This is an intermediate-level session for developers who want to leverage Hadoop and MapReduce.  Developers will learn the MapReduce framework and how to write programs against its API.  In addition to learning how to write individual MapReduce jobs, we will discuss design techniques for larger workflows. Finally, we'll discuss advanced skills for debugging MapReduce programs and optimizing their performance.  This training will be augmented by hands-on exercises. 

If you have no prior experience with Hadoop, you will want to take our one-day Introduction to Hadoop course. If you'd like to go deeper after this session, you might also consider one-day courses on HBase.

Agenda:

• Leveraging Hadoop and MapReduce
  • MapReduce and HDFS
  • Hands-on Exercise: Getting familiar with Hadoop
  • Programming with Hadoop
  • Hands-on Exercise: running a MapReduce job
• Augmenting Existing Systems with Hadoop
  • Hadoop rarely replaces existing infrastructure, but rather enables you to do more with your data by providing a scalable batch processing system. This section helps you understand how it all fits together.
• Best Practices for Data Processing Pipelines
  • In order for Hadoop to crunch large volumes of data, first you'll need to get that data into Hadoop. This section will help you understand how to import different types of data from various sources into Hadoop for further analysis.
• Debugging MapReduce Programs
  • Debugging in the distributed environment is challenging. This lecture will expose you to best practices for program design to mitigate debugging challenges, as well as local testing tools and techniques for debugging at scale.
• Advanced Hadoop API
  • This lecture probes into the API, covering custom data types and file formats, direct HDFS access,  intermediate data partitioning, and other tools such as the DistributedCache.
• Optimizing MapReduce Programs
  • This section will discuss advanced topics for optimizing MapReduce jobs.
• Advanced Algorithms
  • This lecture introduces some graph algorithms that can be adapted for your needs, as well as more involved examples like PageRank. We'll also look at strategies for implementing joins efficiently, and compare different techniques that are appropriate to different data models.

• Cloudera Certified Hadoop Developer exam