Bigdata and Hadoop
Big data is a collection of large datasets that cannot be processed using traditional computing techniques. It is not a single technique or a tool, rather it has become a complete subject, which involves various tools, technqiues and frameworks.
Price : 18000/-
Big Data :
- The problem space and example applications
- Why don't traditional approaches scale?
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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