June 16, 2017

Generally available Kudu

I talked with Cloudera about Kudu in early May. Besides giving me a lot of information about Kudu, Cloudera also helped confirm some trends I’m seeing elsewhere, including:

Now let’s talk about Kudu itself. As I discussed at length in September 2015, Kudu is:

Kudu’s adoption and roll-out story starts: 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

June 14, 2017

Cloudera Altus

I talked with Cloudera before the recent release of Altus. In simplest terms, Cloudera’s cloud strategy aspires to:

In other words, Cloudera is porting its software to an important new platform.* And this port isn’t complete yet, in that Altus is geared only for certain workloads. Specifically, Altus is focused on “data pipelines”, aka data transformation, aka “data processing”, aka new-age ETL (Extract/Transform/Load). (Other kinds of workload are on the roadmap, including several different styles of Impala use.) So what about that is particularly interesting? Well, let’s drill down.

*Or, if you prefer, improving on early versions of the port.

Read more

April 17, 2017

Interana

Interana has an interesting story, in technology and business model alike. For starters:

And to be clear — if we leave aside any questions of marketing-name sizzle, this really is business intelligence. The closest Interana comes to helping with predictive modeling is giving its ad-hoc users inspiration as to where they should focus their modeling attention.

Interana also has an interesting twist in its business model, which I hope can be used successfully by other enterprise software startups as well. Read more

April 13, 2017

Analyzing the right data

0. A huge fraction of what’s important in analytics amounts to making sure that you are analyzing the right data. To a large extent, “the right data” means “the right subset of your data”.

1. In line with that theme:

2. Business intelligence interfaces today don’t look that different from what we had in the 1980s or 1990s. The biggest visible* changes, in my opinion, have been in the realm of better drilldown, ala QlikView and then Tableau. Drilldown, of course, is the main UI for business analysts and end users to subset data themselves.

*I used the word “visible” on purpose. The advances at the back end have been enormous, and much of that redounds to the benefit of BI.

3. I wrote 2 1/2 years ago that sophisticated predictive modeling commonly fit the template:

That continues to be tough work. Attempts to productize shortcuts have not caught fire.

Read more

March 26, 2017

Monitoring

A huge fraction of analytics is about monitoring. People rarely want to frame things in those terms; evidently they think “monitoring” sounds boring or uncool. One cost of that silence is that it’s hard to get good discussions going about how monitoring should be done. But I’m going to try anyway, yet again. :)

Business intelligence is largely about monitoring, and the same was true of predecessor technologies such as green paper reports or even pre-computer techniques. Two of the top uses of reporting technology can be squarely described as monitoring, namely:

Yes, monitoring-oriented BI needs investigative drilldown, or else it can be rather lame. Yes, purely investigative BI is very important too. But monitoring is still the heart of most BI desktop installations.

Predictive modeling is often about monitoring too. It is common to use statistics or machine learning to help you detect and diagnose problems, and many such applications have a strong monitoring element.

I.e., you’re predicting trouble before it happens, when there’s still time to head it off.

As for incident response, in areas such as security — any incident you respond to has to be noticed first Often, it’s noticed through analytic monitoring.

Hopefully, that’s enough of a reminder to establish the great importance of analytics-based monitoring. So how can the practice be improved? At least three ways come to mind, and only one of those three is getting enough current attention.

Read more

March 19, 2017

Cloudera’s Data Science Workbench

0. Matt Brandwein of Cloudera briefed me on the new Cloudera Data Science Workbench. The problem it purports to solve is:

Cloudera’s idea for a third way is:

In theory, that’s pure goodness … assuming that the automagic works sufficiently well. I gather that Cloudera Data Science Workbench has been beta tested by 5 large organizations and many 10s of users. We’ll see what is or isn’t missing as more customers take it for a spin.

Read more

March 12, 2017

Introduction to SequoiaDB and SequoiaCM

For starters, let me say:

Also:

Unfortunately, SequoiaDB has not captured a lot of detailed information about unpaid open source production usage.

Read more

March 1, 2017

One bit of news in Trump’s speech

Donald Trump addressed Congress tonight. As may be seen by the transcript, his speech — while uncharacteristically sober — was largely vacuous.

That said, while Steve Bannon is firmly established as Trump’s puppet master, they don’t agree on quite everything, and one of the documented disagreements had been in their view of skilled, entrepreneurial founder-type immigrants: Bannon opposes them, but Trump has disagreed with his view. And as per the speech, Trump seems to be maintaining his disagreement.

At least, that seems implied by his call for “a merit-based immigration system.”

And by the way — Trump managed to give a whole speech without saying anything overtly racist. Indeed, he specifically decried the murder of an Indian-immigrant engineer. By Trump standards, that counts as a kind of progress.

Edit (March 5): But now there is negative-seeming news about H1-B visas.

Next Page →

Feed: DBMS (database management system), DW (data warehousing), BI (business intelligence), and analytics technology Subscribe to the Monash Research feed via RSS or email:

Login

Search our blogs and white papers

Monash Research blogs

User consulting

Building a short list? Refining your strategic plan? We can help.

Vendor advisory

We tell vendors what's happening -- and, more important, what they should do about it.

Monash Research highlights

Learn about white papers, webcasts, and blog highlights, by RSS or email.