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Using NoSQL and analyzing big data

Learn how to handle massive amounts of distributed data with schemaless datastores

Date:  07 Oct 2011 (Published 19 May 2011) |Level: Introductory ||

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1. Getting started with NoSQL

NoSQL datastores are moving to the forefront because they solve the problem of scalability on a massive scale. Schemaless datastores are fundamentally different from traditional relational databases, but leveraging them is easier than you might think.

Read: Java development 2.0: NoSQLItem marked not complete - Click to mark complete

2. Hands-on introduction to popular NoSQL datastores

Now that you have the basics of NoSQL down, it's time to explore some of the more popular datastores. Get a hands-on introduction to MongoDB, CouchDB, and Amazon's SimpleDB, as well as Google AppEngine's multiple storage options.

Read: MongoDB: A NoSQL datastore with (all the right) RDBMS movesItem marked not complete - Click to mark complete

Listen: Jim Tommaney on big data and InfiniDBItem marked not complete - Click to mark complete

Listen: Eliot Horowitz on MongoDBItem marked not complete - Click to mark complete

Watch: MongoDB video demoItem marked not complete - Click to mark complete

Read: Cloud storage with Amazon's SimpleDB (two-part article)Item marked not complete - Click to mark complete

Watch: Video demo: An introduction to Amazon SimpleDBItem marked not complete - Click to mark complete

Read: REST up with CouchDB and Groovy's RESTClientItem marked not complete - Click to mark complete

Listen: Aaron Miller and Nitin Borwankar on CouchDB and the CouchOne mobile platformItem marked not complete - Click to mark complete

Read: GAE storage with Bigtable, Blobstore, and Google StorageItem marked not complete - Click to mark complete

3. Analyzing distributed data with MapReduce

A key enabling technology of the big data revolution is MapReduce: a programming model and implementation developed by Google for processing massive-scale, distributed data sets. Explore Apache Hadoop, an open source MapReduce implementation that plays a major role in IBM's approach to big data analysis.

Read: Big data analysis with Hadoop MapReduceItem marked not complete - Click to mark complete

Read: Solve cloud-related big data problems with MapReduceItem marked not complete - Click to mark complete

Read: Crunch your existing data using Apache HadoopItem marked not complete - Click to mark complete

Read: A conversation with Rod Smith, IBM's Mr. Big DataItem marked not complete - Click to mark complete




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Topics:

  • Databases and data management
  • Web 2.0

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About this knowledge path

The RDBMS model is a rock-solid foundation for storing data in traditional client-server architectures, but it doesn't scale to multiple nodes easily or cheaply. In the era of highly scalable web applications like Facebook and Twitter, schemaless datastores—NoSQL—provide a solution. This knowledge path introduces Java developers to NoSQL technology and the role of Apache Hadoop MapReduce in big data analysis.

Activities in this path

  1. Getting started with NoSQL
  2. Hands-on introduction to popular NoSQL datastores
  3. Analyzing distributed data with MapReduce

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