Discussion of choices and variety in database management system architecture. Related subjects include:
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 The old EDW/data mart dichotomy is hopelessly outdated for that purpose, and adding a third category for “big data” is little help.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
There are plenty of viable alternatives to relational database management systems. For short-request processing, both document stores and fully object-oriented DBMS can make sense. Text search engines have an important role to play. E. F. “Ted” Codd himself once suggested that relational DBMS weren’t best for analytics.* Analysis of machine-generated log data doesn’t always have a naturally relational aspect. And I could go on with more examples yet.
*Actually, he didn’t admit that what he was advocating was a different kind of DBMS, namely a MOLAP one — but he was. And he was wrong anyway about the necessity for MOLAP. But let’s overlook those details.
Nonetheless, relational DBMS dominate the market. As I see it, the reasons for relational dominance cluster into four areas (which of course overlap):
- Data re-use. Ted Codd’s famed original paper referred to shared data banks for a reason.
- The benefits of normalization, which include:
- You only have to do programming work of writing something once …
- … and you don’t have to do the programming work of keeping multiple versions of the information consistent.
- You only have to do processing work of writing something once.
- You only have to buy storage to hold each fact once.
- Separation of concerns.
- Different people can worry about programming and “database stuff.”
- Indeed, even performance optimization can sometimes be separated from programming (i.e., when all you have to do to get speed is implement the correct indexes).
- Maturity and momentum, as reflected in the availability of:
- A broad variety of mature relational DBMS.
- Vast amounts of packaged software that “talks” SQL.
Generally speaking, I find the reasons for sticking with relational technology compelling in cases such as: Read more
|Categories: Analytic technologies, Data models and architecture, Database diversity, MOLAP, NoSQL, Object, Theory and architecture||18 Comments|
My NoSQL article is finally posted; I hope it lives up to all the foreshadowing. It is being run online at Intelligent Enterprise/Information Week, as per the link above, where Doug Henschen edited it with an admirably light touch.
Below please find three excerpts* that convey the essence of my thinking on NoSQL. For much more detail, please see the article itself.
*Notwithstanding my admiration for Doug’s editing, the excerpts are taken from my final pre-editing submission, not from the published article itself.
My quasi-definition of “NoSQL” wound up being: Read more
After I criticized the marketing of the Aster/Cloudera partnership, my clients at Aster Data and Cloudera ganged up on me and tried to persuade me I was wrong. Be that as it may, that conversation and others were helpful to me in understanding the core thesis: Read more
|Categories: Analytic technologies, Aster Data, Cloudera, Data warehousing, Database diversity, Hadoop, MapReduce, Parallelization, Petabyte-scale data management||11 Comments|
An enterprise data warehouse should:
- Manage data to high standards of accuracy, consistency, cleanliness, clarity, and security.
- Manage all the data in your organization.
Pick ONE. Read more
|Categories: Data models and architecture, Data warehousing, Database diversity, Teradata, Theory and architecture||8 Comments|
Let’s start from some reasonable premises. Read more
|Categories: Data models and architecture, Database diversity, Hadoop, MapReduce, MarkLogic, NoSQL, OLTP, Theory and architecture||37 Comments|
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|
I’ve written some snarky things about the “NoSQL” concept – or at least the moniker. (Carl Olofson’s term “non-schematic databases” seems less bad.) Yet I’m actually favorable about the increasing use of SQL alternatives. Perhaps I should pull those thoughts together. Read more
|Categories: Data models and architecture, Database diversity, Hadoop, NoSQL, Theory and architecture||20 Comments|
Neal Leavitt is writing an article for IEEE on NoSQL. So he’s circulated a long list of questions, encouraging people to answer as many or few as they choose. Unfortunately, most of the questions are technically meaningless, in that they implicitly rely on the false assumption that there is such a thing as a single or at least reasonably well-defined NoSQL technology. (I imagine most of his questions are really about key-value stores.) Nonetheless, I took a crack at a number of them before getting bored. Anybody else want to pitch in too? Read more
|Categories: Data models and architecture, Database diversity, NoSQL, Theory and architecture||10 Comments|
MapReduce is definitely gaining traction, especially but by no means only in the form of Hadoop. In the aftermath of Hadoop World, Jeff Hammerbacher of Cloudera walked me quickly through 25 customers he pulled from Cloudera’s files. Facts and metrics ranged widely, of course:
- Some are in heavy production with Hadoop, and closely engaged with Cloudera. Others are active Hadoop users but are very secretive. Yet others signed up for initial Hadoop training last week.
- Some have Hadoop clusters in the thousands of nodes. Many have Hadoop clusters in the 50-100 node range. Others are just prototyping Hadoop use. And one seems to be “OEMing” a small Hadoop cluster in each piece of equipment sold.
- Many export data from Hadoop to a relational DBMS; many others just leave it in HDFS (Hadoop Distributed File System), e.g. with Hive as the query language, or in exactly one case Jaql.
- Some are household names, in web businesses or otherwise. Others seem to be pretty obscure.
- Industries include financial services, telecom (Asia only, and quite new), bioinformatics (and other research), intelligence, and lots of web and/or advertising/media.
- Application areas mentioned — and these overlap in some cases — include:
- Log and/or clickstream analysis of various kinds
- Marketing analytics
- Machine learning and/or sophisticated data mining
- Image processing
- Processing of XML messages
- Web crawling and/or text processing
- General archiving, including of relational/tabular data, e.g. for compliance