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Course Outline
- Introduction
- History and core concepts of Hadoop
- The ecosystem
- Distributions
- High-level architecture
- Common Hadoop myths
- Hadoop challenges (hardware and software)
- Labs: Discussion of participants' Big Data projects and challenges
- Planning and installation
- Selecting software and Hadoop distributions
- Cluster sizing and growth planning
- Selecting hardware and network infrastructure
- Rack topology
- Installation procedures
- Multi-tenancy
- Directory structure and logs
- Benchmarking
- Labs: Cluster installation and performance benchmarking
- HDFS operations
- Core 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 and replacing defective drives
- Labs: Familiarizing with HDFS command lines
- Data ingestion
- Using Flume for log and other data ingestion into HDFS
- Using Sqoop for importing from SQL databases to HDFS and exporting back
- Data warehousing with Hadoop using Hive
- Transferring data between clusters (distcp)
- Utilizing S3 as a complement to HDFS
- Best practices and architectures for data ingestion
- Labs: Setting up and utilizing Flume and Sqoop
- MapReduce operations and administration
- Parallel computing before MapReduce: Comparing HPC with Hadoop administration
- Managing MapReduce cluster loads
- Nodes and Daemons (JobTracker, TaskTracker)
- Walkthrough of the MapReduce UI
- MapReduce configuration
- Job configuration
- Optimizing MapReduce
- Mitigating errors: Guidance for developers
- Labs: Running MapReduce examples
- 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
- Advanced topics
- Hardware monitoring
- Cluster monitoring
- Adding/removing servers and upgrading Hadoop
- Backup, recovery, and business continuity planning
- Oozie job workflows
- Hadoop High Availability (HA)
- Hadoop Federation
- Securing clusters with Kerberos
- Labs: Setting up monitoring
- Optional tracks
- Cloudera Manager for cluster administration, monitoring, and routine tasks; installation and usage. All exercises and labs in this track are conducted within the Cloudera distribution environment (CDH5).
- Ambari for cluster administration, monitoring, and routine tasks; installation and usage. All exercises and labs in this track are conducted within the Ambari cluster manager and Hortonworks Data Platform (HDP 2.0).
Requirements
- Comfortable with basic Linux system administration
- Basic scripting skills
While prior knowledge of Hadoop and Distributed Computing is not mandatory, these concepts will be introduced and explained throughout the course.
Lab environment
Zero Install: There is no need for students to install Hadoop software on their personal machines. A fully functional Hadoop cluster will be provided for the duration of the course.
Participants will need the following:
- An SSH client (Linux and Mac systems come with built-in SSH clients; for Windows, 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
