';
Cloudera

Developer Training for Spark and Hadoop

Introduction

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 participants 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. Participants 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.

Hands-On Hadoop

Through instructor-led discussion and interactive, hands-on exercises, participants will learn Apache Spark and how it integrates with the entire Hadoop ecosystem, including:

  • 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

Audience & Prerequisites

This course is designed for developers and engineers who have programming experience. 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
Contents

 

  • Introduction to Apache Hadoop and the Hadoop Ecosystem
    • Apache Hadoop Overview
    • Data Storage and Ingest
    • Data Processing
    • Data Analysis and Exploration
    • Other Ecosystem Tools
    • Introduction to the Hands-On Exercises
  • Apache Hadoop File Storage
    • Problems with Traditional Large-Scale Systems
    • HDFS Architecture
    • Using HDFS
    • Apache Hadoop File Formats
  • Data Processing on an Apache Hadoop Cluster
    • YARN Architecture
    • Working With YARN
  • Importing Relational Data with Apache Sqoop
    • Apache Sqoop Overview
    • Importing Data
    • Importing File Options
    • Exporting Data
  • Apache Spark Basics
    • What is Apache Spark?
    • Using the Spark Shell
    • RDDs (Resilient Distributed Datasets)
    • Functional Programming in Spark
  • Working with RDDs
    • Creating RDDs
    • Other General RDD Operations
  • Aggregating Data with Pair RDDs
    • Key-Value Pair RDDs
    • Map-Reduce
    • Other Pair RDD Operations
  • Writing and Running Apache 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 Apache Spark Applications
    • Configuring Spark Properties
    • Logging
  • Parallel Processing in Apache Spark
    • Review: Apache Spark on a Cluster
    • RDD Partitions
    • Partitioning of File-Based RDDs
    • HDFS and Data Locality
    • Executing Parallel Operations
    • Stages and Tasks
  • RDD Persistence
    • RDD Lineage
    • RDD Persistence Overview
    • Distributed Persistence
  • Common Patterns in Apache Spark Data Processing
    • Common Apache Spark Use Cases
    • Iterative Algorithms in Apache Spark
    • Machine Learning
    • Example: k-means
  • DataFrames and Spark SQL
    • Apache Spark SQL and the SQL Context
    • Creating DataFrames
    • Transforming and Querying DataFrames
    • Saving DataFrames
    • DataFrames and RDDs
    • Comparing Apache Spark SQL, Impala, and Hive-on-Spark
  • Capturing Data with Apache Flume
    • What is Apache Flume?
    • Basic Flume Architecture
    • Flume Sources
    • Flume Sinks
    • Flume Channels
    • Flume Configuration
  • Conclusion
DateLink
18/03/2019
4 days		Buy Tickets
03/06/2019
4 days		Buy Tickets
14/10/2019
4 days		Buy Tickets
09/12/2019
4 days		Buy Tickets
Marionete