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

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:

Also:

Read more

March 5, 2015

Cask and CDAP

For starters:

Also:

So far as I can tell:

Read more

January 30, 2015

Growth in machine-generated data

In one of my favorite posts, namely When I am a VC Overlord, I wrote:

I will not fund any entrepreneur who mentions “market projections” in other than ironic terms. Nobody who talks of market projections with a straight face should be trusted.

Even so, I got talked today into putting on the record a prediction that machine-generated data will grow at more than 40% for a while.

My reasons for this opinion are little more than:

I was referring to the creation of such data, but the growth rates of new creation and of persistent storage are likely, at least at this back-of-the-envelope level, to be similar.

Anecdotal evidence actually suggests 50-60%+ growth rates, so >40% seemed like a responsible claim.

Related links

December 10, 2014

A few numbers from MapR

MapR put out a press release aggregating some customer information; unfortunately, the release is a monument to vagueness. Let me start by saying:

Anyhow, the key statement in the MapR release is:

… the number of companies that have a paid subscription for MapR now exceeds 700.

Unfortunately, that includes OEM customers as well as direct ones; I imagine MapR’s direct customer count is much lower.

In one gesture to numerical conservatism, MapR did indicate by email that it counts by overall customer organization, not by department/cluster/contract (i.e., not the way Hortonworks does). 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

October 26, 2014

Datameer at the time of Datameer 5.0

Datameer checked in, having recently announced general availability of Datameer 5.0. So far as I understood, Datameer is still clearly in the investigative analytics business, in that:

Key aspects include:

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

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