BIG DATA Training

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Big Data
•The problem space and example applications
•Why don’t traditional approaches scale?
•Requirements

Hadoop Background
•Hadoop History
•The ecosystem and stack: HDFS, Map Reduce, Hive, Pig…
•Cluster architecture overview

Development Environment
•Hadoop distribution and basic commands
•Eclipse development

HDFS Introduction
•The HDFS command line and web interfaces
•The HDFS Java API (lab)

Map Reduce Introduction
•Key philosophy: move computation, not data
•Core concepts: Mappers, reducers, drivers
•The Map Reduce Java API (lab)

Real-World Map Reduce
•Optimizing with Combiners and Practitioners (lab)
•More common algorithms: sorting, indexing and searching (lab)
•Relational manipulation: map-side and reduce-side joins (lab)
•Chaining Jobs
•Testing with MRUnit

Higher-level Tools
•Patterns to abstract “thinking in Map Reduce”
•The Cascading library (lab)
•The Hive database (lab)

 


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