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Big data Analytics online training
Magnific training
Call : 9052666559
www.hadooponlinetraining.net
• 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
• 3. Development Environment
• Hadoop distribution and basic commands
• Eclipse development
• 4. HDFS Introduction
• The HDFS command line and web interfaces
• The HDFS Java API (lab)
• 5. MapReduce Introduction
• Key philosophy: move computation, not data
• Core 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)

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Big data analytics online training

  • 1. Big data Analytics online training Magnific training Call : 9052666559 www.hadooponlinetraining.net
  • 2. • Course Contents: • 1. Big Data • The problem space and example applications • Why don’t traditional approaches scale? • Requirements
  • 3. • 2. Hadoop Background • Hadoop History • The ecosystem and stack: HDFS, MapReduce, Hive, Pig… • Cluster architecture overview
  • 4. • 3. Development Environment • Hadoop distribution and basic commands • Eclipse development • 4. HDFS Introduction • The HDFS command line and web interfaces • The HDFS Java API (lab)
  • 5. • 5. MapReduce Introduction • Key philosophy: move computation, not data • Core concepts: Mappers, reducers, drivers • The MapReduce Java API (lab)
  • 6. • 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. • 7. Higher-level Tools • Patterns to abstract “thinking in MapReduce” • The Cascading library (lab) • The Hive database (lab)