Data warehousing

Analysis of issues in data warehousing, with extensive coverage of database management systems and data warehouse appliances that are optimized to query large volumes of data. Related subjects include:

September 29, 2013

Visualization or navigation?

I’ve suggested in the past, approximately, that the platform technology side of business intelligence is more significant than the user interface. That formulation, however, doesn’t exactly capture what I believe. To be more precise, let’s differentiate between a couple aspects of business intelligence UI.

It might seem that a lot of the action in business intelligence revolves around ever-better visualization. After all, Tableau is clearly identified as a visualization-centric technology; who’s hotter than Tableau? And numerous other vendors talk of “visualizations” too. But I don’t think that’s exactly right — rather, I see navigation as being a much bigger deal. And unlike most pure visualization, navigation usually depends strongly on underlying platform capabilities.

Examples of what I mean by innovative navigation — all of which have been developed or have gained prominence over the past decade or so — include:

Read more

September 23, 2013

Thoughts on in-memory columnar add-ons

Oracle announced its in-memory columnar option Sunday. As usual, I wasn’t briefed; still, I have some observations. For starters:

I’d also add that Larry Ellison’s pitch “build columns to avoid all that index messiness” sounds like 80% bunk. The physical overhead should be at least as bad, and the main saving in administrative overhead should be that, in effect, you’re indexing ALL columns rather than picking and choosing.

Anyhow, this technology should be viewed as applying to traditional business transaction data, much more than to — for example — web interaction logs, or other machine-generated data. My thoughts around that distinction start:

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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. ;)

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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 14, 2013

The two sides of BI

As is the case for most important categories of technology, discussions of BI can get confused. I’ve remarked in the past that there are numerous kinds of BI, and that the very origin of the term “business intelligence” can’t even be pinned down to the nearest century. But the most fundamental confusion of all is that business intelligence technology really is two different things, which in simplest terms may be categorized as user interface (UI) and platform* technology. And so:

*I wanted to say “server” or “server-side” instead of “platform”, as I dislike the latter word. But it’s too inaccurate, for example in the case of the original Cognos PowerPlay, and also in various thin-client scenarios.

Key aspects of BI platform technology can include:

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August 12, 2013

Things I keep needing to say

Some subjects just keep coming up. And so I keep saying things like:

Most generalizations about “Big Data” are false. “Big Data” is a horrific catch-all term, with many different meanings.

Most generalizations about Hadoop are false. Reasons include:

Hadoop won’t soon replace relational data warehouses, if indeed it ever does. SQL-on-Hadoop is still very immature. And you can’t replace data warehouses unless you have the power of SQL.

Note: SQL isn’t the only way to provide “the power of SQL”, but alternative approaches are just as immature.

Most generalizations about NoSQL are false. Different NoSQL products are … different. It’s not even accurate to say that all NoSQL systems lack SQL interfaces. (For example, SQL-on-Hadoop often includes SQL-on-HBase.)

Read more

August 8, 2013

Curt Monash on video

I made a remarkably rumpled video appearance yesterday with SiliconAngle honchos John Furrier and Dave Vellante. (Excuses include <3 hours sleep, and then a scrambling reaction to a schedule change.) Topics covered included, with approximate timechecks:

Edit: Some of my remarks were transcribed.

Related links

August 6, 2013

Hortonworks, Hadoop, Stinger and Hive

I chatted yesterday with the Hortonworks gang. The main subject was Hortonworks’ approach to SQL-on-Hadoop — commonly called Stinger —  but at my request we cycled through a bunch of other topics as well. Company-specific notes include:

Our deployment and use case discussions were a little confused, because a key part of Hortonworks’ strategy is to support and encourage the idea of combining use cases and workloads on a single cluster. But I did hear:

*By the way — Teradata seems serious about pushing the UDA as a core message.

Ecosystem notes, in Hortonworks’ perception, included:

I also asked specifically about OpenStack. Hortonworks is a member of the OpenStack project, contributes nontrivially to Swift and other subprojects, and sees Rackspace as an important partner. But despite all that, I think strong Hadoop/OpenStack integration is something for the indefinite future.

Hortonworks’ views about Hadoop 2.0 start from the premise that its goal is to support running a multitude of workloads on a single cluster. (See, for example, what I previously posted about Tez and YARN.) Timing notes for Hadoop 2.0 include:

Frankly, I think Cloudera’s earlier and necessarily incremental Hadoop 2 rollout was a better choice than Hortonworks’ later big bang, even though the core-mission aspect of Hadoop 2.0 is what was least ready. HDFS (Hadoop Distributed File System) performance, NameNode failover and so on were well worth having, and it’s more than a year between Cloudera starting supporting them and when Hortonworks is offering Hadoop 2.0.

Hortonworks’ approach to doing SQL-on-Hadoop can be summarized simply as “Make Hive into as good an analytic RDBMS as possible, all in open source”. Key elements include:  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

July 31, 2013

“Disruption” in the software industry

I lampoon the word “disruptive” for being badly overused. On the other hand, I often refer to the concept myself. Perhaps I should clarify. :)

You probably know that the modern concept of disruption comes from Clayton Christensen, specifically in The Innovator’s Dilemma and its sequel, The Innovator’s Solution. The basic ideas are:

In response (this is the Innovator’s Solution part):

But not all cleverness is “disruption”.

Here are some of the examples that make me think of the whole subject. Read more

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