My own data management software taxonomy
On a recent webcast, I presented an 11-node data management software taxonomy, updating a post commenting on Mike Stonebraker’s. It goes:
1. High-end OLTP/general-purpose DBMS
2. Mid-range OLTP/general-purpose DBMS
3. Row-based analytic RDBMS
4. Column- or array-based analytic RDBMS
5. Text search engines
6. XML and OO DBMS (but these may merge with search)
7. RDF and other graphical DBMS (but these may merge with relational)
8. Event/stream processing engines (aka CEP)
9. Embedded DBMS for devices
10. Sub-DBMS file managers (e.g. MapReduce/Hadoop)
11. Science DBMS
Obviously, this is a work in progress. In particular, while there’s clearly more than one kind of analytic DBMS, partitioning them into categories is not easy.
Comments
5 Responses to “My own data management software taxonomy”
Leave a Reply
I would be curious where you would place InterSystems Cache’ and IBM U2 databases (UniData and UniVerse). I classify them both as NF2 (Non-First Normal Form) databases, or possible MultiValue databases, even if these are not particularly hip 21st century designations. These products have an SQL interface and also other means, such as object, XML, and/or PICK languages for working with the DBMS. They are often classifed as “embedded,” which is not altogether accurate and is certainly not a comprehensive classification. When it comes to scalability, performance, reliability, etc, I would think at least the first of these would be in your 1. High-end OLTP/general-purpose DBMS category, although you could decide to classify Cache’ as 6. XML and OO DBMS (but these may merge with search) too, for example.
Since your categories are not “normalized” if you are trying to put each DBMS into just one of the buckets, rather than tagging them in multiple categories (and perhaps you are permitting a multivalued list of categories), you would be eliminating the possibility of a High-end OLTP general purpose DBMS for any products fitting another category.
So, I would suggest separating out matters of use and scalability from indications of the related data models used in the DBMS developer APIs/languages. Maybe a “pick one from the scalability column, one or more from the developer APIs/languages column, and one from the primary purpose column” would still be simple enough?
Dawn,
They’re squarely in Category 2. Nowhere there is a relational model assumed.
Best,
CAM
Cache’ self-declares as an OO DBMS and, if I recall, Gartner has them (at least has U2) as embedded. So, using those same terms to mean something different would be at the very least confusing, I would think. I am also curious what precisely puts it in the mid-range compared to the high-end. Is that an eye-of-the-beholder thing or are there specific benchmarks you have in mind?
[…] My most recent 11-category data management technology taxonomy […]
[…] covered on the first 13 slides should be very familiar to readers of this blog. I touched on database diversity and the disk-speed barrier, after which I zoomed through a quick survey of the data warehouse DBMS […]