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:

August 10, 2017

Notes on data security

1. In June I wrote about burgeoning interest in data security. I’d now like to add:

We can reconcile these anecdata pretty well if we postulate that:

2. My current impressions of the legal privacy vs. surveillance tradeoffs are basically: Read more

June 14, 2017

The data security mess

A large fraction of my briefings this year have included a focus on data security. This is the first year in the past 35 that that’s been true.* I believe that reasons for this trend include:

*Not really an exception: I did once make it a project to learn about classic network security, including firewall appliances and so on.

Certain security requirements, desires or features keep coming up. These include (and as in many of my lists, these overlap):

More specific or extreme requirements include:  Read more

June 14, 2017

Light-touch managed services

Cloudera recently introduced Cloudera Altus, a Hadoop-in-the-cloud offering with an interesting processing model:

Thus, you avoid a potential security risk (shipping your data to Cloudera’s service). I’ve tentatively named this strategy light-touch managed services, and am interested in exploring how broadly applicable it might or might not be.

For light-touch to be a good approach, there should be (sufficiently) little downside in performance, reliability and so on from having your service not actually control the data. That assumption is trivially satisfied in the case of Cloudera Altus, because it’s not an ordinary kind of app; rather, its whole function is to improve the job-running part of your stack. Most kinds of apps, however, want to operate on your data directly. For those, it is more challenging to meet acceptable SLAs (Service-Level Agreements) on a light-touch basis.

Let’s back up and consider what “light-touch” for data-interacting apps (i.e., almost all apps) would actually mean. The basics are:  Read more

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 28, 2016

Are analytic RDBMS and data warehouse appliances obsolete?

I used to spend most of my time — blogging and consulting alike — on data warehouse appliances and analytic DBMS. Now I’m barely involved with them. The most obvious reason is that there have been drastic changes in industry structure:

Simply reciting all that, however, begs the question of whether one should still care about analytic RDBMS at all.

My answer, in a nutshell, is:

Analytic RDBMS — whether on premises in software, in the form of data warehouse appliances, or in the cloud – are still great for hard-core business intelligence, where “hard-core” can refer to ad-hoc query complexity, reporting/dashboard concurrency, or both. But they aren’t good for much else.

Read more

August 21, 2016

Introduction to data Artisans and Flink

data Artisans and Flink basics start:

Like many open source projects, Flink seems to have been partly inspired by a Google paper.

To this point, data Artisans and Flink have less maturity and traction than Databricks and Spark. For example:  Read more

July 31, 2016

Notes on Spark and Databricks — generalities

I visited Databricks in early July to chat with Ion Stoica and Reynold Xin. Spark also comes up in a large fraction of the conversations I have. So let’s do some catch-up on Databricks and Spark. In a nutshell:

I shall explain below. I also am posting separately about Spark evolution, especially Spark 2.0. I’ll also talk a bit in that post about Databricks’ proprietary/closed-source technology.

Spark is the replacement for Hadoop MapReduce.

This point is so obvious that I don’t know what to say in its support. The trend is happening, as originally decreed by Cloudera (and me), among others. People are rightly fed up with the limitations of MapReduce, and — niches perhaps aside — there are no serious alternatives other than Spark.

The greatest use for Spark seems to be the same as the canonical first use for MapReduce: data transformation. Also in line with the Spark/MapReduce analogy:  Read more

February 15, 2016

Some checklists for making technical choices

Whenever somebody asks for my help on application technology strategy, I start by trying to ascertain three things. The absolute first is actually a prerequisite to almost any kind of useful conversation, which is to ascertain in general terms what the hell it is that we are talking about. :)

My second goal is to ascertain technology constraints. Three common types are:

That’s often a short and straightforward discussion, except in those awkward situations when all three of my bullet points above are applicable at once.

The third item is usually more interesting. I try to figure out what is to be accomplished. That’s usually not a simple matter, because the initial list of goals and requirements is almost never accurate. It’s actually more common that I have to tell somebody to be more ambitious than that I need to rein them in.

Commonly overlooked needs include:

And if you take one thing away from this post, then take this:

I guarantee it.

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 22, 2016

Cloudera in the cloud(s)

Cloudera released Version 2 of Cloudera Director, which is a companion product to Cloudera Manager focused specifically on the cloud. This led to a discussion about — you guessed it! — Cloudera and the cloud.

Making Cloudera run in the cloud has three major aspects:

Features new in this week’s release of Cloudera Director include:

I.e., we’re talking about some pretty basic/checklist kinds of things. Cloudera Director is evidently working for Amazon AWS and Google GCP, and planned for Windows Azure, VMware and OpenStack.

As for porting, let me start by noting: Read more

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