Evaluate the value that Hadoop can bring to the business
Examine the Hadoop ecosystem
Choose a suitable distribution model

Examine the difficulties associated with running parallel programs: algorithms, data exchange
Evaluate the storage mode and complexity of Big Data

Fragment and solve large-scale problems
Discover tasks compatible with MapReduce
Solve common business problems

Configure the development environment
Examine the Hadoop distribution
Study the Hadoop daemons
Create the different components of MapReduce tasks
Analyze the different stages of MapReduce processing: split, map, shuffle and shrink

Choose and use multiple mapping and reduction tools, leverage partitioners and built-in map and reduce functions, analyze time series data with a second sort, streamline tasks in different programming languages

Run algorithms: parallel sorts, joins and searches, analyze log files, social media data and emails

Identify parallel algorithms related to network, processor, and disk I / O
Distribute workload with partitioners
Control grouping and sort order with comparators
Measure performance with counters

Optimize data throughput performance
Use redundancy to recover data