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
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 | 19 Comments |
Intersystems Cache’ highlights
I talked with Robert Nagle of Intersystems last week, and it went better than at least one other Intersystems briefing I’ve had. Intersystems’ main product is Cache’, an object-oriented DBMS introduced in 1997 (before that Intersystems was focused on the fourth-generation programming language M, renamed from MUMPS). Unlike most other OODBMS, Cache’ is used for a lot of stuff one would think an RDBMS would be used for, across all sorts of industries. That said, there’s a distinct health-care focus to Intersystems, in that:
- MUMPS, the original Intersystems technology, was focused on health care.
- The reasons Intersystems went object-oriented have a lot to do with the structure of health-care records.
- Intersystems’ biggest and most visible ISVs are in the health-care area.
- Intersystems is actually beginning to sell an electronic health records system called TrakCare around the world (but not in the US, where it has lots of large competitive VARs).
Note: Intersystems Cache’ is sold mainly through VARs (Value-Added Resellers), aka ISVs/OEMs. I.e., it’s sold by people who write applications on top of it.
So far as I understand – and this is still pretty vague and apt to be partially erroneous – the Intersystems Cache’ technical story goes something like this: Read more
| Categories: Data models and architecture, Emulation, transparency, portability, Health care, Intersystems and Cache', Mid-range, Object, OLTP, Sybase, Theory and architecture | 8 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.
Edit (October, 2010): Like other posts I’ve linked to from Barry Zane’s blog, that one seems to be gone, with the URL redirecting elsewhere on ParAccel’s website.
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 and customer counts, ParAccel, Telecommunications | 24 Comments |
Introduction to Gooddata
Around the end of the Cold War, Esther Dyson took it upon herself to go repeatedly to Eastern Europe and do a lot of rah-rah and catalysis, hoping to spark software and other computer entrepreneurs. I don’t know how many people’s lives she significantly affected – I’d guess it’s actually quite a few – but in any case the number is not zero. Roman Stanek, who has built and sold a couple of software business, cites her as a key influence setting him on his path.
Roman’s latest venture is business intelligence firm Gooddata. Gooddata was founded in 2007 and has been soliciting and getting attention for a while, so I was surprised to learn that Gooddata officially launched just a few weeks ago. Anyhow, some less technical highlights of the Gooddata story include: Read more
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, Market share and customer counts, Oracle, Rainstor, SenSage, Telecommunications | 1 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 | 9 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 a 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 | 4 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 | 14 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