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Cloudera Developer Training for Spark and Hadoop (CDTSH1)

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Cloudera Developer Training for Spark and Hadoop is also available in OnDemand e-learning.

$2235.00 USD

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Course Content

Learn how to import data into your Apache Hadoop cluster and process it with Spark, Hive, Flume, Sqoop, Impala, and other Hadoop ecosystem tools.

This four-day hands-on training course delivers the key concepts and expertise you need to ingest and process data on a Hadoop cluster using the most up-to-date tools and techniques. Employing Hadoop ecosystem projects such as Spark, Hive, Flume, Sqoop, and Impala, this training course is the best preparation for the real-world challenges faced by Hadoop developers. You will learn to identify which tool is the right one to use in a given situation, and will gain hands-on experience in developing using those tools.

This course is an excellent place to start for people working towards the CCA Spark & Hadoop Developer certification. Although further study is required before passing the exam, this course covers many of the subjects tested in the CCA Spark & Hadoop Developer exam.

Who should attend

  • Programmers
  • Developers
  • Engineers

Prerequisites

  • Apache Spark examples and hands-on exercises are presented in Scala and Python, so 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
  • Prior knowledge of Hadoop is not required.

Course Objectives

By the end of this course, you will learn:

  • How data is distributed, stored, and processed in a Hadoop cluster
  • How to use Sqoop and Flume to ingest data
  • How to process distributed data with Apache Spark
  • How to model structured data as tables in Impala and Hive
  • How to choose the best data storage format for different data usage patterns
  • Best practices for data storage

Follow On Courses

Detailed Course Outline

Module 1: Introduction to Hadoop and the Hadoop Ecosystem

  • Problems with Traditional Large-Scale Systems
  • Hadoop!
  • Data Storage and Ingest
  • Data Processing
  • Data Analysis and Exploration
  • Other Ecosystem Tools
  • Introduction to the Hands-On Exercises

Module 2: Hadoop Architecture and HDFS

  • Distributed Processing on a Cluster
  • Storage: HDFS Architecture
  • Storage: Using HDFS
  • Resource Management: YARN Architecture
  • Resource Management: Working with YARN

Module 3: Importing Relational Data with Apache Sqoop

  • Sqoop Overview
  • Basic Imports and Exports
  • Limiting Results
  • Improving Sqoop’s Performance
  • Sqoop 2

Module 4: Introduction to Impala and Hive

  • Introduction to Impala and Hive
  • Why Use Impala and Hive?
  • Querying Data With Impala and Hive
  • Comparing Hive and Impala to Traditional Databases

Module 5: Modeling and Managing Data with Impala and Hive

  • Data Storage Overview
  • Creating Databases and Tables
  • Loading Data into Tables
  • HCatalog
  • Impala Metadata Caching

Module 6: Data Formats

  • Selecting a File Format
  • Hadoop Tool Support for File Formats
  • Avro Schemas
  • Using Avro with Hive and Sqoop
  • Avro Schema Evolution
  • Compression

Module 7: Data Partitioning

  • Partitioning Overview
  • Partitioning in Impala and Hive

Module 8: Capturing Data with Apache Flume

  • What is Apache Flume?
  • Basic Flume Architecture
  • Flume Sources
  • Flume Sinks
  • Flume Channels
  • Flume Configuration

Module 9: Spark Basics

  • What is Apache Spark?
  • Using the Spark Shell
  • RDDs (Resilient Distributed Datasets)
  • Functional Programming in Spark

Module 10: Working with RDDs in Spark

  • Creating RDDs
  • Other General RDD Operations

Module 11: Writing and Deploying Spark Applications

  • Spark Applications vs. Spark Shell
  • Creating the SparkContext
  • Building a Spark Application (Scala and Java)
  • Running a Spark Application
  • The Spark Application Web UI
  • Configuring Spark Properties
  • Logging

Module 12: Parallel Processing in Spark

  • Review: Spark on a Cluster
  • RDD Partitions
  • Partitioning of File-based RDDs
  • HDFS and Data Locality
  • Executing Parallel Operations
  • Stages and Tasks

Module 13: Spark RDD Persistence

  • RDD Lineage
  • RDD Persistence Overview
  • Distributed Persistence

Module 14: Common Patterns in Spark Data Processing

  • Common Spark Use Cases
  • Iterative Algorithms in Spark
  • Graph Processing and Analysis
  • Machine Learning
  • Example: k-means

Module 15: DataFrames and Spark SQL

  • Spark SQL and the SQL Context
  • Creating DataFrames
  • Transforming and Querying DataFrames
  • Saving DataFrames
  • Comparing Spark SQL, Impala and Hive-on-Spark
Classroom Training

Duration 4 days

Price
  • United States: US$ 3,195
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Online Training

Duration 4 days

Price
  • United States: US$ 3,195
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