By 2020. IT market will grow from 54B USD to 85B USD and 38% services will comprise of Big Data. Hence you are making the right decision to get into Hadoop Learning. In-person Classroom training.

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Hadoop is an open source, a Java-based programming framework that supports the processing and storage of extremely large data sets in a distributed computing environment. It is part of the Apache project sponsored by the Apache Software Foundation that is used widely to carry out Big Data Analytics very popular since 2016 onwards.

Introduction to Hadoop

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

 

Parallelizing Program Execution
Meeting the challenges of parallel programming

Investigating parallelizable 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

Implementing Real-World MapReduce Jobs
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
Coordinating jobs with Oozie workflow scheduler
Streaming tasks through various programming languages

 

Customizing MapReduce
Solving common data manipulation problems

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

 

Implementing partitioners and combiners

Identifying network bound, CPU bound and disk I/O bound parallel algorithms
Reducing network traffic with combiners
Dividing the workload efficiently using partitioners
Collecting metrics with counters

Persisting Big Data with Distributed Data Stores
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
Partitioning text or binary data
Manipulating Hadoop SequenceFile types

 

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

 

Simplifying Data Analysis with Query Languages
Unleashing the power of SQL with Hive

Structuring data with the Hive MetaStore
Extracting, Transforming and Loading (ETL) data
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)

 

Managing and Deploying Big Data Solutions
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