The Magnific Training Big Data Analysis training and Service provides sophisticated analytics and transformation of both structured and unstructured, complex data.
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Hadoop Training Course Content:
1. Understanding Big Data – What is Big Data ?
- Real world issues with BIG Data – Ex: How facebook manage peta bytes of data.
- Will regular traditional approach works?
2. How Hadoop Evolved
- Back to Hadoop evolution.
- The ecosystem and stack: HDFS, MapReduce, Hive, Pig…
- Cluster architecture overview
3. Environment for Hadoop development
- Hadoop distribution and basic commands
- Eclipse development
4. Understanding HDFS
- Command line and web interfaces for HDFS
- Exercises on HDFS Java API
5. Understanding MapReduce
- Core Logic: move computation, not data
- Base concepts: Mappers, reducers, drivers
- The MapReduce Java API (lab)
6. Real-World MapReduce
- Optimizing with Combiners and Partitioners (lab)
- More common algorithms: sorting, indexing and searching (lab)
- Relational manipulation: map-side and reduce-side joins (lab)
- Chaining Jobs
- Testing with MRUnit
7. Higher-level Tools
- Patterns to abstract “thinking in MapReduce”
- The Cascading library (lab)
- The Hive database (lab)
Interested ? Enroll into our online Apache Hadoop training program now.
Visit: www.magnifictraining.com
Hadoop Training Course Content:
1. Understanding Big Data – What is Big Data ?
- Real world issues with BIG Data – Ex: How facebook manage peta bytes of data.
- Will regular traditional approach works?
2. How Hadoop Evolved
- Back to Hadoop evolution.
- The ecosystem and stack: HDFS, MapReduce, Hive, Pig…
- Cluster architecture overview
3. Environment for Hadoop development
- Hadoop distribution and basic commands
- Eclipse development
4. Understanding HDFS
- Command line and web interfaces for HDFS
- Exercises on HDFS Java API
5. Understanding MapReduce
- Core Logic: move computation, not data
- Base concepts: Mappers, reducers, drivers
- The MapReduce Java API (lab)
6. Real-World MapReduce
- Optimizing with Combiners and Partitioners (lab)
- More common algorithms: sorting, indexing and searching (lab)
- Relational manipulation: map-side and reduce-side joins (lab)
- Chaining Jobs
- Testing with MRUnit
7. Higher-level Tools
- Patterns to abstract “thinking in MapReduce”
- The Cascading library (lab)
- The Hive database (lab)
Interested ? Enroll into our online Apache Hadoop training program now.