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 7, 2015

Zoomdata and the Vs

Let’s start with some terminology biases:

So when my clients at Zoomdata told me that they’re in the business of providing “the fastest visual analytics for big data”, I understood their choice, but rolled my eyes anyway. And then I immediately started to check how their strategy actually plays against the “big data” Vs.

It turns out that:

*The HDFS/S3 aspect seems to be a major part of Zoomdata’s current story.

Core aspects of Zoomdata’s technical strategy include:  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

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:

Read more

March 15, 2015

BI for NoSQL — some very early comments

Over the past couple years, there have been various quick comments and vague press releases about “BI for NoSQL”. I’ve had trouble, however, imagining what it could amount to that was particularly interesting, with my confusion boiling down to “Just what are you aggregating over what?” Recently I raised the subject with a few leading NoSQL companies. The result is that my confusion was expanded. :) Here’s the small amount that I have actually figured out.

As I noted in a recent post about data models, many databases — in particular SQL and NoSQL ones — can be viewed as collections of <name, value> pairs.

Consequently, a NoSQL database can often be viewed as a table or a collection of tables, except that:

That’s all straightforward to deal with if you’re willing to write scripts to extract the NoSQL data and transform or aggregate it as needed. But things get tricky when you try to insist on some kind of point-and-click. And by the way, that last comment pertains to BI and ETL (Extract/Transform/Load) alike. Indeed, multiple people I talked with on this subject conflated BI and ETL, and they were probably right to do so.

Read more

March 5, 2015

Cask and CDAP

For starters:

Also:

So far as I can tell:

Read more

February 28, 2015

Databricks and Spark update

I chatted last night with Ion Stoica, CEO of my client Databricks, for an update both on his company and Spark. Databricks’ actual business is Databricks Cloud, about which I can say:

I do not expect all of the above to remain true as Databricks Cloud matures.

Ion also said that Databricks is over 50 people, and has moved its office from Berkeley to San Francisco. He also offered some Spark numbers, such as: Read more

February 18, 2015

Greenplum is being open sourced

While I don’t find the Open Data Platform thing very significant, an associated piece of news seems cooler — Pivotal is open sourcing a bunch of software, with Greenplum as the crown jewel. Notes on that start:

Greenplum, let us recall, is a pretty decent MPP (Massively Parallel Processing) analytic RDBMS. Various aspects of it were oversold at various times, and I’ve never heard that they actually licked concurrency. But Greenplum has long had good SQL coverage and petabyte-scale deployments and a columnar option and some in-database analytics and so on; i.e., it’s legit. When somebody asks me about open source analytic RDBMS to consider, I expect Greenplum to consistently be on the short list.

Further, the low-cost alternatives for analytic RDBMS are adding up. Read more

January 19, 2015

Where the innovation is

I hoped to write a reasonable overview of current- to medium-term future IT innovation. Yeah, right. :) But if we abandon any hope that this post could be comprehensive, I can at least say:

1. Back in 2011, I ranted against the term Big Data, but expressed more fondness for the V words — Volume, Velocity, Variety and Variability. That said, when it comes to data management and movement, solutions to the V problems have generally been sketched out.

2. Even so, there’s much room for innovation around data movement and management. I’d start with:

3. As I suggested last year, data transformation is an important area for innovation.  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 28, 2014

Some stuff on my mind, September 28, 2014

1. I wish I had some good, practical ideas about how to make a political difference around privacy and surveillance. Nothing else we discuss here is remotely as important. I presumably can contribute an opinion piece to, more or less, the technology publication(s) of my choice; that can have a small bit of impact. But I’d love to do better than that. Ideas, anybody?

2. A few thoughts on cloud, colocation, etc.:

3. As for the analytic DBMS industry: Read more

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