Data Processing

MongoDB, Hadoop and humongous data at MongoSV 2012

This presentation given at MongoSV 2012 focuses on data processing when using MongoDB as your primary database including integration with Hadoop & the new MongoDB aggregation framework. 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.

MongoDB, Hadoop and Humongous Data

Learn how to integrate MongoDB with Hadoop for large-scale distributed data processing. Using Hadoop’s MapReduce 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 support for Hadoop streaming support goes beyond the native Java enabling map reduce to be run in languages like Python and Ruby. MongoDB, Hadoop and Humongous Data View more presentations

MongoDB and Hadoop

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.