Streaming and complex event processing (CEP)

Discussion of complex event processing (CEP), aka event processing or stream processing – i.e., of technology that executes queries before data is ever stored on disk. Related subjects include:

Coral8
StreamBase
Truviso
Progress Apama

July 19, 2016

Notes from a long trip, July 19, 2016

For starters:

A running list of recent posts is:

Subjects I’d like to add to that list include:

Read more

January 25, 2016

Kafka and more

In a companion introduction to Kafka post, I observed that Kafka at its core is remarkably simple. Confluent offers a marchitecture diagram that illustrates what else is on offer, about which I’ll note:

Kafka offers little in the way of analytic data transformation and the like. Hence, it’s commonly used with companion products.  Read more

January 25, 2016

Kafka and Confluent

For starters:

At its core Kafka is very simple:

So it seems fair to say:

Read more

August 24, 2015

Multi-model database managers

I’d say:

Before supporting my claims directly, let me note that this is one of those posts that grew out of a Twitter conversation. The first round went:

Merv Adrian: 2 kinds of multimodel from DBMS vendors: multi-model DBMSs and multimodel portfolios. The latter create more complexity, not less.

Me: “Owned by the same vendor” does not imply “well integrated”. Indeed, not a single example is coming to mind.

Merv: We are clearly in violent agreement on that one.

Around the same time I suggested that Intersystems Cache’ was the last significant object-oriented DBMS, only to get the pushback that they were “multi-model” as well. That led to some reasonable-sounding justification — although the buzzwords of course aren’t from me — namely: 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

May 26, 2015

IT-centric notes on the future of health care

It’s difficult to project the rate of IT change in health care, because:

Timing aside, it is clear that health care change will be drastic. The IT part of that starts with vastly comprehensive electronic health records, which will be accessible (in part or whole as the case may be) by patients, care givers, care payers and researchers alike. I expect elements of such records to include:

These vastly greater amounts of data cited above will allow for greatly changed analytics.
Read more

March 23, 2015

A new logical data layer?

I’m skeptical of data federation. I’m skeptical of all-things-to-all-people claims about logical data layers, and in particular of Gartner’s years-premature “Logical Data Warehouse” buzzphrase. Still, a reasonable number of my clients are stealthily trying to do some kind of data layer middleware, as are other vendors more openly, and I don’t think they’re all crazy.

Here are some thoughts as to why, and also as to challenges that need to be overcome.

There are many things a logical data layer might be trying to facilitate — writing, querying, batch data integration, real-time data integration and more. That said:

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March 5, 2015

Cask and CDAP

For starters:

Also:

So far as I can tell:

Read more

October 5, 2014

Streaming for Hadoop

The genesis of this post is that:

Of course, we should hardly assume that what the Hadoop distro vendors favor will be the be-all and end-all of streaming. But they are likely to at least be influential players in the area.

In the parts of the problem that Cloudera emphasizes, the main tasks that need to be addressed are: Read more

September 7, 2014

An idealized log management and analysis system — from whom?

I’ve talked with many companies recently that believe they are:

At best, I think such competitive claims are overwrought. Still, it’s a genuinely important subject and opportunity, so let’s consider what a great log management and analysis system might look like.

Much of this discussion could apply to machine-generated data in general. But right now I think more players are doing product management with an explicit conception either of log management or event-series analytics, so for this post I’ll share that focus too.

A short answer might be “Splunk, but with more analytic functionality and more scalable performance, at lower cost, plus numerous coupons for free pizza.” A more constructive and bottoms-up approach might start with:  Read more

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