If interested in helping, we are seed funded and hiring.

Chris Wensel COMMON MAPREDUCE PATTERNS from ntc GmbH on Vimeo.

The answer isn't black and white but can be broken down into three intertwined heuristics.

Hadoop is more likely justified,

• the larger the corpus of data needed to satisfy the business problem (big-data).
• the more complex the processes and algorithms required to satisfy the business problem.
• and, the more distinct business problems need concurrent or overlapping access to a corpus of data (multi-tenancy).

Thus Hadoop isn't strictly about huge data-sets, but also about absorbing complexity while maintaining scale.

FlightCaster, a Cascading user, doesn't have huge amounts of data, but they do have a very hard business problem, and Hadoop for them is completely justified.

Facebook on the other hand has huge data, but their tool of choice, Hive, doesn't encourage solutions to complex problems by virtue of being a syntax and SQL based. Of which was initially used to extract small data from the cluster for use by other systems or custom Hadoop jobs.

Cascalog is one of their secret weapons, a Clojure-based query language for Hadoop that makes it simple for them to analyze their data in new ways. Inspired by the venerable Datalog, and built on top of Cascading, it allows you to write queries in Clojure and define even complex operations in simple code. Unlike alternatives like Pig or Hive, it's written within a general-purpose language, so there's no need for separate user-defined functions, but it's still a highly-structured way of defining queries.

It's worthy of note that Cascalog is a distant child of cascading-clojure, created and used by FlightCaster. FlightCaster was acquired this week.

There will be discussions on the future of Cascading, the work done by the FlightCaster folk integrating Cascading with Clojure, and various tips and techniques.

Hope to see you there.