Log analysis

Discussion of how data warehousing and analytic technologies are applied to logfile analysis. Related subjects include:

July 24, 2012

Notes on Datameer

In a short October, 2011 post about Datameer, I wrote:

Datameer is designed to let you do simple stuff on large amounts of data, where “large amounts of data” typically means data in Hadoop, and “simple stuff” includes basic versions of a spreadsheet, of BI, and of EtL (Extract/Transform/Load, without much in the way of T).

That’s all still mainly true, although with the recent Datameer 2.0:

In essence, Datameer has two positionings.

Read more

June 16, 2012

Introduction to Metamarkets and Druid

I previously dropped a few hints about my clients at Metamarkets, mentioning that they:

But while they’re a joy to talk with, writing about Metamarkets has been frustrating, with many hours and pages of wasted of effort. Even so, I’m trying again, in a three-post series:

Much like Workday, Inc., Metamarkets is a SaaS (Software as a Service) company, with numerous tiers of servers and an affinity for doing things in RAM. That’s where most of the similarities end, however, as  Metamarkets is a much smaller company than Workday, doing very different things.

Metamarkets’ business is SaaS (Software as a Service) business intelligence, on large data sets, with low latency in both senses (fresh data can be queried on, and the queries happen at RAM speed). As you might imagine, Metamarkets is used by digital marketers and other kinds of internet companies, whose data typically wants to be in the cloud anyway. Approximate metrics for Metamarkets (and it may well have exceeded these by now) include 10 customers, 100,000 queries/day, 80 billion 100-byte events/month (before summarization), 20 employees, 1 popular CEO, and a metric ton of venture capital.

To understand how Metamarkets’ technology works, it probably helps to start by realizing: Read more

March 21, 2012

DataStax Enterprise 2.0

Edit: Multiple errors in the post below have been corrected in a follow-on post about DataStax Enterprise and Cassandra.

My client DataStax is announcing DataStax Enterprise 2.0. The big point of the release is that there’s a bunch of stuff integrated together, including at least:

DataStax stresses that all this runs on the same cluster, with the same administrative tools and so on. For example, on a single cluster:

Read more

February 11, 2012

Applications of an analytic kind

The most straightforward approach to the applications business is:

However, this strategy is not as successful in analytics as in the transactional world, for two main reasons:

I first realized all this about a decade ago, after Henry Morris coined the term analytic applications and business intelligence companies thought it was their future. In particular, when Dave Kellogg ran marketing for Business Objects, he rattled off an argument to the effect that Business Objects had generated more analytic app revenue over the lifetime of the company than Cognos had. I retorted, with only mild hyperbole, that the lifetime numbers he was citing amounted to “a bad week for SAP”. Somewhat hoist by his own petard, Dave quickly conceded that he agreed with my skepticism, and we changed the subject accordingly.

Reasons that analytic applications are commonly less complete than the transactional kind include: Read more

February 6, 2012

Sumo Logic and UIs for text-oriented data

I talked with the Sumo Logic folks for an hour Thursday. Highlights included:

What interests me about Sumo Logic is that automated classification story. I thought I heard Sumo Logic say: Read more

January 10, 2012

Splunk update

Splunk is announcing the Splunk 4.3 point release. Before discussing it, let’s recall a few things about Splunk, starting with:

As in any release, a lot of Splunk 4.3 is about “Oh, you didn’t have that before?” features and Bottleneck Whack-A-Mole performance speed-up. One performance enhancement is Bloom filters, which are a very hot topic these days. More important is a switch from Flash to HTML5, so as to accommodate mobile devices with less server-side rendering. Splunk reports that its users — especially the non-IT ones — really want to get Splunk information on the tablet devices. While this somewhat contradicts what I wrote a few days ago pooh-poohing mobile BI, let me hasten to point out:

That’s pretty much the ideal scenario for mobile BI: Timeliness matters and prettiness doesn’t.

Read more

January 8, 2012

Big data terminology and positioning

Recently, I observed that Big Data terminology is seriously broken. It is reasonable to reduce the subject to two quasi-dimensions:

given that

But the conflation should stop there.

*Low-volume/high-velocity problems are commonly referred to as “event processing” and/or “streaming”.

When people claim that bigness and structure are the same issue, they oversimplify into mush. So I think we need four pieces of terminology, reflective of a 2×2 matrix of possibilities. For want of better alternatives, my suggestions are:

Read more

October 19, 2011

What those nested data structures are about

As I’ve noted before, the very big web companies have an issue with nested data structures. The subject came up in XLDB talks yesterday too, so my big goal for lunch was to finally understand what was being talked about. Sitting at a table full of eBay and LinkedIn folks turned out to be a good tactic.

The explanation was led by Oliver Ratzesberger, late of eBay* and progenitor of eBay’s Singularity project. In simplest terms, one event can spawn a lot of event attribute information, perhaps in the form of name-value pairs, which it then makes sense to store together in some way. The example Oliver dwelled on was that, on any given web page, there can be 100+ pieces of information to record, including:

*Edit: Oliver subsequently moved on to Sears and then Teradata.

There are several reasons why one might wish to store this information in ways that grieve relational purists. First, reconstructing all this information via joins would be brutally expensive. What’s more, reconstructing all this information via joins could be impractical. Some comes from third party ad servers, which might not reproduce the same ads upon demand. Other is in the form of rankings, which can’t always be reliably reproduced from one query to the next. (That’s just one of several reasons text search and relational DBMS are an awkward fit.)

Also, there’s a strong dynamic schema flavor to these databases. The list of attributes for one web click might be very different in kind from the list for the next page. Forcing that kind of variability into a fixed relational schema, while theoretically possible, doesn’t necessarily make a lot of sense.

October 10, 2011

Text data management, Part 1: Confusion

This is Part 1 of a three post series. The posts cover:

  1. Confusion about text data management.
  2. Choices for text data management (general and short-request).
  3. Choices for text data management (analytic).

There’s much confusion about the management of text data, among technology users, vendors, and investors alike. Reasons seems to include:

Above all: The use cases for text data vary greatly, just as the use cases for simply-structured databases do.

There are probably fewer people now than there were six years ago who need to be told that text and relational database management are very different things. Other misconceptions, however, appear to be on the rise. Specific points that are commonly overlooked include: Read more

September 12, 2011

Hadoop notes

I visited California recently, and chatted with numerous companies involved in Hadoop — Cloudera, Hortonworks, MapR, DataStax, Datameer, and more. I’ll defer further Hadoop technical discussions for now — my target to restart them is later this month — but that still leaves some other issues to discuss, namely adoption and partnering.

The total number of enterprises in the world paying subscription and license fees that they would regard as being for “Hadoop or something Hadoop-related” probably is not much over 100 right now, but I’d expect to see pretty rapid growth. Beyond that, let’s divide customers into three groups:

Hadoop vendors, in different mixes, claim to be doing well in all three segments. Even so, almost all use cases involve some kind of machine-generated data, with one exception being a credit card vendor crunching a large database of transaction details. Multiple kinds of machine-generated data come into play — web/network/mobile device logs, financial trade data, scientific/experimental data, and more. In particular, pharmaceutical research got some mentions, which makes sense, in that it’s one area of scientific research that actually enjoys fat for-profit research budgets.

Read more

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