Hadoop and Spark

DM103


4 Days

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Course Outline

The 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. How to employ Hadoop ecosystem projects such as Spark, Hive, Flume, Sqoop, and Impala. Learning about the 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.

Upcoming Meetings

Modules

Introduction to Hadoop and the Hadoop Ecosystem
  • Problems with Traditional Large-scale Systems
  • The Hadoop EcoSystem
Hadoop Architecture and HDFS
  • Distributed Processing on a Cluster
  • Storage: HDFS Architecture
  • Storage: Using HDFS
  • Resource Management: YARN Architecture
  • Resource Management: Working with YARN
Importing Relational Data with Apache Sqoop
  • Sqoop Overview
  • Basic Imports and Exports
  • Limiting Results
  • Improving Sqoop’s Performance
  • Sqoop 2
Introduction to Impala and Hive
  • Introduction to Impala and Hive
  • Why Use Impala and Hive?
  • Comparing Hive to Traditional Databases
  • Hive Use Cases
Modeling and Managing Data with Impala and Hive
  • Data Storage Overview
  • Creating Databases and Tables
  • Loading Data into Tables
  • HCatalog
  • Impala Metadata Caching
Data Formats
  • Selecting a File Format
  • Hadoop Tool Support for File Formats
  • Avro Schemas
  • Using Avro with Hive and Sqoop
  • Avro Schema Evolution
  • Compression
Data Partitioning
  • Partitioning Overview
  • Partitioning in Impala and Hive
Capturing Data with Apache Flume
  • What is Apache Flume?
  • Basic Flume Architecture
  • Flume Sources
  • Flume Sinks
  • Flume Channels
  • Flume Configuration
Spark Basics
  • What is Apache Spark?
  • Using the Spark Shell
  • RDDs (Resilient Distributed Datasets)
  • Functional Programming in Spark
Working with RDDs in Spark
  • A Closer Look at RDDs
  • Key-Value Pair RDDs
  • MapReduce
  • Other Pair RDD Operations
Writing and Deploying Spark Applications
  • Spark Applications vs. Spark Shell
  • Creating the SparkContext
  • Building a Spark Application (Scala & Java)
  • Running a Spark Application
  • The Spark Application Web UI
  • Configuring Spark Properties
  • Logging
Parallel Programming with Spark
  • Review: Spark on a Cluster
  • RDD Partitions
  • Partitioning of File-based RDDs
  • HDFS and Data Locality
  • Executing Parallel Operations
  • Stages and Tasks
Spark Caching and Persistence
  • RDD Lineage
  • Caching Overview
  • Distributed Persistence
Common Patterns in Spark Data Processing
  • Common Spark Use Cases
  • Iterative Algorithms in Spark
  • Graph Processing and Analysis
  • Machine Learning
  • Example: k-means
Spark SQL
  • Spark SQL and the SQL Context
  • Creating DataFrames
  • Transforming and Querying DataFrames
  • Saving DataFrames
  • Comparing Spark SQL with Impala

Prerequisites

  • Basic knowledge of database concepts and development environments

Upcoming Meetings

Target Audience

    • Israel
    • Poland
    • USA
    • Russia
    • India
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