Application areas
Posts focusing on the use of database and analytic technologies in specific application domains. Related subjects include:
- Any subcategory
- (in Text Technologies) Specific application areas for text analytics
The Sybase Aleri RAP
Well, I got a quick Sybase/Aleri briefing, along with multiple apologies for not being prebriefed. (Main excuse: News was getting out, which accelerated the announcement.) Nothing badly contradicted my prior post on the Sybase/Aleri deal.
To understand Sybase’s plans for Aleri and CEP, it helps to understand Sybase’s current CEP-oriented offering, Sybase RAP. So far as I can tell, Sybase RAP has to date only been sold in the form of Sybase RAP: The Trading Edition. In that guise, Sybase RAP has been sold to >40 outfits since its May, 2008 launch, mainly big names in the investment banking and stock exchange sectors. If I understood correctly, the next target market for Sybase RAP is telcos, for real-time network tuning and management.
In addition to any domain-specific applications, Sybase RAP has three layers:
- CEP (Complex Event Processing). Sybase RAP CEP is based on a version of the Coral8 engine Sybase licensed and has been subsequently developing.
- In-memory DBMS. Sybase’s IMDB is part of (but I guess separable from) and has the same API as Sybase’s OLTP DBMS Adaptive Server Enterprise (ASE, aka Sybase Classic).
- Sybase IQ. Actually, Sybase used the phrase “based on Sybase IQ,” but I’m guessing it’s just Sybase IQ.
Quick thoughts on Sybase/Aleri
Sybase announced an asset purchase that amounts to a takeover of CEP (Complex Event Processing) Aleri. Perhaps not coincidentally, Sybase already had technology under the hood from Aleri predecessor/acquiree Coral8, for financial services uses (notwithstanding that between Aleri Classic and Coral8, Aleri Classic was the one of the two more focused on financial services). Quick reactions include:
- The folks at Sybase still haven’t figured out when to prebrief me. (Edit: I’ve been briefed subsequently.)
- Sybase/Aleri is a potentially powerful combination, if they can effectively address the point I just made about integrating disparate latencies. That said, I’m not expecting a lot, because the CEP industry always disappoints me.
- Microsoft, IBM, and (somewhat less clearly) Oracle are all trying to do CEP inhouse. Sybase is making a good choice in having serious CEP inhouse itself
- Surely the main focus and financial justification for the Sybase/Aleri acquisition is the financial services market.
- Specifically, I expect the focus of technical integration between Aleri and Sybase’s DBMS products to start with Sybase IQ.
- Coral8 had some interesting ideas about how to integrate CEP with OLTP/operational BI, but I’m not aware that they got much traction.
- I bet there are use cases where Sybase tries and fails to sell Adaptive Server SQL Anywhere that CEP would be a better technical fit, but I don’t immediately see much practical business significance to that observation.
- While this deal could easily strengthen the Vertica/StreamBase partnership, I don’t see any reason why it would lead those two companies to actually merge.
Related link
| Categories: Aleri and Coral8, Analytic technologies, Complex event processing (CEP), Investment research and trading, Sybase | 7 Comments |
Three broad categories of data
People often try to draw a distinction between:
- Traditional data of the sort that’s stored in relational databases, aka “structured.”
- Everything else, aka “unstructured” or “semi-structured” or “complex.”
There are plenty of problems with these formulations, not the least of which is that the supposedly “unstructured” data is the kind that actually tends to have interesting internal structures. But of the many reasons why these distinctions don’t tend to work very well, I think the most important one is that:
Databases shouldn’t be divided into just two categories. Even as a rough-cut approximation, they should be divided into three, namely:
- Human/Tabular data –i.e., human-generated data that fits well into relational tables or arrays
- Human/Nontabular data — i.e., all other data generated by humans
- Machine-Generated data
Even that trichotomy is grossly oversimplified, for reasons such as:
- These categories overlap.
- There are kinds of data that get into fuzzy border zones.
- Not all data in each category has all the same properties.
But at least as a starting point, I think this basic categorization has some value. Read more
| Categories: Database diversity, Investment research and trading, Log analysis, Telecommunications, Web analytics | 6 Comments |
There sure seem to be a lot of inaccuracies on ParAccel’s website
In what is actually an interesting post on database compression, ParAccel CTO Barry Zane threw in
Anyone who has met with us knows ParAccel shies away from hype.
But like many things ParAccel says, that is not true.
The latest whoppers came in the form of several customers ParAccel listed on its website who hadn’t actually bought ParAccel’s DBMS, nor even decided to do so. It is fairly common to to claim a customer win, then retract the claim due to lack of permission to disclose. But that’s not what happened in these cases. Based on emails helpfully shared by a ParAccel competitor competing in some of those accounts, it seems clear that ParAccel actually posted fabricated claims of customer wins. Read more
| Categories: Columnar database management, Data warehousing, Database compression, Market share, ParAccel, Telecommunications | 21 Comments |
Notes on RainStor, the company formerly known as Clearpace
Information preservation* DBMS vendor Clearpace officially changed its name to RainStor this week. RainStor is also relocating its CEO John Bantleman and more generally its headquarters to San Francisco. This all led to a visit with John and his colleague Ramon Chen, highlights of which included: Read more
| Categories: Archiving and information preservation, Clearpace, Market share, Oracle, SenSage, Telecommunications | Leave a Comment |
A framework for thinking about data warehouse growth
There are only three ways that the amount of data stored in data warehouses can grow:
- The same kinds of data are stored as before, with more being added over time.
- The same kinds of data are stored as before, but in more detail.
- New kinds of data are stored.
| Categories: Analytic technologies, Application areas, Data warehousing, Investment research and trading, Log analysis, Solid-state memory, Storage, Telecommunications, Text, Web analytics | 7 Comments |
Webinar on MapReduce for complex analytics (Thursday, December 3, 10 am and 2 pm Eastern)
The second in my two-webinar series for Aster Data will occur tomorrow, twice (both live), at 10 am and 2 pm Eastern time. The other presenters will be Jonathan Goldman, who was Principal Scientist at LinkedIn but now has joined Aster himself, and Steve Wooledge of Aster (playing host). Key links are:
- Registration for tomorrow’s webinars
- Replay of the first webinar
- My slides from the first webinar
The main subjects of the webinar will be:
- Some review of material from the first webinar (all three presenters)
- Discussion of how MapReduce can help with three kinds of analytics:
- Pattern matching (Jonathan will give detail)
- Number-crunching (I’ll cover that, and it will be short)
- Graph analytics (I haven’t written the slides yet, but my starting point will be some of the relationship analytics ideas we discussed in August)
Arguably, aspects of data transformation fit into each of those three categories, which may help explain why data transformation has been so prominent among the early applications of MapReduce.
As you can see from Aster’s title for the webinar (which they picked while I was on vacation), at least their portion will be focused on customer analytics, e.g. web analytics.
| Categories: Analytic technologies, Aster Data, Data integration and middleware, EAI, EII, ETL, ELT, ETLT, MapReduce, RDF and graphs, Web analytics | 2 Comments |
Boston Big Data Summit keynote outline
Last month, Bob Zurek asked me to give a talk on “Big Data”, where “big” is anything from a few terabytes on up, then moderate a panel on cloud computing. We agreed that I could talk just from notes, without slides. So, since I have them typed up, I’m posting them below.
Greenplum Single-Node Edition — sometimes free is a real cool price
Greenplum is announcing today that you can run Greenplum software on a single 8-core commodity server, free. First and foremost, that’s a strong statement that Greenplum wants enterprises to pay it for Greenplum’s parallelization/”private cloud” capabilities. Second, it may be an attractive gift to a variety of folks who want to extract insight from terabyte-scale databases of various kinds.
Greenplum Single-Node Edition:
- Is free of charge, although you can buy support.
- Has no restrictions on use, production or otherwise.
- Has no restrictions on database size.
- Is closed-source.
For those who want free, terabyte-scale data warehousing software, Greenplum Single-Node Edition may be quite appealing, considering that the main available alternatives are:
- General-purpose open-source DBMS, such as PostgreSQL and MySQL (lacking analytic DBMS performance and features)
- Infobright Community Edition (the other best choice – Infobright’s commercial sales success indicates the solidity of Infobright’s technology)
- Rough research-project code and other other questionable open source offerings
- Crippleware from other commercial analytic DBMS vendors (e.g., Teradata)
For example, comparing PostgreSQL-based Greenplum with PostgreSQL itself, Greenplum offers:
- The ability to scale out queries across all cores in your box (and no, pgpool is not a serious alternative)
- Storage alternatives such as columnar (I am told that EnterpriseDB recently stopped funding a project for a PostgreSQL columnar option)
| Categories: Analytic technologies, Data warehousing, EnterpriseDB and Postgres Plus, Greenplum, Infobright, Open source, PostgreSQL, Pricing, Scientific research | 9 Comments |
Three big myths about MapReduce
Once again, I find myself writing and talking a lot about MapReduce. But I suspect that MapReduce-related conversations would go better if we overcame three fairly common MapReduce myths:
- MapReduce is something very new
- MapReduce involves strict adherence to the Map-Reduce programming paradigm
- MapReduce is a single technology