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Course Contents:
1. Big Data
The problem space and example applications
Why don’t traditional approaches scale?
Requirements.
2. Hadoop Background
Hadoop History
The ecosystem and stack: HDFS, MapReduce, Hive, Pig…
Cluster architecture overview.
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3. Development Environment
Hadoop distribution and basic commands
Eclipse development.
4. HDFS Introduction
The HDFS command line and web interfaces
The HDFS Java API.
5. MapReduce Introduction
Key philosophy: move computation, not data
Core concepts: Mappers, reducers, drivers
The MapReduce Java API.
6. Real-World MapReduce
Optimizing with Combiners and Partitioners
More common algorithms: sorting, indexing and searching
Relational manipulation: map-side and reduce-side joins
Chaining Jobs
Testing with MRUnit.
7. Higher-level Tools
Patterns to abstract “thinking in MapReduce”
The Cascading library
The Hive database.
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