• Understand the role of Big Data and use of Hadoop
• Illustrate history of Hadoop and comparison to other systems
• Define use of MapReduce and analysis capacities
• Demonstrate design of HDFS and major concepts (Blocks, Caching, Federation, etc.)
• Illustrate command line and use of basic commands
• Define Java interface for reading, writing, deleting of data and query operations
• Demonstrate role of YARM to split up the functionalities of resource management and job scheduling/monitoring into separate daemons
• Depict role of ResourceManager and the NodeManager to form the data-computation framework
• Illustrate Hadoop I/O with data integrity, use of compression tools and data serialization
• Demonstrate the MapReduce configuration API and define development environment
• Create unit Test within MapReduce using MRUnit
• Illustrate MapReduce execution locally and cluster-based
• Depict MapReduce workflows and decomposition into MapRecue Jobs
• Illustrate MapReduce flow thru submission, assignment and execution and demonstrate fallback when failures occur within environment
• Demonstrate MapReduce data types for input and output types
• Understand features of MapReduce for counters, joins, sorting and distribution
• Depict process for setting up a Hadoop Cluster via sizing and topology
• Demonstrate Hadoop Cluster installation, formatting and daemon startup
• Illustrate use of Kerberos for Hadoop security and use of delegation tokens
• Understand Hadoop administration techniques for HDFS (persistence, logging, etc), Monitoring (log files and metrics) and basic maintenance
• Optional Additions

Additional topics that may be added include Flume, Pig, Sqoop, Hive, Parquet and Flume.