ALL COVERED TOPICS

NoSQL Benchmarks NoSQL use cases NoSQL Videos NoSQL Hybrid Solutions NoSQL Presentations Big Data Hadoop MapReduce Pig Hive Flume Oozie Sqoop HDFS ZooKeeper Cascading Cascalog BigTable Cassandra HBase Hypertable Couchbase CouchDB MongoDB OrientDB RavenDB Jackrabbit Terrastore Amazon DynamoDB Redis Riak Project Voldemort Tokyo Cabinet Kyoto Cabinet memcached Amazon SimpleDB Datomic MemcacheDB M/DB GT.M Amazon Dynamo Dynomite Mnesia Yahoo! PNUTS/Sherpa Neo4j InfoGrid Sones GraphDB InfiniteGraph AllegroGraph MarkLogic Clustrix CouchDB Case Studies MongoDB Case Studies NoSQL at Adobe NoSQL at Facebook NoSQL at Twitter

NAVIGATE MAIN CATEGORIES

Close

Cascading components for a Big Data applications

Jules S. Damji in a quick intro to Cascading:

At the core of most data-driven applications is a data pipeline through which data flows, originating from Taps and Sources (ingestion) and ending in a Sink (retention) while undergoing transformation along a pipeline (Pipes, Traps, and Flows). And should something fail, a Trap (exception) must handle it. In the big data parlance, these are aspects of ETL operations.

You have to agree that when compared with the MapReduce model, these components could bring a lot of readability to your code. On the other hand, at a first glance Cascading API still feels verbose.

Original title and link: Cascading components for a Big Data applications (NoSQL database©myNoSQL)

via: http://hortonworks.com/blog/cascading-hadoop-big-data-whatever/