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Cloudera Developer Training for Spark & Hadoop

  • Tuition USD $3,195 GSA  $2,736.27
  • Reviews star_rate star_rate star_rate star_rate star_half 524 Ratings
  • Course Code DEV-S-H
  • Duration 4 days
  • Available Formats Classroom, Virtual
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This four-day hands-on training course delivers the key concepts and expertise developers need to use Apache Spark to develop high-performance parallel applications. Participants will learn how to use Spark SQL to query structured data and Spark Streaming to perform real-time processing on streaming data from a variety of sources. Developers will also practice writing applications that use core Spark to perform ETL processing and iterative algorithms. The course covers how to work with “big data” stored in a distributed file system, and execute Spark applications on a Hadoop cluster. After taking this course, participants will be prepared to face real-world challenges and build applications to execute faster decisions, better decisions, and interactive analysis, applied to a wide variety of use cases, architectures, and industries.

Who Can Benefit

This course is designed for developers and engineers who have programming experience, but prior knowledge of Hadoop and/or Spark is not required.

Prerequisites

This course is designed for developers and engineers who have programming experience, but prior knowledge of Spark and Hadoop is not required. Apache Spark examples and hands-on exercises are presented in Scala and Python. The ability to program in one of those languages is required. Basic familiarity with the Linux command line is assumed. Basic knowledge of SQL is helpful.

Course Details

Course Outline

1. Introduction

2. Introduction to Apache Hadoop and the Hadoop Ecosystem

  • Apache Hadoop Overview
  • Data Processing
  • Introduction to the Hands-On Exercises

3. Apache Hadoop File Storage

  • Apache Hadoop Cluster Components
  • HDFS Architecture
  • Using HDFS

4. Distributed Processing on an Apache Hadoop Cluster

  • YARN Architecture
  • Working With YARN

5. Apache Spark Basics

  • What is Apache Spark?
  • Starting the Spark Shell
  • Using the Spark Shell
  • Getting Started with Datasets and DataFrames
  • DataFrame Operations

6. Working with DataFrames and Schemas

  • Creating DataFrames from Data Sources
  • Saving DataFrames to Data Sources
  • DataFrame Schemas
  • Eager and Lazy Execution

7. Analyzing Data with DataFrame Queries

  • Querying DataFrames Using Column Expressions
  • Grouping and Aggregation Queries
  • Joining DataFrames

8. RDD Overview

  • RDD Overview
  • RDD Data Sources
  • Creating and Saving RDDs
  • RDD Operations

9. Transforming Data with RDDs

  • Writing and Passing Transformation Functions
  • Transformation Execution
  • Converting Between RDDs and DataFrames

10. Aggregating Data with Pair RDDs

  • Querying Tables in Spark Using SQL
  • Querying Files and Views
  • The Catalog API
  • Comparing Spark SQL, Apache Impala, and Apache Hive-on-Spark

11. Querying Tables and Views with SQL

  • Querying Tables in Spark Using SQL
  • Querying Files and Views
  • The Catalog API

12. Working with Datasets in Scala

  • Datasets and DataFrames
  • Creating Datasets
  • Loading and Saving Datasets
  • Dataset Operations

13. Writing, Configuring, and Running Spark Applications

  • Writing a Spark Application
  • Building and Running an Application
  • Application Deployment Mode
  • The Spark Application Web UI
  • Configuring Application Properties

14. Spark Distributed Processing

  • Review: Apache Spark on a Cluster
  • RDD Partitions
  • Example: Partitioning in Queries
  • Stages and Tasks
  • Job Execution Planning
  • Example: Catalyst Execution Plan
  • Example: RDD Execution Plan

15. Distributed Data Persistence

  • DataFrame and Dataset Persistence
  • Persistence Storage Levels
  • Viewing Persisted RDDs

16. Common Patterns in Spark Data Processing

  • Common Apache Spark Use Cases
  • Iterative Algorithms in Apache Spark
  • Machine Learning
  • Example: k-means

17. Introduction to Structured Streaming

  • Apache Spark Streaming Overview
  • Creating Streaming DataFrames
  • Transforming DataFrames
  • Executing Streaming Queries

18. Structured Streaming with Apache Kafka

  • Overview
  • Receiving Kafka Messages
  • Sending Kafka Messages

19. Aggregating and Joining Streaming DataFrames

  • Streaming Aggregation
  • Joining Streaming DataFrames

20. Conclusion

A. Message Processing with Apache Kafka

  • What Is Apache Kafka?
  • Apache Kafka Overview
  • Scaling Apache Kafka
  • Apache Kafka Cluster Architecture
  • Apache Kafka Command Line Tools
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When does class start/end?

Classes begin promptly at 9:00 am, and typically end at 5:00 pm.

Does the course schedule include a Lunchbreak?

Lunch is normally an hour long and begins at noon. Coffee, tea, hot chocolate and juice are available all day in the kitchen. Fruit, muffins and bagels are served each morning. There are numerous restaurants near each of our centers, and some popular ones are indicated on the Area Map in the Student Welcome Handbooks - these can be picked up in the lobby or requested from one of our ExitCertified staff.

How can someone reach me during class?

If someone should need to contact you while you are in class, please have them call the center telephone number and leave a message with the receptionist.

What languages are used to deliver training?

Most courses are conducted in English, unless otherwise specified. Some courses will have the word "FRENCH" marked in red beside the scheduled date(s) indicating the language of instruction.

What does GTR stand for?

GTR stands for Guaranteed to Run; if you see a course with this status, it means this event is confirmed to run. View our GTR page to see our full list of Guaranteed to Run courses.

Does ExitCertified deliver group training?

Yes, we provide training for groups, individuals and private on sites. View our group training page for more information.

I came to learn and get some high level understanding of Hadoop and Spark. I feel confident about all Spark can do and can apply this to my work

It was very pleasant experience in going through this training with Charles Hardin. It is truly amazing to see Charles passion in teaching and technology.

Eric was a superb instructor that effortlessly explained complex topics in a simple and a fun way.

Joel is one of the best instructor to listen to and explain the way you expect from a specialist . He is the best in industry.

Eric has been able to fill in nearly all knowledge gap I have. During this week, I have actually used the knowledge gained in actual cases.

5 options available

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  • Oct 13, 2020 – Oct 16, 2020 (4 days)
    Location
    Virtual
    Language
    English
    Time
    10:00 am – 6:00 pm EDT
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  • Oct 27, 2020 – Oct 30, 2020 (4 days)
    Location
    iMVP
    Language
    English
    Time
    9:00AM – 5:00PM EDT
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  • Nov 17, 2020 – Nov 20, 2020 (4 days)
    Location
    Virtual
    Language
    English
    Time
    10:00 am – 6:00 pm EST
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  • Dec 15, 2020 – Dec 18, 2020 (4 days)
    Location
    Virtual
    Language
    English
    Time
    10:00 am – 6:00 pm EST
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  • Jan 12, 2021 – Jan 15, 2021 (4 days)
    Location
    Virtual
    Language
    English
    Time
    10:00 am – 6:00 pm EST
    Enroll
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