July 7, 2015

Zoomdata and the Vs

Let’s start with some terminology biases:

So when my clients at Zoomdata told me that they’re in the business of providing “the fastest visual analytics for big data”, I understood their choice, but rolled my eyes anyway. And then I immediately started to check how their strategy actually plays against the “big data” Vs.

It turns out that:

*The HDFS/S3 aspect seems to be a major part of Zoomdata’s current story.

Core aspects of Zoomdata’s technical strategy include:  Read more

June 8, 2015

Teradata will support Presto

At the highest level:

Now let’s make that all a little more precise.

Regarding Presto (and I got most of this from Teradata)::

Daniel Abadi said that Presto satisfies what he sees as some core architectural requirements for a modern parallel analytic RDBMS project:  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

February 18, 2015

Hadoop: And then there were three

Hortonworks, IBM, EMC Pivotal and others have announced a project called “Open Data Platform” to do … well, I’m not exactly sure what. Mainly, it sounds like:

Edit: Now there’s a press report saying explicitly that Hortonworks is taking over Pivotal’s Hadoop distro customers (which basically would mean taking over the support contracts and then working to migrate them to Hortonworks’ distro).

The claim is being made that this announcement solves some kind of problem about developing to multiple versions of the Hadoop platform, but to my knowledge that’s a problem rarely encountered in real life. When you already have a multi-enterprise open source community agreeing on APIs (Application Programming interfaces), what API inconsistency remains for a vendor consortium to painstakingly resolve?

Anyhow, it now seems clear that if you want to use a Hadoop distribution, there are three main choices:

In saying that, I’m glossing over a few points, such as: 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 16, 2014

Cloudera’s announcements this week

This week being Hadoop World, Cloudera naturally put out a flurry of press releases. In anticipation, I put out a context-setting post last weekend. That said, the gist of the news seems to be:

Notes on Cloudera Director start:

What I have not heard is any answer for the traditional performance challenge of Hadoop-in-the-cloud, which is:

Maybe that problem isn’t — or is no longer — as big a deal as I’ve been told.

October 13, 2014

Context for Cloudera

Hadoop World/Strata is this week, so of course my clients at Cloudera will have a bunch of announcements. Without front-running those, I think it might be interesting to review the current state of the Cloudera product line. Details may be found on the Cloudera product comparison page. Examining those details helps, I think, with understanding where Cloudera does and doesn’t place sales and marketing focus, which given Cloudera’s Hadoop market stature is in my opinion an interesting thing to analyze.

So far as I can tell (and there may be some errors in this, as Cloudera is not always accurate in explaining the fine details):

In analyzing all this, I’m focused on two particular aspects:

Read more

October 5, 2014

Streaming for Hadoop

The genesis of this post is that:

Of course, we should hardly assume that what the Hadoop distro vendors favor will be the be-all and end-all of streaming. But they are likely to at least be influential players in the area.

In the parts of the problem that Cloudera emphasizes, the main tasks that need to be addressed are: Read more

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