RDF and graphs

Analysis of data management technology optimized for RDF-formatted and/or graph data.

February 1, 2010

Open issues in database and analytic technology

The last part of my New England Database Summit talk was on open issues in database and analytic technology. This was closely intertwined with the previous section, and also relied on a lot that I’ve posted here. So I’ll just put up a few notes on that part, with lots of linkage to prior discussion of the same points. Read more

December 2, 2009

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 Principal Scientist at LinkedIn but now has joined Aster himself, and Steve Wooledge of Aster (playing host). Key links are:

The main subjects of the webinar will be:

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.

August 21, 2009

Social network analysis, aka relationship analytics

A number of applications lend themselves to graph-oriented analytics, including:

There are plenty more graph-oriented applications, of course, such as the identification of biochemical pathways. But I want to focus for now on ones like those on my list. My key points are:

Here’s what I mean. Read more

April 15, 2009

Cloudera presents the MapReduce bull case

Monday was fire-drill day regarding MapReduce vs. MPP relational DBMS. The upshot was that I was quoted in Computerworld and paraphrased in GigaOm as being a little more negative on MapReduce than I really am, in line with my comment

Frankly, my views on MapReduce are more balanced than [my] weary negativity would seem to imply.

Tuesday afternoon the dial turned a couple notches more positive yet, when I talked with Michael Olson and Jeff Hammerbacher of Cloudera. Cloudera is a new company, built around the open source MapReduce implementation Hadoop. So far Cloudera gives away its Hadoop distribution, without charging for any sort of maintenance or subscription, and just gets revenue from professional services. Presumably, Cloudera plans for this business model to change down the road.

Much of our discussion revolved around Facebook, where Jeff directed a huge and diverse Hadoop effort. Apparently, Hadoop played much of the role of an enterprise data warehouse at Facebook — at least for clickstream/network data — including:

Some Facebook data, however, was put into an Oracle RAC cluster for business intelligence. And Jeff does concede that query execution is slower in Hadoop than in a relational DBMS. Hadoop was also used to build the index for Facebook’s custom text search engine.

Jeff’s reasons for liking Hadoop over relational DBMS at Facebook included: Read more

September 23, 2008

Oracle spotlights its datatype support

Oracle put out a flurry of press releases today in conjunction with Oracle OpenWorld. One, which was simply positioned as a report on some “mission-critical” customer apps, caught my eye because all four detailed examples involved nonstandard datatypes:

August 26, 2008

Known applications of MapReduce

Most of the actual MapReduce applications I’ve heard of fall into a few areas:

That covers all MapReduce apps I recall hearing about via commercial companies and users, and also includes most of what’s in the two big sources I found online. Read more

February 16, 2008

Mike Stonebraker’s DBMS taxonomy

In a response to my recent five-part series on DBMS diversity, Mike Stonebraker has proposed his own taxonomy of data management technologies over on Vertica’s Database Column blog.

  1. OLTP DBMSs focused on fast, reliable transaction processing
  2. Analytic/Data Warehouse DBMSs focused on efficient load and ad-hoc query performance
  3. Science DBMSs — after all MatLab does not scale to disk-sized arrays
  4. RDF stores focused on efficiently storing semi-structured data in this format
  5. XML stores focused on semi-structured data in this format
  6. Search engines — the big players all use proprietary engines in this area
  7. Stream Processing Engines focused on real-time StreamSQL
  8. “Lean and Mean,” less-than-a-database engines focused on doing a small number of things very well (embedded databases are probably in this category)
  9. MapReduce and Hadoop — after all Google has enough “throw weight” to define a category

He goes on to say that each will be architected differently, except that — as he already convinced me back in July — RDF will be well-managed by specialty data warehouse DBMS. Read more

November 7, 2007

Vertica update – HP appliance deal, customer information, and more

Vertica quietly announced an appliance bundling deal with HP and Red Hat today. That got me quickly onto the phone with Vertica’s Andy Ellicott, to discuss a few different subjects. Most interesting was the part about Vertica’s customer base, highlights of which included:

Read more

July 13, 2007

Nonstandard data management software — beyond the Bowling Alley?

I just finished a short Monash Letter on markets for nonstandard data management software. Of course, the whole thing is available only to Monash Advantage members, but here are some salient points:

June 15, 2007

Fast RDF in specialty relational databases

When Mike Stonebraker and I discussed RDF yesterday, he quickly turned to suggesting fast ways of implementing it over an RDBMS. Then, quite characteristically, he sent over a paper that allegedly covered them, but actually was about closely related schemes instead. :) Edit: The paper has a new, stable URL. Hat tip to Daniel Abadi.

All minor confusion aside, here’s the story. At its core, an RDF database is one huge three-column table storing subject-property-object triples. In the naive implementation, you then have to join this table to itself repeatedly. Materialized views are a good start, but they only take you so far. Read more

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