Data types

Analysis of data management technology optimized for specific datatypes, such as text, geospatial, object, RDF, or XML. Related subjects include:

October 24, 2013

JSON in Teradata

I coined the term schema-on-need last month. More precisely, I coined it while being briefed on JSON-in-Teradata, which was announced earlier this week, and is slated for availability in the first half of 2014.

The basic JSON-in-Teradata story is as you expect:

JSON virtual columns are referenced a little differently than ordinary physical columns are. Thus, if you materialize a virtual column, you have to change your SQL. If you’re doing business intelligence through a semantic layer, or otherwise have some kind of declarative translation, that’s probably not a big drawback. If you’re coding analytic procedures directly, it still may not be a big drawback — hopefully you won’t reference the virtual column too many times in code before you decide to materialize it instead.

My Bobby McFerrin* imitation notwithstanding, Hadapt illustrates a schema-on-need approach that is slicker than Teradata’s in two ways. First, Hadapt has full SQL transparency between virtual and physical columns. Second, Hadapt handles not just JSON, but anything represented by key-value pairs. Still, like XML before it but more concisely, JSON is a pretty versatile data interchange format. So JSON-in-Teradata would seem to be useful as it stands.

*The singer in the classic 1988 music video Don’t Worry Be Happy. The other two performers, of course, were Elton John and Robin Williams.

October 10, 2013

Aster 6, graph analytics, and BSP

Teradata Aster 6 has been preannounced (beta in Q4, general release in Q1 2014). The general architectural idea is:

There’s much more, of course, but those are the essential pieces.

Just to be clear: Teradata Aster 6, aka the Teradata Aster Discovery Platform, includes HDFS compatibility, native MapReduce and ways of invoking Hadoop MapReduce on non-Aster nodes or clusters — but even so, you can’t run Hadoop MapReduce within Aster over Aster’s version of HDFS.

The most dramatic immediate additions are in the graph analytics area.* The new SQL-Graph is supported by something called BSP (Bulk Synchronous Parallel). I’ll start by observing (and some of this is confusing):

Use cases suggested are a lot of marketing, plus anti-fraud.

*Pay no attention to Aster’s previous claims to do a good job on graph — and not only via nPath — in SQL-MR.

So far as I can infer from examples I’ve seen, the semantics of Teradata Aster SQL-Graph start:

Within those functions, the core idea is:  Read more

October 10, 2013

Libraries in Teradata Aster

I recently wrote (emphasis added):

My clients at Teradata Aster probably see things differently, but I don’t think their library of pre-built analytic packages has been a big success. The same goes for other analytic platform vendors who have done similar (generally lesser) things. I believe that this is because such limited libraries don’t do enough of what users want.

The bolded part has been, shall we say, confirmed. As Randy Lea tells it, Teradata Aster sales qualification includes the determination that at least one SQL-MR operator — be relevant to the use case. (“Operator” seems to be the word now, rather than “function”.) Randy agreed that some users prefer hand-coding, but believes a large majority would like to push work to data analysts/business analysts who might have strong SQL skills, but be less adept at general mathematical programming.

This phrasing will all be less accurate after the release of Aster 6, which extends Aster’s capabilities beyond the trinity of SQL, the SQL-MR library, and Aster-supported hand-coding.

Randy also said:

And Randy seemed to agree when I put words in his mouth to the effect that the prebuilt operators save users months of development time.

Meanwhile, Teradata Aster has started a whole new library for relationship analytics.

September 24, 2013

JSON in DB2

There’s a growing trend for DBMS to beef up their support for multiple data manipulation languages (DMLs) or APIs — and there’s a special boom in JSON support, MongoDB-compatible or otherwise. So I talked earlier tonight with IBM’s Bobbie Cochrane about how JSON is managed in DB2.

For starters, let’s note that there are at least four strategies IBM could have used.

IBM’s technology choices are of course influenced by its use case focus. It’s reasonable to divide MongoDB use cases into two large buckets:

IBM’s DB2 JSON features are targeted at the latter bucket. Also, I suspect that IBM is generally looking for a way to please users who enjoy working on and with their MongoDB skills.  Read more

September 21, 2013

Schema-on-need

Two years ago I wrote about how Zynga managed analytic data:

Data is divided into two parts. One part has a pretty ordinary schema; the other is just stored as a huge list of name-value pairs. (This is much like eBay‘s approach with its Teradata-based Singularity, except that eBay puts the name-value pairs into long character strings.) … Zynga adds data into the real schema when it’s clear it will be needed for a while.

What was then the province of a few huge web companies is now poised to be a broader trend. Specifically:

That migration from virtual to physical columns is what I’m calling “schema-on-need”. Thus, schema-on-need is what you invoke when schema-on-read no longer gets the job done. ;)

Read more

September 8, 2013

Layering of database technology & DBMS with multiple DMLs

Two subjects in one post, because they were too hard to separate from each other

Any sufficiently complex software is developed in modules and subsystems. DBMS are no exception; the core trinity of parser, optimizer/planner, and execution engine merely starts the discussion. But increasingly, database technology is layered in a more fundamental way as well, to the extent that different parts of what would seem to be an integrated DBMS can sometimes be developed by separate vendors.

Major examples of this trend — where by “major” I mean “spanning a lot of different vendors or projects” — include:

Other examples on my mind include:

And there are several others I hope to blog about soon, e.g. current-day PostgreSQL.

In an overlapping trend, DBMS increasingly have multiple data manipulation APIs. Examples include:  Read more

September 3, 2013

The Hemisphere program

Another surveillance slide deck has emerged, as reported by the New York Times and other media outlets. This one is for the Hemisphere program, which apparently:

Other notes include:

I’ve never gotten a single consistent figure, but typical CDR size seems to be in the 100s of bytes range. So I conjecture that Project Hemisphere spawned one of the first petabyte-scale databases ever.

Hemisphere Project unknowns start:  Read more

August 25, 2013

Cloudera Hadoop strategy and usage notes

When we scheduled a call to talk about Sentry, Cloudera’s Charles Zedlewski and I found time to discuss other stuff as well. One interesting part of our discussion was around the processing “frameworks” Cloudera sees as most important.

HBase was artificially omitted from this “frameworks” discussion because Cloudera sees it as a little bit more of a “storage” system than a processing one.

Another good subject was offloading work to Hadoop, in a couple different senses of “offload”: Read more

August 24, 2013

Hortonworks business notes

Hortonworks did a business-oriented round of outreach, talking with at least Derrick Harris and me. Notes  from my call — for which Rob Bearden didn’t bother showing up — include, in no particular order:

In Hortonworks’ view, Hadoop adopters typically start with a specific use case around a new type of data, such as clickstream, sensor, server log, geolocation, or social.  Read more

August 4, 2013

Data model churn

Perhaps we should remind ourselves of the many ways data models can be caused to churn. Here are some examples that are top-of-mind for me. They do overlap a lot — and the whole discussion overlaps with my post about schema complexity last January, and more generally with what I’ve written about dynamic schemas for the past several years..

Just to confuse things further — some of these examples show the importance of RDBMS, while others highlight the relational model’s limitations.

The old standbys

Product and service changes. Simple changes to your product line many not require any changes to the databases recording their production and sale. More complex product changes, however, probably will.

A big help in MCI’s rise in the 1980s was its new Friends and Family service offering. AT&T couldn’t respond quickly, because it couldn’t get the programming done, where by “programming” I mainly mean database integration and design. If all that was before your time, this link seems like a fairly contemporaneous case study.

Organizational changes. A common source of hassle, especially around databases that support business intelligence or planning/budgeting, is organizational change. Kalido’s whole business was based on accommodating that, last I checked, as were a lot of BI consultants’. Read more

← Previous PageNext Page →

Feed: DBMS (database management system), DW (data warehousing), BI (business intelligence), and analytics technology Subscribe to the Monash Research feed via RSS or email:

Login

Search our blogs and white papers

Monash Research blogs

User consulting

Building a short list? Refining your strategic plan? We can help.

Vendor advisory

We tell vendors what's happening -- and, more important, what they should do about it.

Monash Research highlights

Learn about white papers, webcasts, and blog highlights, by RSS or email.