Hadoop Programming with Java for Big Data Solutions

• Identifying the business benefits of Hadoop
• Surveying the Hadoop ecosystem
• Selecting a suitable distribution

Meeting the challenges of parallel programming

• Investigating parallelisable challenges: algorithms, data and information exchange
• Estimating the storage and complexity of Big Data

Parallel programming with MapReduce

• Dividing and conquering large-scale problems
• Uncovering jobs suitable for MapReduce
• Solving typical business problems

Applying the Hadoop MapReduce paradigm

• Configuring the development environment
• Exploring the Hadoop distribution
• Creating the components of MapReduce jobs
• Introducing the Hadoop daemons
• Analyzing the stages of MapReduce processing: splitting, mapping, shuffling and reducing

Building complex MapReduce jobs

• Selecting and employing multiple mappers and reducers
• Leveraging built-in mappers, reducers and partitioners
• Analyzing time series data with secondary sort
• Streaming tasks through various programming languages

Solving common data manipulation problems

• Executing algorithms: parallel sorts, joins and searches
• Analyzing log files, social media data and e-mails

Implementing partitioners and comparators

• Identifying network-bound, CPU-bound and disk I/O-bound parallel algorithms
• Dividing the workload efficiently using partitioners
• Controlling grouping and sort order with comparators
• Collecting metrics with counters

Making the case for distributed data

• Achieving high performance data throughput
• Recovering from media failure through redundancy

Interfacing with Hadoop Distributed File System (HDFS)

• Breaking down the structure and organization of HDFS
• Loading raw data and retrieving results
• Reading and writing data programmatically
• Manipulating Hadoop SequenceFile types
• Sharing reference data with DistributedCache

Structuring data with HBase

• Migrating from structured to unstructured storage
• Applying NoSQL concepts with schema on read
• Connecting to HBase from MapReduce jobs
• Comparing HBase to other types of NoSQL data stores

Unleashing the power of SQL with Hive

• Structuring databases, tables, views and partitions
• Integrating MapReduce jobs with Hive queries
• Querying with HiveQL
• Accessing Hive servers through JDBC
• Extending HiveQL with User-Defined Functions (UDF)

Executing workflows with Pig

• Developing Pig Latin scripts to consolidate workflows
• Integrating Pig queries with Java
• Interacting with data through the grunt console
• Extending Pig with User-Defined Functions (UDF)

Testing and debugging Hadoop code

• Logging significant events for auditing and debugging
• Debugging in local mode
• Validating requirements with MRUnit

Deploying, monitoring and tuning performance

• Deploying to a production cluster
• Optimizing performance with administrative tools
• Monitoring job execution through web user interfaces