Discussion of Hadoop. Related subjects include:

Open source database management systems

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.
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May 20, 2015

MemSQL 4.0

I talked with my clients at MemSQL about the release of MemSQL 4.0. Let’s start with the reminders:

The main new aspects of MemSQL 4.0 are:

There’s also a new free MemSQL “Community Edition”. MemSQL hopes you’ll experiment with this but not use it in production. And MemSQL pricing is now wholly based on RAM usage, so the column store is quasi-free from a licensing standpoint is as well.

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May 13, 2015

Notes on analytic technology, May 13, 2015

1. There are multiple ways in which analytics is inherently modular. For example:

Also, analytics is inherently iterative.

If I’m right that analytics is or at least should be modular and iterative, it’s easy to see why people hate multi-year data warehouse creation projects. Perhaps it’s also easy to see why I like the idea of schema-on-need.

2. In 2011, I wrote, in the context of agile predictive analytics, that

… the “business analyst” role should be expanded beyond BI and planning to include lightweight predictive analytics as well.

I gather that a similar point is at the heart of Gartner’s new term citizen data scientist. I am told that the term resonates with at least some enterprises.  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.

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April 10, 2015

MariaDB and MaxScale

I chatted with the MariaDB folks on Tuesday. Let me start by noting:

The numbers around MariaDB are a little vague. I was given the figure that there were ~500 customers total, but I couldn’t figure out what they were customers for. Remote DBA services? MariaDB support subscriptions? Something else? I presume there are some customers in each category, but I don’t know the mix. Other notes on MariaDB the company are:

MariaDB, the company, also has an OEM business. Part of their pitch is licensing for connectors — specifically LGPL — that hopefully gets around some of the legal headaches for MySQL engine suppliers.

MaxScale is a proxy, which starts out by intercepting and parsing MariaDB queries. Read more

April 9, 2015

Which analytic technology problems are important to solve for whom?

I hear much discussion of shortfalls in analytic technology, especially from companies that want to fill in the gaps. But how much do these gaps actually matter? In many cases, that depends on what the analytic technology is being used for. So let’s think about some different kinds of analytic task, and where they each might most stress today’s available technology.

In separating out the task areas, I’ll focus first on the spectrum “To what extent is this supposed to produce novel insights?” and second on the dimension “To what extent is this supposed to be integrated into a production/operational system?” Issues of latency, algorithmic novelty, etc. can follow after those. In particular, let’s consider the tasks: 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:

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March 17, 2015

More notes on HBase

1. Continuing from last week’s HBase post, the Cloudera folks were fairly proud of HBase’s features for performance and scalability. Indeed, they suggested that use cases which were a good technical match for HBase were those that required fast random reads and writes with high concurrency and strict consistency. Some of the HBase architecture for query performance seems to be:

Notwithstanding that a couple of those features sound like they might help with analytic queries, the base expectation is that you’ll periodically massage your HBase data into a more analytically-oriented form. For example — I was talking with Cloudera after all — you could put it into Parquet.

2. The discussion of which kinds of data are originally put into HBase was a bit confusing.

OpenTSDB, by the way, likes to store detailed data and aggregates side-by-side, which resembles a pattern I discussed in my recent BI for NoSQL post.

3. HBase supports caching, tiered storage, and so on. Cloudera is pretty sure that it is publicly known (I presume from blog posts or conference talks) that:  Read more

March 10, 2015

Notes on HBase

I talked with a couple of Cloudera folks about HBase last week. Let me frame things by saying:


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March 5, 2015

Cask and CDAP

For starters:


So far as I can tell:

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