Analysis of data management technology optimized for text data. Related subjects include:

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 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

December 1, 2015

What is AI, and who has it?

This is part of a four post series spanning two blogs.

1. “Artificial intelligence” is a term that usually means one or more of:

But that covers a lot of ground, especially since reasonable people might disagree as to what constitutes “smart”.

2. Examples of what has been called “AI” include:

Read more

October 26, 2015

Sources of differentiation

Obviously, a large fraction of what I write about involves technical differentiation. So let’s try for a framework where differentiation claims can be placed in context. This post will get through the generalities. The sequels will apply them to specific cases.

Many buying and design considerations for IT fall into six interrelated areas:  Read more

September 14, 2015

DataStax and Cassandra update

MongoDB isn’t the only company I reached out to recently for an update. Another is DataStax. I chatted mainly with Patrick McFadin, somebody with whom I’ve had strong consulting relationships at a user and vendor both. But Rachel Pedreschi contributed the marvelous phrase “twinkling dashboard”.

It seems fair to say that in most cases:

Those generalities, in my opinion, make good technical sense. Even so, there are some edge cases or counterexamples, such as:

*And so a gas company is doing lightweight analysis on boiler temperatures, which it regards as hot data. :)

While most of the specifics are different, I’d say similar things about MongoDB, Cassandra, or any other NoSQL DBMS that comes to mind: Read more

September 10, 2015

MongoDB update

One pleasure in talking with my clients at MongoDB is that few things are NDA. So let’s start with some numbers:

Also >530 staff, and I think that number is a little out of date.

MongoDB lacks many capabilities RDBMS users take for granted. MongoDB 3.2, which I gather is slated for early November, narrows that gap, but only by a little. Features include:

There’s also a closed-source database introspection tool coming, currently codenamed MongoDB Scout.  Read more

May 26, 2015

IT-centric notes on the future of health care

It’s difficult to project the rate of IT change in health care, because:

Timing aside, it is clear that health care change will be drastic. The IT part of that starts with vastly comprehensive electronic health records, which will be accessible (in part or whole as the case may be) by patients, care givers, care payers and researchers alike. I expect elements of such records to include:

These vastly greater amounts of data cited above will allow for greatly changed analytics.
Read more

May 2, 2015

Notes, links and comments, May 2, 2015

I’m going to be out-of-sorts this week, due to a colonoscopy. (Between the prep, the procedure, and the recovery, that’s a multi-day disablement.) In the interim, here’s a collection of links, quick comments and the like.

1. Are you an engineer considering a start-up? This post is for you. It’s based on my long experience in and around such scenarios, and includes a section on “Deadly yet common mistakes”.

2. There seems to be a lot of confusion regarding the business model at my clients Databricks. Indeed, my own understanding of Databricks’ on-premises business has changed recently. There are no changes in my beliefs that:

However, I now get the impression that revenue from such relationships is a bigger deal to Databricks than I previously thought.

Databricks, by the way, has grown to >50 people.

3. DJ Patil and Ruslan Belkin apparently had a great session on lessons learned, covering a lot of ground. Many of the points are worth reading, but one in particular echoed something I’m hearing lots of places — “Data is super messy, and data cleanup will always be literally 80% of the work.” Actually, I’d replace the “always” by something like “very often”, and even that mainly for newish warehouses, data marts or datasets. But directionally the comment makes a whole lot of sense.

Read more

April 16, 2015

Notes on indexes and index-like structures

Indexes are central to database management.

Perhaps it’s time for a round-up post on indexing. :)

1. First, let’s review some basics. Classically:

2. Further:  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

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