Hadoop certifications training
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Hadoop certifications training

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or full course details please visit our website www.hadooponlinetraining.net

Duration for course is 30 days or 45 hours and special care will be taken. It is a one to one training with hands on experience.

* Resume preparation and Interview assistance will be provided.
For any further details please

contact India +91-9052666559
Usa : +1-678-693-3475.

visit www.hadooponlinetraining.net

please mail us all queries to info@magnifictraining.com

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    Hadoop certifications training Hadoop certifications training Presentation Transcript

    • Hadoop certifications training Magnific training 9052666559
    • • Cloudera Certified Developer for Apache Hadoop (CCDH) • Cloudera delivers the industry's only certification for software developers on Hadoop. Built on the content of our training courses, the program tests your knowledge of Hadoop's operation and use.
    • • Detailed Agenda: • 1. The Motivation For Hadoop • Problems with traditional large-scale systems • Requirements for a new approach • Introducing Hadoop
    • 2. Hadoop: Basic Concepts • The Hadoop Project and Hadoop Components • The Hadoop Distributed File System • Hands-On Exercise: Using HDFS • How MapReduce Works • Hands-On Exercise: Running a MapReduce Job • How a Hadoop Cluster Operates • Other Hadoop Ecosystem Projects
    • • 3. Writing a MapReduce Program • The MapReduce Flow • Basic MapReduce API Concepts • Writing MapReduce Drivers, Mappers and Reducers in Java • Writing Mappers and Reducers in Other Languages Using the Streaming API • Speeding Up Hadoop Development by Using Eclipse • Hands-On Exercise: Writing a MapReduce Program • Differences Between the Old and New MapReduce APIs
    • • 4. Unit Testing MapReduce Programs • Unit Testing • The JUnit and MRUnit Testing Frameworks • Writing Unit Tests with MRUnit • Hands-On Exercise: Writing Unit Tests with the MRUnit Framework
    • • 5. Delving Deeper into the Hadoop API • Using the ToolRunner Class • Hands-On Exercise: Writing and Implementing a Combiner • Setting Up and Tearing Down Mappers and Reducers by Using the Configure and Close Methods • Writing Custom Partitioners for Better Load Balancing • Optional Hands-On Exercise: Writing a Partitioner.
    • • 6. Practical Development Tips and Techniques • Strategies for Debugging MapReduce Code • Testing MapReduce Code Locally by Using LocalJobReducer • Writing and Viewing Log Files • Retrieving Job Information with Counters • Determining the Optimal Number of Reducers for a Job • Creating Map-Only MapReduce Jobs
    • • 7. Data Input and Output • Creating Custom Writable and WritableComparable Implementations • Saving Binary Data Using SequenceFile and Avro Data Files