Data models and architecture
Discussion of issues in data modeling, and whether databases should be consolidated or loosely coupled. Related subjects include:
Notes and links, June 15, 2011
Five things: Read more
Another category of derived data
Six months ago, I argued the importance of derived analytic data, saying
… there’s no escaping the importance of derived/augmented/enhanced/cooked/adjusted data for analytic data processing. The five areas I have in mind are, loosely speaking:
- Aggregates, when they are maintained, generally for reasons of performance or response time.
- Calculated scores, commonly based on data mining/predictive analytics.
- Text analytics.
- The kinds of ETL (Extract/Transform/Load) Hadoop and other forms of MapReduce are commonly used for.
- Adjusted data, especially in scientific contexts.
Probably there are yet more examples that I am at the moment overlooking.
Well, I did overlook at least one category. 🙂
A surprisingly important kind of derived data is metadata, especially for large, poly-structured data sets. For example, CERN has vastly quantities of experiment sensor data, stored as files; just the metadata alone fills over 10 terabytes in an Oracle database. MarkLogic is big on storing derived metadata, both on the publishing/media and intelligence sides of the business.
| Categories: Data models and architecture, Derived data, Hadoop, MarkLogic | 2 Comments |
When it’s still best to use a relational DBMS
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:
- People.
- 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 | 21 Comments |
Starcounter high-speed memory-centric object-oriented DBMS, coming soon
Since posting recently about Starcounter, I’ve had the chance to actually talk with the company (twice). Hence I know more than before. 🙂 Starcounter:
- Has been around as a company since 2006.
- Has developed memory-centric object-oriented DBMS technology that has been OEMed by a few application software companies (especially in bricks-and-mortar retailing and in online advertising).
- Is planning to actually launch an OODBMS product sometime this summer.
- Has 14 employees (most or all of whom are in Sweden, which is also where I think Starcounter’s current customers are centered).
- Is planning to shift emphasis soon to the US market.
Starcounter’s value propositions are programming ease (no object/relational impedance mismatch) and performance. Starcounter believes its DBMS has 100X the performance of conventional DBMS at short-request transaction processing, and 10X the performance of other memory-centric and/or object-oriented DBMS (e.g. Oracle TimesTen, or Versant). That said, Starcounter has not yet tested VoltDB. Starcounter does not claim performance much beyond that of disk-based DBMS on analytic tasks such as aggregations.
The key technical aspect to Starcounter is integration between the DBMS and the virtual machine, so that the same copy of the data is accessed by both the DBMS and the application program, without any movement or transformation being needed. (Starcounter isn’t aware of any other object-oriented DBMS that work this way.) Transient and persistent data are handled in the same way, seamlessly.
Other Starcounter technical highlights include: Read more
| Categories: Data models and architecture, In-memory DBMS, Memory-centric data management, Object, OLTP, Starcounter, Theory and architecture | 3 Comments |
What to do about “unstructured data”
We hear much these days about unstructured or semi-structured (as opposed to) structured data. Those are misnomers, however, for at least two reasons. First, it’s not really the data that people think is un-, semi-, or fully structured; it’s databases.* Relational databases are highly structured, but the data within them is unstructured — just lists of numbers or character strings, whose only significance derives from the structure that the database imposes.
*Here I’m using the term “database” literally, rather than as a concise synonym for “database management system”. But see below.
Second, a more accurate distinction is not whether a database has one structure or none — it’s whether a database has one structure or many. The easiest way to see this is for databases that have clearly-defined schemas. A relational database has one schema (even if it is just the union of various unrelated sub-schemas); an XML database, however, can have as many schemas as it contains documents.
One small terminological problem is easily handled, namely that people don’t talk about true databases very often, at least when they’re discussing generalities; rather, they talk about data and DBMS.* So let’s talk of DBMS being “structured” singly or multiply or whatever, just as the databases they’re designed to manage are.
*And they refer to the DBMS as “databases,” because they don’t have much other use for the word.
All that said — I think that single vs. multiple database structures isn’t a bright-line binary distinction; rather, it’s a spectrum. For example: Read more
| Categories: Cassandra, Couchbase, Data models and architecture, HBase, IBM and DB2, MarkLogic, MongoDB, NoSQL, Splunk, Theory and architecture | 19 Comments |
The MongoDB story
Along with CouchDB/Couchbase, MongoDB was one of the top examples I had in mind when I wrote about document-oriented NoSQL. Invented by 10gen, MongoDB is an open source, no-schema DBMS, so it is suitable for very quick development cycles. Accordingly, there are a lot of MongoDB users who build small things quickly. But MongoDB has heftier uses as well, and naturally I’m focused more on those.
MongoDB’s data model is based on BSON, which seems to be JSON-on-steroids. In particular:
- You just bang things into single BSON objects managed by MongoDB; there is nothing like a foreign key to relate objects. However …
- … there are fields, datatypes, and so on within MongoDB BSON objects. The fields are indexed.
- There’s a multi-value/nested-data-structure flavor to MongoDB; for example, a BSON object might store multiple addresses in an array.
- You can’t do joins in MongoDB. Instead, you are encouraged to put what might be related records in a relational database into a single MongoDB object. If that doesn’t suffice, then use client-side logic to do the equivalent of joins. If that doesn’t suffice either, you’re not looking at a good MongoDB use case.
| Categories: Clustering, Data models and architecture, MapReduce, MongoDB, NoSQL, Parallelization | 10 Comments |
Workday comments on its database architecture
In my discussion of Workday’s technology, I gave an estimate that Workday’s database, if relationally designed, would require “1000s” of tables. That estimate came from Workday, Inc. CTO Stan Swete, in a thoughtful email that made several points about Workday’s database strategy. Workday kindly gave me permission to quote it below.
Read more
| Categories: Data models and architecture, Object, OLTP, Software as a Service (SaaS), Specific users, Theory and architecture, Workday | 3 Comments |
The Workday architecture — a new kind of OLTP software stack
One of my coolest company visits in some time was to SaaS (Software as a Service) vendor Workday, Inc., earlier this month. Reasons included:
- Workday has forward-thinking ideas about SaaS enterprise applications and the integration of business intelligence into same.
- Workday has highly innovative ideas in how it manages data.
- Companies founded by Dave Duffield tend to feature smart, likeable people who talk to one pleasantly and forthrightly. Workday is no exception; CTO Stan Swete and the other Workday folks present were a delight to talk with.
- I’d invited Merv Adrian to come along with me. He asked great questions, and I could gather myself a bit despite how sleep-deprived I was for the first part of that trip.
Workday kindly allowed me to post this Workday slide deck. Otherwise, I’ve split out a quick Workday, Inc. company overview into a separate post.
The biggie for me was the data and object management part. Specifically: Read more
Extended set theory, aka “What is a tuple anyway?”
The Algebraix folks are trying to repopularize David Childs’ idea of “Extended set theory.” In a nutshell, the extended set theory idea is:
A tuple is a set of (field-name, field-value) pairs.
I’ve been fairly negative about the extended set theory concept – but in fairness, that may be because I misunderstood how other people thought of tuples. Any time I’ve had to formalize what I thought of a tuple as being, I came up with something very much like the above, except that if one wants to be relational one needs a requirement like:
In any one tuple, each field-name must be unique.
In line with that definition, I’d say a table is something like: Read more
Is the enterprise data warehouse a myth?
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