Cloudera Developer Training for Spark and Hadoop I

• Programmers
• Developers
• Engineers

• 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.

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

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.

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