Big Data

Big Data :

1. The problem space and example applications

2. Why don’t traditional approaches scale?

3. Requirements

Hadoop Background :

1. Hadoop History

2. The ecosystem and stack: HDFS, Map Reduce, Hive, Pig…

3. Cluster architecture overview

Development Environment :

1. Hadoop distribution and basic commands

2. Eclipse development

HDFS Introduction :

1. The HDFS command line and web interfaces

2. The HDFS Java API (lab)

Map Reduce Introduction :

1. Key philosophy: move computation, not data

2. Core concepts: Mappers, reducers, drivers

3. The Map Reduce Java API (lab)

Real-World Map Reduce :

1. Optimizing with Combiners and Practitioners (lab)

2. More common algorithms: sorting, indexing and searching (lab)

3. Relational manipulation: map-side and reduce-side joins (lab)

4. Chaining Jobs

5. Testing with MRUnit

Higher-level Tools :

1. Patterns to abstract “thinking in Map Reduce”

2. The Cascading library (lab)

3. The Hive database (lab)

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