Data types

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

October 22, 2014

Snowflake Computing

I talked with the Snowflake Computing guys Friday. For starters:

Much of the Snowflake story can be summarized as cloud/elastic/simple/cheap.*

*Excuse me — inexpensive. Companies rarely like their products to be labeled as “cheap”.

In addition to its purely relational functionality, Snowflake accepts poly-structured data. Notes on that start:

I don’t know enough details to judge whether I’d call that an example of schema-on-need.

A key element of Snowflake’s poly-structured data story seems to be lateral views. I’m not too clear on that concept, but I gather: Read more

September 21, 2014

Data as an asset

We all tend to assume that data is a great and glorious asset. How solid is this assumption?

*”Our assets are our people, capital and reputation. If any of these is ever diminished, the last is the most difficult to restore.” I love that motto, even if Goldman Sachs itself eventually stopped living up to it. If nothing else, my own business depends primarily on my reputation and information.

This all raises the idea – if you think data is so valuable, maybe you should get more of it. Areas in which enterprises have made significant and/or successful investments in data acquisition include:  Read more

May 6, 2014

Notes and comments, May 6, 2014

After visiting California recently, I made a flurry of posts, several of which generated considerable discussion.

Here is a catch-all post to complete the set.  Read more

April 17, 2014

MongoDB is growing up

I caught up with my clients at MongoDB to discuss the recent MongoDB 2.6, along with some new statements of direction. The biggest takeaway is that the MongoDB product, along with the associated MMS (MongoDB Management Service), is growing up. Aspects include:

Read more

March 6, 2014

Splunk and inverted-list indexing

Some technical background about Splunk

In an October, 2009 technical introduction to Splunk, I wrote (emphasis added):

Splunk software both reads logs and indexes them. The same code runs both on the nodes that do the indexing and on machines that simply emit logs.

It turns out that the bolded part was changed several years ago. However, I don’t have further details, so let’s move on to Splunk’s DBMS-like aspects.

I also wrote:

The fundamental thing that Splunk looks at is an increment to a log – i.e., whatever has been added to the log since Splunk last looked at it.

That remains true. Confusingly, Splunk refers to these log increments as “rows”, even though they’re really structured and queried more like documents.

I further wrote:

Splunk has a simple ILM (Information Lifecycle management) story based on time. I didn’t probe for details.

Splunk’s ILM story turns out to be simple indeed.

Finally, I wrote:

I get the impression that most Splunk entity extraction is done at search time, not at indexing time. Splunk says that, if a <name, value> pair is clearly marked, its software does a good job of recognizing same. Beyond that, fields seem to be specified by users when they define searches.

and

I have trouble understanding how Splunk could provide flexible and robust reporting unless it tokenized and indexed specific fields more aggressively than I think it now does.

The point of what I in October, 2013 called

a high(er)-performance data store into which you can selectively copy columns of data

and which Splunk enthusiastically calls its “High Performance Analytic Store” is to meet that latter need.

Inverted-list indexing

Inverted list technology is confusing for several reasons, which start:  Read more

February 23, 2014

Confusion about metadata

A couple of points that arise frequently in conversation, but that I don’t seem to have made clearly online.

“Metadata” is generally defined as “data about data”. That’s basically correct, but it’s easy to forget how many different kinds of metadata there are. My list of metadata kinds starts with:

What’s worse, the past year’s most famous example of “metadata”, telephone call metadata, is misnamed. This so-called metadata, much loved by the NSA (National Security Agency), is just data, e.g. in the format of a CDR (Call Detail Record). Calling it metadata implies that it describes other data — the actual contents of the phone calls — that the NSA strenuously asserts don’t actually exist.

And finally, the first bullet point above has a counter-intuitive consequence — all common terminology notwithstanding, relational data is less structured than document data. Reasons include:

Related links

February 2, 2014

Spark and Databricks

I’ve heard a lot of buzz recently around Spark. So I caught up with Ion Stoica and Mike Franklin for a call. Let me start by acknowledging some sources of confusion.

The “What is Spark?” question may soon be just as difficult as the ever-popular “What is Hadoop?” That said — and referring back to my original technical post about Spark and also to a discussion of prominent Spark user ClearStory — my try at “What is Spark?” goes something like this:

Read more

January 9, 2014

The games of Watson

IBM excels at game technology, most famously in Deep Blue (chess) and Watson (Jeopardy!). But except at the chip level — PowerPC — IBM hasn’t accomplished much at game/real world crossover. And so I suspect the Watson hype is far overblown.

I believe that for two main reasons. First, whenever IBM talks about big initiatives like Watson, it winds up bundling a bunch of dissimilar things together and claiming they’re a seamless whole. Second, some core Watson claims are eerily similar to artificial intelligence (AI) over-hype three or more decades past. For example, the leukemia treatment advisor that is being hopefully built in Watson now sounds a lot like MYCIN from the early 1970s, and the idea of collecting a lot of tidbits of information sounds a lot like the Cyc project. And by the way:

Read more

November 8, 2013

Comments on the 2013 Gartner Magic Quadrant for Operational Database Management Systems

The 2013 Gartner Magic Quadrant for Operational Database Management Systems is out. “Operational” seems to be Gartner’s term for what I call short-request, in each case the point being that OLTP (OnLine Transaction Processing) is a dubious term when systems omit strict consistency, and when even strictly consistent systems may lack full transactional semantics. As is usually the case with Gartner Magic Quadrants:

Anyhow:  Read more

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.

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