Hadoop Developer In Real World

Free Cluster Access * HDFS * MapReduce * YARN * Pig * Hive * Flume * Sqoop * AWS * EMR * Optimization * Troubleshooting

What you'll learn:

• Understand what is Big Data, the challenges with Big Data and how Hadoop propose a solution for the Big Data problem
• Work and navigate Hadoop cluster with ease
• Install and configure a Hadoop cluster on cloud services like Amazon Web Services (AWS)
• Understand the difference phases of MapReduce in detail
• Write optimized Pig Latin instruction to perform complex data analysis
• Write optimized Hive queries to perform data analysis on simple and nested datasets
• Work with file formats like SequenceFile, AVRO etc
• Understand Hadoop architecture, Single Point Of Failures (SPOF), Secondary/Checkpoint/Backup nodes, HA configuration and YARN
• Tune and optimize slowing running MapReduce jobs, Pig instructions and Hive queries
• Understand how Joins work behind the scenes and will be able to write optimized join statements
• Wherever possible, students will be introduced to difficult questions that are asked in real Hadoop interviews

From the creators of the successful Hadoop Starter Kit course hosted in Udemy, comes Hadoop In Real World course. This course is designed for anyone who aspire a career as a Hadoop developer. In this course we have covered all the concepts that every aspiring Hadoop developer must know to SURVIVE in REAL WORLD Hadoop environments.

The course covers all the must know topics like HDFS, MapReduce, YARN, Apache Pig and Hive etc. and we go deep in exploring the concepts. We just don’t stop with the easy concepts, we take it a step further and cover important and complex topics like file formats, custom Writables, input/output formats, troubleshooting, optimizations etc.

All concepts are backed by interesting hands-on projects like analyzing million song dataset to find less familiar artists with hot songs, ranking pages with page dumps from wikipedia, simulating mutual friends functionality in Facebook just to name a few.