Hadoop Programming with Java for Big Data Solutions
Virtual ClassroomLearning Style
4 DaysCourse Duration
About Individual Course:
About this course:
The availability of large data sets presents new opportunities and challenges to organizations of all sizes. In this course, you will implement a strategy for developing Hadoop jobs and extracting business value from large and varied data sets. This Apache Hadoop development training is essential for programmers who want to augment their programming skills to use Hadoop for a variety of big data solutions.
The average salary for Hadoop Developer is $139,000 per year.
After completing this course, students will be able to:
- Write, customize, and deploy Java MapReduce jobs to summarize data
- Develop Hive and Pig queries to simplify data analysis
- Test and debug jobs using MRUnit
- Monitor task execution and cluster health
This course is intended for:
- Big Data Engineers
- Java experience through Java introductory course, or at least six months of Java programming experience
Suggested prerequisites courses:
Virtual Instructed-Led Outline
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 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
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
- 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
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
- 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
Simplifying Data Analysis with Query Languages
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)
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
|Learning Style||Virtual Classroom|
|Course Duration||4 Days|
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