HDP Developer Apache Pig and HiveNewly Released

HDP Developer Apache Pig and Hive Course Description

Duration: 4.00 days (32 hours)

This course is designed for developers who need to create applications to analyze Big Data stored in Apache Hadoop using Pig and Hive. Topics include: Hadoop, YARN, HDFS, MapReduce, data ingestion, workflow definition, using Pig and Hive to perform data analytics on Big Data and an introduction to Spark Core and Spark SQL.

Next Class Dates

Feb 19, 2018 – Feb 22, 2018
8:00 AM – 4:00 PM MT
519 8th Avenue, 2nd Floor, New York, NY 10018
New York, NY 10018

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Contact us to customize this class with your own dates, times and location. You can also call 1-888-563-8266 or chat live with a Learning Consultant.

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Intended Audience for this HDP Developer Apache Pig and Hive Course

  • » Software developers who need to understand and develop applications for Hadoop.

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Course Prerequisites for HDP Developer Apache Pig and Hive

  • » Students should be familiar with programming principles and have experience in software development. SQL knowledge is also helpful. No prior Hadoop knowledge is required.

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HDP Developer Apache Pig and Hive Course Objectives

  • » Describe Hadoop, YARN and use cases for Hadoop
  • » Describe Hadoop ecosystem tools and frameworks
  • » Describe the HDFS architecture
  • » Use the Hadoop client to input data into HDFS
  • » Transfer data between Hadoop and a relational database
  • » Explain YARN and MaoReduce architectures
  • » Run a MapReduce job on YARN
  • » Use Pig to explore and transform data in HDFS
  • » Understand how Hive tables are defined and implemented
  • » Use Hive to explore and analyze data sets
  • » Use the new Hive windowing functions
  • » Explain and use the various Hive file formats
  • » Create and populate a Hive table that uses ORC file formats
  • » Use Hive to run SQL-like queries to perform data analysis
  • » Use Hive to join datasets using a variety of techniques
  • » Write efficient Hive queries
  • » Create ngrams and context ngrams using Hive
  • » Perform data analytics using the DataFu Pig library
  • » Explain the uses and purpose of HCatalog
  • » Use HCatalog with Pig and Hive
  • » Define and schedule an Oozie workflow
  • » Present the Spark ecosystem and high-level architecture
  • » Perform data analysis with Spark's Resilient Distributed Dataset API
  • » Explore Spark SQL and the DataFrame API

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HDP Developer Apache Pig and Hive Course Outline

      1. Describe Hadoop, YARN and use cases for Hadoop
      2. Describe Hadoop ecosystem tools and frameworks
      3. Describe the HDFS architecture
      4. Use the Hadoop client to input data into HDFS
      5. Transfer data between Hadoop and a relational database
      6. Explain YARN and MaoReduce architectures
      7. Run a MapReduce job on YARN
      8. Use Pig to explore and transform data in HDFS
      9. Understand how Hive tables are defined and implemented
      10. Use Hive to explore and analyze data sets
      11. Use the new Hive windowing functions
      12. Explain and use the various Hive file formats
      13. Create and populate a Hive table that uses ORC file formats
      14. Use Hive to run SQL-like queries to perform data analysis
      15. Use Hive to join datasets using a variety of techniques
      16. Write efficient Hive queries
      17. Create ngrams and context ngrams using Hive
      18. Perform data analytics using the DataFu Pig library
      19. Explain the uses and purpose of HCatalog
      20. Use HCatalog with Pig and Hive
      21. Define and schedule an Oozie workflow
      22. Present the Spark ecosystem and high-level architecture
      23. Perform data analysis with Spark's Resilient Distributed Dataset API
      24. Explore Spark SQL and the DataFrame API
      25. Labs
        1. Use HDFS commands to add/remove files and folders
        2. Use Sqoop to transfer data between HDFS and a RDBMS
        3. Run MapReduce and YARN application jobs
        4. Explore, transform, split and join datasets using Pig
        5. Use Pig to transform and export a dataset for use with Hive
        6. Use HCatLoader and HCatStorer
        7. Use Hive to discover useful information in a dataset
        8. Describe how Hive queries get executed as MapReduce jobs
        9. Perform a join of two datasets with Hive
        10. Use advanced Hive features: windowing, views, ORC files
        11. Use Hive analytics functions
        12. Write a custom reducer in Python
        13. Analyze clickstream data and compute quantiles with DataFu
        14. Use Hive to compute ngrams on Avro-formatted files
        15. Define an Oozie workflow
        16. Use Spark Core to read files and perform data analysis
        17. Create and join DataFrames with Spark SQL

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Do you have the right background for HDP Developer Apache Pig and Hive?

Skills Assessment

We ensure your success by asking all students to take a FREE Skill Assessment test. These short, instructor-written tests are an objective measure of your current skills that help us determine whether or not you will be able to meet your goals by attending this course at your current skill level. If we determine that you need additional preparation or training in order to gain the most value from this course, we will recommend cost-effective solutions that you can use to get ready for the course.

Our required skill-assessments ensure that:

  1. All students in the class are at a comparable skill level, so the class can run smoothly without beginners slowing down the class for everyone else.
  2. NetCom students enjoy one of the industry's highest success rates, and pass rates when a certification exam is involved.
  3. We stay committed to providing you real value. Again, your success is paramount; we will register you only if you have the skills to succeed.
This assessment is for your benefit and best taken without any preparation or reference materials, so your skills can be objectively measured.

Take your FREE Skill Assessment test »

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