I have been interested (but have not really done anything useful yet) in large scale data analysis. Here are some personal interests:
- Analyze the InfoStreams I track from Twitter, Blogs and our own Customized feeds on programming, multi-core and semantic web topics
- Explore Open Linked Data, visualization, connections and analysis
- Applying machine intelligence to understand raw data and notifications of change as well as tracking velocity of change.
This leads to dabbling in the semantic encoding of data (RDF/OWL), visualization techniques (processing), data analysis (R Language) and large scale streaming data (map/reduce, hadoop).
So when I stumbled across Ben Lorica’s Big Data: SSD’s, R and Linked Data Streams I could not resist reading it. A few comments and some links below:
This is how I landed in this strangely named platform called Pig, a sub-project of Apache’s Hadoop. From the wiki:
Pig is a platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs. The salient property of Pig programs is that their structure is amenable to substantial parallelization, which in turns enables them to handle very large data sets.
At the present time, Pig’s infrastructure layer consists of a compiler that produces sequences of Map-Reduce programs, for which large-scale parallel implementations already exist (e.g., the Hadoop subproject). Pig’s language layer currently consists of a textual language called Pig Latin, which has the following key properties:
- Ease of programming. It is trivial to achieve parallel execution of simple, “embarrassingly parallel” data analysis tasks. Complex tasks comprised of multiple interrelated data transformations are explicitly encoded as data flow sequences, making them easy to write, understand, and maintain.
- Optimization opportunities. The way in which tasks are encoded permits the system to optimize their execution automatically, allowing the user to focus on semantics rather than efficiency.
- Extensibility. Users can create their own functions to do special-purpose processing.
Hope to give it a spin and try to see whether I can manage a drink from my InfoStreams firehose.