It could be used for live sentiment analysis, for instance, where an organization estimates the popular sentiment around a product by scanning social networks such as Twitter. It could also be used as a security tool to watch activity logs for unusual activity. It could also be used an alternative to commercial ETL (extract, transform and load) programs, widely used to prepare data for analysis by business intelligence software.
MapReduce's limitation is that it can only analyze data in batch mode, which means all the data must be collected before it can be analyzed. A number of new software programs have been developed to get around the limitation of batch processing, such as Twitter Storm and Apache Spark, which are both available as open source and can run on Hadoop.
Google's own approach to live data analysis uses a number of technologies built by the company, notably Flume and MillWheel. Flume aggregates large amounts of data and MillWheel provides a platform for low-latency data processing.
The service provides a software development kit that can be used to build complex pipelines and analysis. Like MapReduce, Cloud Dataflow will initially use the Java programming language. In the future, other languages may be supported.
The pipelines can ingest data from external sources and use them for a variety of things. The service provides a library to prepare and reformat data for further analysis, and users can write their own transformations.
The treated dataset can be queried against using Google's BigQuery service. Or the user can write modules to examine the data as it crosses the wire, to look for aberrant behavior or trends in real-time.
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