Course Outline

  • Introduction
    • Hadoop history, concepts
    • Ecosystem
    • Distributions
    • High level architecture
    • Hadoop myths
    • Hadoop challenges (hardware / software)
    • Labs: discuss your Big Data projects and problems
  • Planning and installation
    • Selecting software, Hadoop distributions
    • Sizing the cluster, planning for growth
    • Selecting hardware and network
    • Rack topology
    • Installation
    • Multi-tenancy
    • Directory structure, logs
    • Benchmarking
    • Labs: cluster install, run performance benchmarks
  • HDFS operations
    • Concepts (horizontal scaling, replication, data locality, rack awareness)
    • Nodes and daemons (NameNode, Secondary NameNode, HA Standby NameNode, DataNode)
    • Health monitoring
    • Command-line and browser-based administration
    • Adding storage, replacing defective drives
    • Labs: getting familiar with HDFS command lines
  • Data ingestion
    • Flume for logs and other data ingestion into HDFS
    • Sqoop for importing from SQL databases to HDFS, as well as exporting back to SQL
    • Hadoop data warehousing with Hive
    • Copying data between clusters (distcp)
    • Using S3 as complementary to HDFS
    • Data ingestion best practices and architectures
    • Labs: setting up and using Flume, the same for Sqoop
  • MapReduce operations and administration
    • Parallel computing before mapreduce: compare HPC vs Hadoop administration
    • MapReduce cluster loads
    • Nodes and Daemons (JobTracker, TaskTracker)
    • MapReduce UI walk through
    • Mapreduce configuration
    • Job config
    • Optimizing MapReduce
    • Fool-proofing MR: what to tell your programmers
    • Labs: running MapReduce examples
  • YARN: new architecture and new capabilities
    • YARN design goals and implementation architecture
    • New actors: ResourceManager, NodeManager, Application Master
    • Installing YARN
    • Job scheduling under YARN
    • Labs: investigate job scheduling
  • Advanced topics
    • Hardware monitoring
    • Cluster monitoring
    • Adding and removing servers, upgrading Hadoop
    • Backup, recovery and business continuity planning
    • Oozie job workflows
    • Hadoop high availability (HA)
    • Hadoop Federation
    • Securing your cluster with Kerberos
    • Labs: set up monitoring
  • Optional tracks
    • Cloudera Manager for cluster administration, monitoring, and routine tasks; installation, use. In this track, all exercises and labs are performed within the Cloudera distribution environment (CDH5)
    • Ambari for cluster administration, monitoring, and routine tasks; installation, use. In this track, all exercises and labs are performed within the Ambari cluster manager and Hortonworks Data Platform (HDP 2.0)

Requirements

  • comfortable with basic Linux system administration
  • basic scripting skills

Knowledge of Hadoop and Distributed Computing is not required, but will be introduced and explained in the course.

Lab environment

Zero Install : There is no need to install hadoop software on students’ machines! A working hadoop cluster will be provided for students.

Students will need the following

  21 Hours
 

Testimonials

Related Courses

Apache Ambari: Efficiently Manage Hadoop Clusters

 21 hours

Apache Ambari is an open-source management platform for provisioning, managing, monitoring and securing Apache Hadoop clusters. In this instructor-led live training participants will learn the management tools and practices provided by Ambari to

Administrator Training for Apache Hadoop

 35 hours

Audience: The course is intended for IT specialists looking for a solution to store and process large data sets in a distributed system environment Goal: Deep knowledge on Hadoop cluster

Apache Hadoop: Manipulation and Transformation of Data Performance

 21 hours

This course is intended for developers, architects, data scientists or any profile that requires access to data either intensively or on a regular basis. The major focus of the course is data manipulation and transformation. Among the tools

Hadoop Administration

 21 hours

The course is dedicated to IT specialists that are looking for a solution to store and process large data sets in distributed system environment Course goal: Getting knowledge regarding Hadoop cluster

Hadoop for Business Analysts

 21 hours

Apache Hadoop is the most popular framework for processing Big Data. Hadoop provides rich and deep analytics capability, and it is making in-roads in to tradional BI analytics world. This course will introduce an analyst to the core components of

Hadoop for Developers (4 days)

 28 hours

Apache Hadoop is the most popular framework for processing Big Data on clusters of servers. This course will introduce a developer to various components (HDFS, MapReduce, Pig, Hive and HBase) Hadoop

Advanced Hadoop for Developers

 21 hours

Apache Hadoop is one of the most popular frameworks for processing Big Data on clusters of servers. This course delves into data management in HDFS, advanced Pig, Hive, and HBase.  These advanced programming techniques will be beneficial to

Hadoop for Developers and Administrators

 21 hours

Hadoop is the most popular Big Data processing framework.

Hadoop for Project Managers

 14 hours

As more and more software and IT projects migrate from local processing and data management to distributed processing and big data storage, Project Managers are finding the need to upgrade their knowledge and skills to grasp the concepts and

Hadoop Administration on MapR

 28 hours

Audience: This course is intended to demystify big data/hadoop technology and to show it is not difficult to understand.

HBase for Developers

 21 hours

This course introduces HBase – a NoSQL store on top of Hadoop.  The course is intended for developers who will be using HBase to develop applications,  and administrators who will manage HBase clusters. We will walk a developer

Hortonworks Data Platform (HDP) for Administrators

 21 hours

Hortonworks Data Platform (HDP) is an open-source Apache Hadoop support platform that provides a stable foundation for developing big data solutions on the Apache Hadoop ecosystem. This instructor-led, live training (online or onsite) introduces

Data Analysis with Hive/HiveQL

 7 hours

This course covers how to use Hive SQL language (AKA: Hive HQL, SQL on Hive, HiveQL) for people who extract data from Hive

Impala for Business Intelligence

 21 hours

Cloudera Impala is an open source massively parallel processing (MPP) SQL query engine for Apache Hadoop clusters. Impala enables users to issue low-latency SQL queries to data stored in Hadoop Distributed File System and Apache

Apache Avro: Data Serialization for Distributed Applications

 14 hours

Audience Developers Format of the Course Lectures, hands-on practice, small tests along the way to gauge understanding