Cloudera Developer Training for Spark and Hadoop

Cloudera Developer Training for Spark and Hadoop (HDP303)

Course Length: 4 days
Delivery Methods: Available as private class only
Course Overview

This Cloudera Developer Training for Spark and Hadoop training class 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.

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

Course Benefits
  • How the Apache Hadoop ecosystem fits in with the data processing lifecycle
  • How data is distributed, stored, and processed in a Hadoop cluster
  • How to write, configure, and deploy Apache Spark applications on a Hadoop cluster
  • How to use the Spark shell and Spark applications to explore, process, and analyze distributed data
  • How to query data using Spark SQL, DataFrames, and Datasets
  • How to use Spark Streaming to process a live data stream
Course Outline
  1. Introduction to Apache Hadoop and the Hadoop Ecosystem
    1. Apache Hadoop Overview
    2. Data Processing
    3. Introduction to the Hands-On Exercises
  2. Apache Hadoop File Storage
    1. Apache Hadoop Cluster Components
    2. HDFS Architecture
    3. Using HDFS
  3. Distributed Processing on an Apache Hadoop Cluster
    1. YARN Architecture
    2. Working With YARN
  4. Apache Spark Basics
    1. What is Apache Spark?
    2. Starting the Spark Shell
    3. Using the Spark Shell
    4. Getting Started with Datasets and DataFrames
    5. DataFrame Operations
  5. Working with DataFrames and Schemas
    1. Creating DataFrames from Data Sources
    2. Saving DataFrames to Data Sources
    3. DataFrame Schemas
    4. Eager and Lazy Execution
  6. Analyzing Data with DataFrame Queries
    1. Querying DataFrames Using Column Expressions
    2. Grouping and Aggregation Queries
    3. Joining DataFrames
  7. RDD Overview
    1. RDD Overview
    2. RDD Data Sources
    3. Creating and Saving RDDs
    4. RDD Operations
  8. Transforming Data with RDDs
    1. Writing and Passing Transformation Functions
    2. Transformation Execution
    3. Converting Between RDDs and DataFrames
  9. Aggregating Data with Pair RDDs
    1. Querying Tables in Spark Using SQL
    2. Querying Files and Views
    3. The Catalog API
    4. Comparing Spark SQL, Apache Impala, and Apache Hive-on-Spark
  10. Querying Tables and Views with SQL
    1. Querying Tables in Spark Using SQL
    2. Querying Files and Views
    3. The Catalog API
  11. Working with Datasets in Scala
    1. Datasets and DataFrames
    2. Creating Datasets
    3. Loading and Saving Datasets
    4. Dataset Operations
  12. Writing, Configuring, and Running Spark Applications
    1. Writing a Spark Application
    2. Building and Running an Application
    3. Application Deployment Mode
    4. The Spark Application Web UI
    5. Configuring Application Properties
  13. Spark Distributed Processing
    1. Review: Apache Spark on a Cluster
    2. RDD Partitions
    3. Example: Partitioning in Queries
    4. Stages and Tasks
    5. Job Execution Planning
    6. Example: Catalyst Execution Plan
    7. Example: RDD Execution Plan
  14. Distributed Data Persistence
    1. DataFrame and Dataset Persistence
    2. Persistence Storage Levels
    3. Viewing Persisted RDDs
  15. Common Patterns in Spark Data Processing
    1. Common Apache Spark Use Cases
    2. Iterative Algorithms in Apache Spark
    3. Machine Learning
    4. Example: k-means
  16. Introduction to Structured Streaming
    1. Apache Spark Streaming Overview
    2. Creating Streaming DataFrames
    3. Transforming DataFrames
    4. Executing Streaming Queries
  17. Structured Streaming with Apache Kafka
    1. Overview
    2. Receiving Kafka Messages
    3. Sending Kafka Messages
  18. Aggregating and Joining Streaming DataFrames
    1. Streaming Aggregation
    2. Joining Streaming DataFrames
  19. Conclusion
    1. Message Processing with Apache Kafka
    2. What Is Apache Kafka?
    3. Apache Kafka Overview
    4. Scaling Apache Kafka
    5. Apache Kafka Cluster Architecture
    6. Apache Kafka Command Line Tools
Class Materials

Each student will receive a comprehensive set of materials, including course notes and all the class examples.

Class Prerequisites

Experience in the following is required for this Hadoop class:

  • The ability to program in Scala or Python is required.
  • Basic familiarity with the Linux command line.

Experience in the following would be useful for this Hadoop class:

  • Basic knowledge of SQL.
Live Private Class
  • Private Class for your Team
  • Live training
  • Online or On-location
  • Customizable
  • Expert Instructors
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