We examine the effectiveness of a statistical technique known as survival analysis to optimize the cache time-to-live for hotel rates in a hotel rate cache. We describe how we collect and prepare nearly a billion records per day utilizing MongoDB and Hadoop. Finally, we show how this analysis is improving the operation of our hotel rate cache.
Learn how to integrate MongoDB with Hadoop for large-scale distributed data processing. Using tools like MapReduce, Pig and Streaming you will learn how to do analytics and ETL on large datasets with the ability to load and save data against MongoDB. With Hadoop MapReduce, Java and Scala programmers will find a native solution for using MapReduce to process their data with MongoDB. Programmers of all kinds will find a new way to work with ETL using Pig to extract and analyze large datasets and persist the results to MongoDB. Python and Ruby Programmers can rejoice as well in a new way to write native Mongo MapReduce using the Hadoop Streaming interfaces.
28th February to 1st March 2012