• Preface
• Big Data Basics
• Cloud Computing
• NoSQL
• Multi-tenancy
• Database-as-a-Service
• Scalability Options
• CAP Theorem
• Parallel Databases
• Big Data Storage
• Google BigTable/HBase
• Amazon Dynamo
• Amazon SimpleDB
• Amazon S3
• Windows Azure Table and Blob Storages
• Cassandra
• HyperTable
• MongoDB
• CouchDB
• Voldermot
• Database-as-a-Service
• Amazon RDS
• Microsoft SQL Azure
• Google Data Store
• Google Cloud SQL
• Heroku Postgress
• Salesforce
• Big Data Batch Processing
• MapReduce/Hadoop
• HaLoop
• iMapReduce
• Twister
• Pig
• Hive
• Dryad/DryadLinq
• Jaql
• HadoopDB
• Spark
• Stratosphere
• SpatialHadoop
• Dremel/BigQuery
• Amazon Redshift
• Apache Mahout
• Hadoop Online
• Twitter Storm
• Pregel
• GraphLab
• Pegasus
• Trinity
• Echo System
• Apache ZooKeeper
• Apache Sqoop
• Apache Flume
• Apache Oozie
• Apache Thrift
• Cascading
• Google Fusion Tables
• InfoSphere BigInsights

Cascading

The Cascading project is a Java application framework that enables typical developers to quickly and easily develop rich Data Analytics and Data Management applications that can be deployed and managed across a variety of computing environments. Cascading works seamlessly with Apache Hadoop 1.0 and API compatible distributions. Cascading provides a rich Java API for defining complex data flows and creating sophisticated data oriented frameworks. These frameworks can be Maven compatible libraries, or Domain Specic Languages (DSLs) for scripting. Cascading allows developers to create and test rich functionality before tackling complex integration problems. Thus integration points can be developed and tested before plugging them into a production data flow.

Because Cascading is Java based, it naturally fits into all of the JVM based languages available. Notably Scala, Clojure, Jruby, Jython, and Groovy. Within many of these languages, scripting and query languages have been created by the Cascading community to simplify adhoc and production ready analytics and machine learning applications.