Hadoop Training in West Covina
Enroll in or hire us to teach our Hadoop class in West Covina, California by calling us @303.377.6176. Like all HSG
classes, Hadoop may be offered either onsite or via instructor led virtual training. Consider looking at our public training schedule to see if it
is scheduled: Public Training Classes
Provided there are enough attendees, Hadoop may be taught at one of our local training facilities.
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We offer private customized training for groups of 3 or more attendees.
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Course Description |
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| Implement Hadoop Solutions throughout the enterprise If you're ready to
make the most out of massively scalable analytics, you need to know how
to take advantage of Hadoop technologies. With this class, you'll learn
how! It focuses on the architecture and approaches for building
advanced, Hadoop-based enterprise applications, taking a deeper,
code-level dive into delivering real-world solutions. It also walks you
through the data design and how it impacts implementation. It explains
how MapReduce works, and shows you how to reformulate specific business
problems in MapReduce. Throughout the chapters, you'll find in-depth
Java code examples that you can use, derived from applications that have
been successfully built and deployed.
Course Length: 3 Days
Course Tuition: $1590 (US) |
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Prerequisites |
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| Knowledge of database systems, SQL programming and a software development language is needed. Previous experience with the Java software language is required. | |
Course Outline |
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Chapter 1: Big Data and the Hadoop Ecosystem Chapter 2: Storing Data in Hadoop Chapter 3: Processing Your Data with MapReduce Chapter 4: Customizing MapReduce Execution Chapter 5: Building Reliable MapReduce Apps Chapter 6: Automating Data Processing with Oozie Chapter 7: Using Oozie Chapter 8: Advanced Oozie FEATURES Chapter 9: Real-Time Hadoop Chapter 10: Hadoop Security 331A Brief History: Understanding Hadoop Security Challenges Chapter 11: Running Hadoop Applications on AWS Chapter 12: Building Enterprise Security Solutions for Hadoop Implementations Chapter 13: Hadoop's Future |
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