February 28, 2017

Coordination, the underused “C” word

I’d like to argue that a single frame can be used to view a lot of the issues that we think about. Specifically, I’m referring to coordination, which I think is a clearer way of characterizing much of what we commonly call communication or collaboration.

It’s easy to argue that computing, to an overwhelming extent, is really about communication. Most obviously:

Indeed, it’s reasonable to claim:

A little less obvious is the much of this communication could be alternatively described as coordination. Some communication has pure consumer value, such as when we talk/email/Facebook/Snapchat/FaceTime with loved ones. But much of the rest is for the purpose of coordinating business or technical processes.

Among the technical categories that boil down to coordination are:

That’s a lot of the value in “platform” IT right there.  Read more

February 2, 2017

There’s no escape from politics now

The United States and consequently much of the world are in political uproar. Much of that is about very general and vital issues such as war, peace or the treatment of women. But quite a lot of it is to some extent tech-industry-specific. The purpose of this post is outline how and why that is.

For example:

Because they involve grave threats to liberty, I see surveillance/privacy as the biggest technology-specific policy issues in the United States. (In other countries, technology-driven censorship might loom larger yet.) My views on privacy and surveillance have long been:

Given the recent election of a US president with strong authoritarian tendencies, that foot-dragging is much more important than it was before.

Other important areas of technology/policy overlap include: Read more

February 2, 2017

Politics and policy in the age of Trump

The United States presidency was recently assumed by an Orwellian lunatic.* Sadly, this is not an exaggeration. The dangers — both of authoritarianism and of general mis-governance — are massive. Everybody needs in some way to respond.

*”Orwellian lunatic” is by no means an oxymoron. Indeed, many of the most successful tyrants in modern history have been delusional; notable examples include Hitler, Stalin, Mao and, more recently, Erdogan. (By way of contrast, I view most other Soviet/Russian leaders and most jumped-up-colonel coup leaders as having been basically sane.)

There are many candidates for what to focus on, including:

But please don’t just go on with your life and leave the politics to others. Those “others” you’d like to rely on haven’t been doing a very good job.

What I’ve chosen to do personally includes: Read more

December 18, 2016

Introduction to Crate.io and CrateDB

Crate.io and CrateDB basics include:

In essence, CrateDB is an open source and less mature alternative to MemSQL. The opportunity for MemSQL and CrateDB alike exists in part because analytic RDBMS vendors didn’t close it off.

CrateDB’s not-just-relational story starts:

Read more

November 23, 2016

DBAs of the future

After a July visit to DataStax, I wrote

The idea that NoSQL does away with DBAs (DataBase Administrators) is common. It also turns out to be wrong. DBAs basically do two things.

  • Handle the database design part of application development. In NoSQL environments, this part of the job is indeed largely refactored away. More precisely, it is integrated into the general app developer/architect role.
  • Manage production databases. This part of the DBA job is, if anything, a bigger deal in the NoSQL world than in more mature and automated relational environments. It’s likely to be called part of “devops” rather than “DBA”, but by whatever name it’s very much a thing.

That turns out to understate the core point, which is that DBAs still matter in non-RDBMS environments. Specifically, it’s too narrow in two ways.

My wake-up call for that latter bit was a recent MongoDB 3.4 briefing. MongoDB certainly has various efforts in administrative tools, which I won’t recapitulate here. But to my surprise, MongoDB also found a role for something resembling relational database design. The idea is simple: A database administrator defines a view against a MongoDB database, where views: Read more

November 23, 2016

MongoDB 3.4 and “multimodel” query

“Multimodel” database management is a hot new concept these days, notwithstanding that it’s been around since at least the 1990s. My clients at MongoDB of course had to join the train as well, but they’ve taken a clear and interesting stance:

When I pointed out that it would make sense to call this “multimodel query” — because the storage isn’t “multimodel” at all — they quickly agreed.

To be clear: While there are multiple ways to read data in MongoDB, there’s still only one way to write it. Letting that sink in helps clear up confusion as to what about MongoDB is or isn’t “multimodel”. To spell that out a bit further: Read more

October 21, 2016

Rapid analytics

“Real-time” technology excites people, and has for decades. Yet the actual, useful technology to meet “real-time” requirements remains immature, especially in cases which call for rapid human decision-making. Here are some notes on that conundrum.

1. I recently posted that “real-time” is getting real. But there are multiple technology challenges involved, including:

2. In early 2011, I coined the phrase investigative analytics, about which I said three main things: Read more

October 10, 2016

Notes on anomaly management

Then felt I like some watcher of the skies
When a new planet swims into his ken

— John Keats, “On First Looking Into Chapman’s Homer”

1. In June I wrote about why anomaly management is hard. Well, not only is it hard to do; it’s hard to talk about as well. One reason, I think, is that it’s hard to define what an anomaly is. And that’s a structural problem, not just a semantic one — if something is well enough understood to be easily described, then how much of an anomaly is it after all?

Artificial intelligence is famously hard to define for similar reasons.

“Anomaly management” and similar terms are not yet in the software marketing mainstream, and may never be. But naming aside, the actual subject matter is important.

2. Anomaly analysis is clearly at the heart of several sectors, including:

Each of those areas features one or both of the frameworks:

So if you want to identify, understand, avert and/or remediate bad stuff, data anomalies are the first place to look.

3. The “insights” promised by many analytics vendors — especially those who sell to marketing departments — are also often heralded by anomalies. Already in the 1970s, Walmart observed that red clothing sold particularly well in Omaha, while orange flew off the shelves in Syracuse. And so, in large college towns, they stocked their stores to the gills with clothing in the colors of the local football team. They also noticed that fancy dresses for little girls sold especially well in Hispanic communities … specifically for girls at the age of First Communion.

Read more

October 3, 2016

Notes on the transition to the cloud

1. The cloud is super-hot. Duh. And so, like any hot buzzword, “cloud” means different things to different marketers. Four of the biggest things that have been called “cloud” are:

Further, there’s always the idea of hybrid cloud, in which a vendor peddles private cloud systems (usually appliances) running similar technology stacks to what they run in their proprietary public clouds. A number of vendors have backed away from such stories, but a few are still pushing it, including Oracle and Microsoft.

This is a good example of Monash’s Laws of Commercial Semantics.

2. Due to economies of scale, only a few companies should operate their own data centers, aka true on-prem(ises). The rest should use some combination of colo, SaaS, and public cloud.

This fact now seems to be widely understood.

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

← Previous PageNext 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.