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Course Outline
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Introduction
- Hadoop history and core concepts
- The Hadoop ecosystem
- Hadoop distributions
- High-level architecture overview
- Common Hadoop myths
- Hadoop challenges (hardware and software)
- Labs: Discuss your Big Data projects and challenges
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Planning and installation
- Selecting software and Hadoop distributions
- Sizing the cluster and planning for future growth
- Selecting appropriate hardware and network infrastructure
- Rack topology considerations
- Installation procedures
- Multi-tenancy implementation
- Directory structure and log management
- Benchmarking techniques
- Labs: Cluster installation and performance benchmarking
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HDFS operations
- Core concepts (horizontal scaling, replication, data locality, rack awareness)
- Nodes and daemons (NameNode, Secondary NameNode, HA Standby NameNode, DataNode)
- Health monitoring strategies
- Command-line and browser-based administration
- Adding storage and replacing defective drives
- Labs: Getting familiar with HDFS command-line operations
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Data ingestion
- Using Flume for logs and other data ingestion into HDFS
- Using Sqoop to import data from SQL databases to HDFS, and export back to SQL
- Hadoop data warehousing with Hive
- Copying data between clusters (distcp)
- Leveraging S3 as a complement to HDFS
- Best practices and architectures for data ingestion
- Labs: Setting up and using Flume and Sqoop
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MapReduce operations and administration
- Parallel computing before MapReduce: Comparing HPC with Hadoop administration
- MapReduce cluster workloads
- Nodes and daemons (JobTracker, TaskTracker)
- Walkthrough of the MapReduce UI
- MapReduce configuration
- Job configuration
- Optimising MapReduce performance
- Fool-proofing MapReduce: Guidance for programmers
- Labs: Running MapReduce examples
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YARN: New architecture and capabilities
- YARN design goals and implementation architecture
- New components: ResourceManager, NodeManager, Application Master
- Installing YARN
- Job scheduling under YARN
- Labs: Investigating job scheduling mechanisms
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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: Setting up monitoring systems
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Optional tracks
- Cloudera Manager for cluster administration, monitoring, and routine tasks; installation and usage. In this track, all exercises and labs are conducted within the Cloudera distribution environment (CDH5).
- Ambari for cluster administration, monitoring, and routine tasks; installation and usage. In this track, all exercises and labs are performed within the Ambari cluster manager and Hortonworks Data Platform (HDP 2.0).
Requirements
- Proficiency in basic Linux system administration
- Fundamental scripting skills
Prior knowledge of Hadoop and Distributed Computing is not required, as these concepts will be introduced and explained throughout the course.
Lab environment
Zero Install: There is no need to install Hadoop software on students’ machines! A fully functional Hadoop cluster will be provided for practical exercises.
Students will require the following:
- An SSH client (Linux and Mac users already have built-in SSH clients; for Windows users, PuTTY is recommended)
- A web browser to access the cluster. We recommend Firefox with the FoxyProxy extension installed.
21 Hours
Testimonials (1)
Hands on exercises. Class should have been 5 days, but the 3 days helped to clear up a lot of questions that I had from working with NiFi already
