Application areas

Posts focusing on the use of database and analytic technologies in specific application domains. Related subjects include:

September 6, 2016

“Real-time” is getting real

I’ve been an analyst for 35 years, and debates about “real-time” technology have run through my whole career. Some of those debates are by now pretty much settled. In particular:

A big issue that does remain open is: How fresh does data need to be? My preferred summary answer is: As fresh as is needed to support the best decision-making. I think that formulation starts with several advantages:

Straightforward applications of this principle include: Read more

August 21, 2016

More about Databricks and Spark

Databricks CEO Ali Ghodsi checked in because he disagreed with part of my recent post about Databricks. Ali’s take on Databricks’ position in the Spark world includes:

Ali also walked me through customer use cases and adoption in wonderful detail. In general:

The story on those sectors, per Ali, is:  Read more

May 30, 2016

Adversarial analytics and other topics

Five years ago, in a taxonomy of analytic business benefits, I wrote:

A large fraction of all analytic efforts ultimately serve one or more of three purposes:

  • Marketing
  • Problem and anomaly detection and diagnosis
  • Planning and optimization

That continues to be true today. Now let’s add a bit of spin.

1. A large fraction of analytics is adversarial. In particular: Read more

May 18, 2016

Governments vs. tech companies — it’s complicated

Numerous tussles fit the template:

As a general rule, what’s best for any kind of company is — pricing and so on aside — whatever is best or most pleasing for their customers or users. This would suggest that it is in tech companies’ best interest to favor privacy, but there are two important quasi-exceptions: Read more

October 15, 2015

Basho and Riak

Basho was on my (very short) blacklist of companies with whom I refuse to speak, because they have lied about the contents of previous conversations. But Tony Falco et al. are long gone from the company. So when Basho’s new management team reached out, I took the meeting.

For starters:

Basho’s product line has gotten a bit confusing, but as best I understand things the story is:

Technical notes on some of that include:  Read more

October 15, 2015

Couchbase 4.0 and related subjects

I last wrote about Couchbase in November, 2012, around the time of Couchbase 2.0. One of the many new features I mentioned then was secondary indexing. Ravi Mayuram just checked in to tell me about Couchbase 4.0. One of the important new features he mentioned was what I think he said was Couchbase’s “first version” of secondary indexing. Obviously, I’m confused.

Now that you’re duly warned, let me remind you of aspects of Couchbase timeline.

Technical notes on Couchbase 4.0 — and related riffs :) — start: Read more

October 5, 2015

Consumer data management

Don’t plan to fish in your personal data lake.

Perhaps the biggest mess in all of IT is the management of individual consumers’ data. Our electronic data is thoroughly scattered. Most individual portions are poorly managed. There’s no integration. The data that’s on paper is even worse. For example:

For the most part, the technology community is barely trying to solve those problems. But even when it does try, success is mixed at best. For example:

And those are some of the most successful names.

There are numerous reasons for this dismal state of affairs.  Read more

September 17, 2015

Rocana’s world

For starters:

Rocana portrays itself as offering next-generation IT operations monitoring software. As you might expect, this has two main use cases:

Rocana’s differentiation claims boil down to fast and accurate anomaly detection on large amounts of log data, including but not limited to:

Read more

August 3, 2015

Data messes

A lot of what I hear and talk about boils down to “data is a mess”. Below is a very partial list of examples.

To a first approximation, one would expect operational data to be rather clean. After all, it drives and/or records business transactions. So if something goes awry, the result can be lost money, disappointed customers, or worse, and those are outcomes to be strenuously avoided. Up to a point, that’s indeed true, at least at businesses large enough to be properly automated. (Unlike, for example — :) — mine.)

Even so, operational data has some canonical problems. First, it could be inaccurate; somebody can just misspell or otherwise botch an entry. Further, there are multiple ways data can be unreachable, typically because it’s:

Inconsistency can take multiple forms, including:  Read more

June 10, 2015

Hadoop generalities

Occasionally I talk with an astute reporter — there are still a few left :) — and get led toward angles I hadn’t considered before, or at least hadn’t written up. A blog post may then ensue. This is one such post.

There is a group of questions going around that includes:

To a first approximation, my responses are:  Read more

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