Data: processes data from Reuters® RMDS™ and Reuters Triarch™
* TIBCO® Rendezvous™: converts Rendezvous message into StreamBase tuples and vice versa.
* StreamBase Adapter for JDBC: connects StreamBase to enterprise databases, allowing submission of SQL queries to external resources such as IBM® DB2™, Oracle®, Microsoft® SQLServer™, and Sybase®.
* StreamBase Adapter for JMS: integrates StreamBase with any JMS-compliant message bus.
* StreamBase Adapter for Microsoft Excel™: allows applications to publish data to Excel or read data from Excel.
* StreamBase CSV Adapters: allow applications to read data from, and write data to, comma-separated value (CSV) files.
* StreamBase SMTP adapter: taps into the IP stack on a running system to process live data, converts the IP packets into a TCP data stream, or reads IP packets from captured files.
* StreamBase XML Adapter: streams XML-formatted data records into and out of StreamBase applicationsWe also can connect to financial exchanges either using our own adapters or through a third-party partnership. Below you’ll find a listing of those.
| Categories: Memory-centric data management, Progress, Apama, and DataDirect, StreamBase, Streaming and complex event processing (CEP) | Leave a Comment |
Progress Apama
I finally got my promised briefing with Progress Apama. Unfortunately, nobody particularly technical was able to attend, but I came away with a better understanding even so.
Unlike StreamBase or Truviso, Apama has a rules-based architecture. In essence, the rules engine maintains state of various kinds, and matches that state against desired patterns, called “scenarios.” They can handle 100s or possibly even 1000s of scenarios at once. Read more
| Categories: Memory-centric data management, Progress, Apama, and DataDirect, Streaming and complex event processing (CEP) | 2 Comments |
Webinar Wednesday June 27 at 2:00 pm ET
I’m sorry for the short notice, but — well, never mind what the distractions have been. This Wednesday, at 2:00 pm Eastern time, I’m doing a webinar on behalf of Solid. The core subject is memory-centric OLTP data management. I will of course also cover some DBMS and memory-centric generalities.
More info and sign-up can be found here.
| Categories: In-memory DBMS, Memory-centric data management, OLTP, solidDB | Leave a Comment |
Memory-centric vs. conventional DBMS — a Solid difference
I had the chance to talk at length with Solid Information Technology tech guru Antoni Wolski about their memory-centric DBMS technology architecture. The most urgent topic was what made in-memory database managers inherently faster than disk-based ones that happened to have all the data in cache. But we didn’t really separate that subject from the general topic of how they made their memory-centric technology run fast, from its introduction in 2002 through substantial upgrades in the most recent release.
There were 4 main subtopics to the call:
1. Indexing structures that are very different from those of disk-based DBMS.
2. Optimizations to those indexing structures.
3. Optimizations to logging and checkpointing.
4. Miscellaneous architectural issues.
Read more
| Categories: In-memory DBMS, Memory-centric data management, OLTP, solidDB | 4 Comments |
SolidDB caching for DB2
It’s just at the proof-of-concept stage, but Solid has a nice write-up about SolidDB being used as a front-end cache for DB2. Well, it’s a marketing document, so of course there’s a lot of pabulum too, but interspersed there’s some real meat as well. Highlights include 40X throughput improvement and 1 millisecond average response time (something that clearly can’t be achieved with disk-centric technology alone).
Analogies to Oracle/TimesTen are probably not coincidental; this is exactly the upside scenario for the TimesTen acquisition, as well as being TimesTen’s biggest growth area towards the end of its stint as an independent company.
| Categories: Cache, IBM and DB2, Memory-centric data management, OLTP, Oracle, Oracle TimesTen, solidDB | 1 Comment |
More on stream processing integration with disk-based DBMS
Mike Stonebraker wrote in with one “nit pick” about yesterday’s blog. I had credited Truviso for strong DBMS/stream processor integration. He shot back that StreamBase has Sleepycat integrated in-process. He further pointed out that a Sleepycat record lookup takes only 5 microseconds if the data is in cache. Assuming what he means is that it’s in Sleepycat’s cache, that would be tight integration indeed.
I wonder whether StreamBase will indefinitely rely on Sleepycat, which is of course now an Oracle product …
| Categories: Memory-centric data management, Michael Stonebraker, Oracle, StreamBase, Streaming and complex event processing (CEP) | Leave a Comment |
Mike Stonebraker on financial stream processing
After my call with Truviso and blog post referencing same, I had the chance to discuss stream processing with Mike Stonebraker, who among his many other distinctions is also StreamBase’s Founder/CTO. We focused almost exclusively on the financial trading market. Here are some of the highlights. Read more
| Categories: Memory-centric data management, Michael Stonebraker, StreamBase, Streaming and complex event processing (CEP), Truviso | Leave a Comment |
Fast RDF in specialty relational databases
When Mike Stonebraker and I discussed RDF yesterday, he quickly turned to suggesting fast ways of implementing it over an RDBMS. Then, quite characteristically, he sent over a paper that allegedly covered them, but actually was about closely related schemes instead. 🙂 Edit: The paper has a new, stable URL. Hat tip to Daniel Abadi.
All minor confusion aside, here’s the story. At its core, an RDF database is one huge three-column table storing subject-property-object triples. In the naive implementation, you then have to join this table to itself repeatedly. Materialized views are a good start, but they only take you so far. Read more
| Categories: Columnar database management, Data models and architecture, Data warehousing, Database compression, RDF and graphs, Theory and architecture, Vertica Systems | 1 Comment |
RDF “definitely has legs”
Thus spake Mike Stonebraker to me, on a call we’d scheduled to talk about several other things altogether. This was one day after I was told at the Text Analytics Summit that the US government is going nuts for RDF. And I continue to get confirmation of something I first noted last year — Oracle is pushing RDF heavily, especially in the life sciences market.
Evidently, the RDF data model is for real … unless, of course, you’re the kind of purist who cares to dispute whether RDF is a true “data model” at all.
| Categories: Data models and architecture, Oracle, RDF and graphs, Theory and architecture | Comments Off on RDF “definitely has legs” |
Native XML engine short list
I’ve been implying that the short list for native XML database engine vendors should be MarkLogic, IBM, and maybe Microsoft, on the theory that Progress and Intersystems tried the market and pulled back. Well, add Intersystems to the list, and not necessarily in last place. They’ve long had a very fast nonrelational engine in Cache’. Perhaps building Ensemble on it has induced them to sharpen up the XML capabilities again.
Anyhow, while I’m not at liberty to explain more of my reasoning (i.e., to disclose my evidence) — Cache’ should be taken seriously as an XML DBMS alternative … even if I never can seem to get a proper DBMS briefing from them (which is far from entirely being their fault).