Analytic technologies

Discussion of technologies related to information query and analysis. Related subjects include:

November 19, 2015

The questionably named Cloudera Navigator Optimizer

I only have mixed success at getting my clients to reach out to me for messaging advice when they’re introducing something new. Cloudera Navigator Optimizer, which is being announced along with Cloudera 5.5, is one of my failures in that respect; I heard about it for the first time Tuesday afternoon. I hate the name. I hate some of the slides I saw. But I do like one part of the messaging, namely the statement that this is about “refactoring” queries.

All messaging quibbles aside, I think the Cloudera Navigator Optimizer story is actually pretty interesting, and perhaps not just to users of SQL-on-Hadoop technologies such as Hive (which I guess I’d put in that category for simplicity) or Impala. As I understand Cloudera Navigator Optimizer:

Read more

November 19, 2015

CDH 5.5

I talked with Cloudera shortly ahead of today’s announcement of Cloudera 5.5. Much of what we talked about had something or other to do with SQL data management. Highlights include:

While I had Cloudera on the phone, I asked a few questions about Impala adoption, specifically focused on concurrency. There was mention of: Read more

October 26, 2015

Differentiation in business intelligence

Parts of the business intelligence differentiation story resemble the one I just posted for data management. After all:

That said, insofar as BI’s competitive issues resemble those of DBMS, they are those of DBMS-lite. For example:

And full-stack analytic systems — perhaps delivered via SaaS (Software as a Service) — can moot the BI/data management distinction anyway.

Of course, there are major differences between how DBMS and BI are differentiated. The biggest are in user experience. I’d say: Read more

October 26, 2015

Differentiation in data management

In the previous post I broke product differentiation into 6-8 overlapping categories, which may be abbreviated as:

and sometimes also issues in adoption and administration.

Now let’s use this framework to examine two market categories I cover — data management and, in separate post, business intelligence.

Applying this taxonomy to data management:
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 28, 2015

Introduction to Cloudera Kudu

This is part of a three-post series on Kudu, a new data storage system from Cloudera.

Cloudera is introducing a new open source project, Kudu,* which from Cloudera’s standpoint is meant to eventually become the single best underpinning for analytics on the Hadoop stack. I’ve spent multiple hours discussing Kudu with Cloudera, mainly with Todd Lipcon. Any errors are of course entirely mine.

*Like the impala, the kudu is a kind of antelope. I knew that, because I enjoy word games. What I didn’t know — and which is germane to the naming choice — is that the kudu has stripes. :)

For starters:

Read more

September 17, 2015

Rocana’s world

For starters:

Rocana portrays itself as offering next-generation IT operations monitoring software. As you might expect, this has two main use cases:

Rocana’s differentiation claims boil down to fast and accurate anomaly detection on large amounts of log data, including but not limited to:

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

August 24, 2015

Multi-model database managers

I’d say:

Before supporting my claims directly, let me note that this is one of those posts that grew out of a Twitter conversation. The first round went:

Merv Adrian: 2 kinds of multimodel from DBMS vendors: multi-model DBMSs and multimodel portfolios. The latter create more complexity, not less.

Me: “Owned by the same vendor” does not imply “well integrated”. Indeed, not a single example is coming to mind.

Merv: We are clearly in violent agreement on that one.

Around the same time I suggested that Intersystems Cache’ was the last significant object-oriented DBMS, only to get the pushback that they were “multi-model” as well. That led to some reasonable-sounding justification — although the buzzwords of course aren’t from me — namely: Read more

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