EAI, EII, ETL, ELT, ETLT

Analysis of data integration products and technologies, especially ones related to data warehousing, such as ELT (Extract/Transform/Load). Related subjects include:

July 20, 2014

Data integration as a business opportunity

A significant fraction of IT professional services industry revenue comes from data integration. But as a software business, data integration has been more problematic. Informatica, the largest independent data integration software vendor, does $1 billion in revenue. INFA’s enterprise value (market capitalization after adjusting for cash and debt) is $3 billion, which puts it way short of other category leaders such as VMware, and even sits behind Tableau.* When I talk with data integration startups, I ask questions such as “What fraction of Informatica’s revenue are you shooting for?” and, as a follow-up, “Why would that be grounds for excitement?”

*If you believe that Splunk is a data integration company, that changes these observations only a little.

On the other hand, several successful software categories have, at particular points in their history, been focused on data integration. One of the major benefits of 1990s business intelligence was “Combines data from multiple sources on the same screen” and, in some cases, even “Joins data from multiple sources in a single view”. The last few years before application servers were commoditized, data integration was one of their chief benefits. Data warehousing and Hadoop both of course have a “collect all your data in one place” part to their stories — which I call data mustering — and Hadoop is a data transformation tool as well.

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March 23, 2014

DBMS2 revisited

The name of this blog comes from an August, 2005 column. 8 1/2 years later, that analysis holds up pretty well. Indeed, I’d keep the first two precepts exactly as I proposed back then:

I’d also keep the general sense of the third precept, namely appropriately-capable data integration, but for that one the specifics do need some serious rework.

For starters, let me say: Read more

February 9, 2014

Distinctions in SQL/Hadoop integration

Ever more products try to integrate SQL with Hadoop, and discussions of them seem confused, in line with Monash’s First Law of Commercial Semantics. So let’s draw some distinctions, starting with (and these overlap):

In particular:

Let’s go to some examples. Read more

February 2, 2014

Some stuff I’m thinking about (early 2014)

From time to time I like to do “what I’m working on” posts. From my recent blogging, you probably already know that includes:

Other stuff on my mind includes but is not limited to:

1. Certain categories of buying organizations are inherently leading-edge.

Fine. But what really intrigues me is when more ordinary enterprises also put leading-edge technologies into production. I pester everybody for examples of that.

Read more

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:

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December 5, 2013

Vertica 7

It took me a bit of time, and an extra call with Vertica’s long-time R&D chief Shilpa Lawande, but I think I have a decent handle now on Vertica 7, code-named Crane. The two aspects of Vertica 7 I find most interesting are:

Other Vertica 7 enhancements include:

Overall, two recurring themes in our discussion were:

Read more

October 30, 2013

Glassbeam instantiates a lot of trends

Glassbeam checked in recently, and they turn out to exemplify quite a few of the themes I’ve been writing about. For starters:

Glassbeam basics include:

All Glassbeam customers except one are SaaS/cloud (Software as a Service), and even that one was only offered a subscription (as oppose to perpetual license) price.

So what does Glassbeam’s technology do? Glassbeam says it is focused on “machine data analytics,” specifically for the “Internet of Things”, which it distinguishes from IT logs.* Specifically, Glassbeam sells to manufacturers of complex devices — IT (most of its sales so far ), medical, automotive (aspirational to date), etc. — and helps them analyze “phone home” data, for both support/customer service and marketing kinds of use cases. As of a recent release, the Glassbeam stack can: Read more

October 18, 2013

Entity-centric event series analytics

Much of modern analytic technology deals with what might be called an entity-centric sequence of events. For example:

Analytic questions are asked along the lines “Which sequences of events are most productive in terms of leading to the events we really desire?”, such as product sales. Another major area is sessionization, along with data preparation tasks that boil down to arranging data into meaningful event sequences in the first place.

A number of my clients are focused on such scenarios, including WibiData, Teradata Aster (e.g. via nPath), Platfora (in the imminent Platfora 3), and others. And so I get involved in naming exercises. The term entity-centric came along a while ago, because “user-centric” is too limiting. (E.g., the data may not be about a person, but rather specifically about the actions taken on her mobile device.) Now I’m adding the term event series to cover the whole scenario, rather than the “event sequence(s)” I might appear to have been hinting at above.

I decided on “event series” earlier this week, after noting that:  Read more

September 29, 2013

ClearStory, Spark, and Storm

ClearStory Data is:

I think I can do an interesting post about ClearStory while tap-dancing around the still-secret stuff, so let’s dive in.

ClearStory:

To a first approximation, ClearStory ingests data in a system built on Storm (code name: Stormy), dumps it into HDFS, and then operates on it in a system built on Spark (code name: Sparky). Along the way there’s a lot of interaction with another big part of the system, a metadata catalog with no code name I know of. Or as I keep it straight:

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September 20, 2013

Trends in predictive modeling

I talked with Teradata about a bunch of stuff yesterday, including this week’s announcements in in-database predictive modeling. The specific news was about partnerships with Fuzzy Logix and Revolution Analytics. But what I found more interesting was the surrounding discussion. In a nutshell:

This is the strongest statement of perceived demand for in-database modeling I’ve heard. (Compare Point #3 of my July predictive modeling post.) And fits with what I’ve been hearing about R.

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