Theory and architecture

Analysis of design choices in databases and database management systems. Related subjects include:

March 5, 2015

Cask and CDAP

For starters:

Also:

So far as I can tell:

Read more

February 28, 2015

Databricks and Spark update

I chatted last night with Ion Stoica, CEO of my client Databricks, for an update both on his company and Spark. Databricks’ actual business is Databricks Cloud, about which I can say:

I do not expect all of the above to remain true as Databricks Cloud matures.

Ion also said that Databricks is over 50 people, and has moved its office from Berkeley to San Francisco. He also offered some Spark numbers, such as: Read more

February 22, 2015

Data models

7-10 years ago, I repeatedly argued the viewpoints:

Since then, however:

So it’s probably best to revisit all that in a somewhat organized way.

Read more

February 12, 2015

MongoDB 3.0

Old joke:

A lot has happened in MongoDB technology over the past year. For starters:

*Newly-released MongoDB 3.0 is what was previously going to be MongoDB 2.8. My clients at MongoDB finally decided to give a “bigger” release a new first-digit version number.

To forestall confusion, let me quickly add: 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

January 10, 2015

Migration

There is much confusion about migration, by which I mean applications or investment being moved from one “platform” technology — hardware, operating system, DBMS, Hadoop, appliance, cluster, cloud, etc. — to another. Let’s sort some of that out. For starters:

I mixed together true migration and new-app platforms in a post last year about DBMS architecture choices, when I wrote: Read more

December 31, 2014

Notes on machine-generated data, year-end 2014

Most IT innovation these days is focused on machine-generated data (sometimes just called “machine data”), rather than human-generated. So as I find myself in the mood for another survey post, I can’t think of any better idea for a unifying theme.

1. There are many kinds of machine-generated data. Important categories include:

That’s far from a complete list, but if you think about those categories you’ll probably capture most of the issues surrounding other kinds of machine-generated data as well.

2. Technology for better information and analysis is also technology for privacy intrusion. Public awareness of privacy issues is focused in a few areas, mainly: 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

October 22, 2014

Snowflake Computing

I talked with the Snowflake Computing guys Friday. For starters:

Much of the Snowflake story can be summarized as cloud/elastic/simple/cheap.*

*Excuse me — inexpensive. Companies rarely like their products to be labeled as “cheap”.

In addition to its purely relational functionality, Snowflake accepts poly-structured data. Notes on that start:

I don’t know enough details to judge whether I’d call that an example of schema-on-need.

A key element of Snowflake’s poly-structured data story seems to be lateral views. I’m not too clear on that concept, but I gather: Read more

September 28, 2014

Some stuff on my mind, September 28, 2014

1. I wish I had some good, practical ideas about how to make a political difference around privacy and surveillance. Nothing else we discuss here is remotely as important. I presumably can contribute an opinion piece to, more or less, the technology publication(s) of my choice; that can have a small bit of impact. But I’d love to do better than that. Ideas, anybody?

2. A few thoughts on cloud, colocation, etc.:

3. As for the analytic DBMS industry: 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.