Hadoop course curriculm

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Hadoop course curriculm

  1. 1.  Introduction to Distributed Programming › Background of Hadoop › What is Hadoop ? › How Hadoop works ?  Installing Hadoop › Setting up SSH › Setting up Environment Variables › Running Hadoop › Web-Based Cluster
  2. 2.  Components of Hadoop › Working with Hadoop File-System › Understanding Hadoop Map-Reduce › Reading and Writing  Writing Basic Map Reduce Program › Getting the Patent Data Set › Constructing Basic Map-Reduce Program › Working with Hadoop Streaming › Improving Performance with Combiners
  3. 3.  Advanced MapReduce › Summarization Patterns › Filtering Patterns › Data Organization Patterns › Join Patterns › Meta Patterns › Input and Output Patterns  Programming Practices › Developing Map-Reduce Programs › Monitoring and Debugging on a cluster › Tuning for performance
  4. 4.  Hadoop Cookbook › Passing Job-Specific Parameters to your tasks › Probing for Task-Specific Parameters › Partitioning into multiple output files › Inputting from and output to database › Keeping Output in Sorted Order  Managing Hadoop › Checking System’s Health › Setting permissions › Managing Quotas , Enabling Trash , Adding/Deleting Nodes, Recovering from a failed NameNode
  5. 5.  Running Hadoop in the Cloud › Introducing Amazon Web Services › Setting up AWS and Setting up cloud on EC2 › Running Map-Reduce Programs on EC2 › Cleaning up and Shutting down your EC2 instances. › Amazon Elastic Map-Reduce and other AWS Services
  6. 6.  Programming with Pig › Thinking like a pig › Installing Pig › Running Pig › Learning Pig Latin through Grunt › Pig Latin Syntax › Working with UDF › Working with Scripts
  7. 7.  Getting Started on Hive  Data Types and File Formats  HiveQL – Data Definition  HiveQL - Data Manipulation  HiveQL – Queries, Views and Indexes  Schema Design , Tuning & Record Formats  Hive Integration with Oozie  Hive and Amazon Web Services
  8. 8.  NoSQL Database › Why No SQL ? › Aggregate Data Models › Distribution Models › Consistency  No SQL DBs › Key-Value DataBases › Document Databases › Column Family Stores › Graph Databases
  9. 9.  MongoDB › Introduction › MongoDB through JavaScript Shell › Writing Programs using MongoDB › Document Oriented Data › Queries and Aggregation › Updates, Atomic Operations and Deletes › Indexing, Replication and Sharding
  10. 10.  Mahout – Machine Learning › Introduction › Recommenders  Representing Recommender Data  Making Recommendations › Clustering  Clustering Algorithms in Mahout › Classification  Training a Classifier  Evaluating and Tuning a Classifier
  11. 11.  Moving Data in and out of Hadoop › Flume › Oozie › Sqoop › Hbase  Data Serialization Formats › XML, JSON › SequenceFiles, Protocol Buffers, Thrift and Avro
  12. 12.  Utilizing Data Structures and Algorithms › Modelling Data & Solving Problems with Graphs › Parallelized Bloom Filter Creation in Map- Reduce  Programming Pipelines with Pig › Using Pig to find malicious actors in log data. › Optimizing user workflow with Pig.
  13. 13.  Crunch  Cascading  Puppet  Unit Testing Map-Reduce  Heavyweight Job Testing using LocalJobRunner  Debugging User-Space Problems

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