Microsoft and SQL*Server

Microsoft’s efforts in the database management, analytics, and data connectivity markets. Related subjects include:

May 18, 2016

Governments vs. tech companies — it’s complicated

Numerous tussles fit the template:

As a general rule, what’s best for any kind of company is — pricing and so on aside — whatever is best or most pleasing for their customers or users. This would suggest that it is in tech companies’ best interest to favor privacy, but there are two important quasi-exceptions: Read more

January 25, 2016

Kafka and Confluent

For starters:

At its core Kafka is very simple:

So it seems fair to say:

Read more

January 22, 2016

Cloudera in the cloud(s)

Cloudera released Version 2 of Cloudera Director, which is a companion product to Cloudera Manager focused specifically on the cloud. This led to a discussion about — you guessed it! — Cloudera and the cloud.

Making Cloudera run in the cloud has three major aspects:

Features new in this week’s release of Cloudera Director include:

I.e., we’re talking about some pretty basic/checklist kinds of things. Cloudera Director is evidently working for Amazon AWS and Google GCP, and planned for Windows Azure, VMware and OpenStack.

As for porting, let me start by noting: Read more

December 31, 2015

Oracle as the new IBM — has a long decline started?

When I find myself making the same observation fairly frequently, that’s a good impetus to write a post based on it. And so this post is based on the thought that there are many analogies between:

And when you look at things that way, Oracle seems to be swimming against the tide.

Drilling down, there are basically three things that can seriously threaten Oracle’s market position:

Oracle’s decline, if any, will be slow — but I think it has begun.

 

Oracle/IBM analogies

There’s a clear market lead in the core product category. IBM was dominant in mainframe computing. While not as dominant, Oracle is definitely a strong leader in high-end OTLP/mixed-use (OnLine Transaction Processing) RDBMS.

That market lead is even greater than it looks, because some of the strongest competitors deserve asterisks. Many of IBM’s mainframe competitors were “national champions” — Fujitsu and Hitachi in Japan, Bull in France and so on. Those were probably stronger competitors to IBM than the classic BUNCH companies (Burroughs, Univac, NCR, Control Data, Honeywell).

Similarly, Oracle’s strongest direct competitors are IBM DB2 and Microsoft SQL Server, each of which is sold primarily to customers loyal to the respective vendors’ full stacks. SAP is now trying to play a similar game.

The core product is stable, secure, richly featured, and generally very mature. Duh.

The core product is complicated to administer — which provides great job security for administrators. IBM had JCL (Job Control Language). Oracle has a whole lot of manual work overseeing indexes. In each case, there are many further examples of the point. Edit: A Twitter discussion suggests the specific issue with indexes has been long fixed.

Niche products can actually be more reliable than the big, super-complicated leader. Tandem Nonstop computers were super-reliable. Simple, “embeddable” RDBMS — e.g. Progress or SQL Anywhere — in many cases just work. Still, if you want one system to run most of your workload 24×7, it’s natural to choose the category leader. Read more

December 7, 2015

Transitioning to the cloud(s)

There’s a lot of talk these days about transitioning to the cloud, by IT customers and vendors alike. Of course, I have thoughts on the subject, some of which are below.

1. The economies of scale of not running your own data centers are real. That’s the kind of non-core activity almost all enterprises should outsource. Of course, those considerations taken alone argue equally for true cloud, co-location or SaaS (Software as a Service).

2. When the (Amazon) cloud was newer, I used to hear that certain kinds of workloads didn’t map well to the architecture Amazon had chosen. In particular, shared-nothing analytic query processing was necessarily inefficient. But I’m not hearing nearly as much about that any more.

3. Notwithstanding the foregoing, not everybody loves Amazon pricing.

4. Infrastructure vendors such as Oracle would like to also offer their infrastructure to you in the cloud. As per the above, that could work. However:

Actually, if we replace “Oracle” by “Microsoft”, the whole idea sounds better. While Microsoft doesn’t have a proprietary server hardware story like Oracle’s, many folks are content in the Microsoft walled garden. IBM has fiercely loyal customers as well, and so may a couple of Japanese computer manufacturers.

5. Even when running stuff in the cloud is otherwise a bad idea, there’s still: Read more

December 1, 2015

Machine learning’s connection to (the rest of) AI

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

1. I think the technical essence of AI is usually:

Of course, a lot of non-AI software can be described the same way.

To check my claim, please consider:

To see why it’s true from a bottom-up standpoint, please consider the next two points.

2. It is my opinion that most things called “intelligence” — natural and artificial alike — have a great deal to do with pattern recognition and response. Examples of what I mean include:  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

December 7, 2014

Notes on the Hortonworks IPO S-1 filing

Given my stock research experience, perhaps I should post about Hortonworks’ initial public offering S-1 filing. :) For starters, let me say:

And, perhaps of interest only to me — there are approximately 50 references to YARN in the Hortonworks S-1, but only 1 mention of Tez.

Read more

November 30, 2014

Thoughts and notes, Thanksgiving weekend 2014

I’m taking a few weeks defocused from work, as a kind of grandpaternity leave. That said, the venue for my Dances of Infant Calming is a small-but-nice apartment in San Francisco, so a certain amount of thinking about tech industries is inevitable. I even found time last Tuesday to meet or speak with my clients at WibiData, MemSQL, Cloudera, Citus Data, and MongoDB. And thus:

1. I’ve been sloppy in my terminology around “geo-distribution”, in that I don’t always make it easy to distinguish between:

The latter case can be subdivided further depending on whether multiple copies of the data can accept first writes (aka active-active, multi-master, or multi-active), or whether there’s a clear single master for each part of the database.

What made me think of this was a phone call with MongoDB in which I learned that the limit on number of replicas had been raised from 12 to 50, to support the full-replication/latency-reduction use case.

2. Three years ago I posted about agile (predictive) analytics. One of the points was:

… if you change your offers, prices, ad placement, ad text, ad appearance, call center scripts, or anything else, you immediately gain new information that isn’t well-reflected in your previous models.

Subsequently I’ve been hearing more about predictive experimentation such as bandit testing. WibiData, whose views are influenced by a couple of Very Famous Department Store clients (one of which is Macy’s), thinks experimentation is quite important. And it could be argued that experimentation is one of the simplest and most direct ways to increase the value of your data.

3. I’d further say that a number of developments, trends or possibilities I’m seeing are or could be connected. These include agile and experimental predictive analytics in general, as noted in the previous point, along with:  Read more

September 7, 2014

An idealized log management and analysis system — from whom?

I’ve talked with many companies recently that believe they are:

At best, I think such competitive claims are overwrought. Still, it’s a genuinely important subject and opportunity, so let’s consider what a great log management and analysis system might look like.

Much of this discussion could apply to machine-generated data in general. But right now I think more players are doing product management with an explicit conception either of log management or event-series analytics, so for this post I’ll share that focus too.

A short answer might be “Splunk, but with more analytic functionality and more scalable performance, at lower cost, plus numerous coupons for free pizza.” A more constructive and bottoms-up approach might start with:  Read more

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