Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
-
Introduction
- Hadoop history and core concepts
- Ecosystem overview
- Distributions
- High-level architecture
- Common Hadoop myths
- Hadoop challenges (hardware and software)
- Labs: Discussing your Big Data projects and problems
-
Planning and installation
- Selecting software and Hadoop distributions
- Sizing the cluster and planning for growth
- Selecting hardware and network infrastructure
- Rack topology
- Installation procedures
- Multi-tenancy
- Directory structure and logs
- Benchmarking
- Labs: Cluster installation and running performance benchmarks
-
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: Familiarising yourself 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 to SQL
- Hadoop data warehousing with Hive
- Copying data between clusters using distcp
- Using S3 as a complement to HDFS
- Best practices and architectures for data ingestion
- Labs: Setting up and using Flume and Sqoop
-
MapReduce operations and administration
- Parallel computing before MapReduce: comparing HPC with Hadoop administration
- MapReduce cluster loads
- Nodes and Daemons (JobTracker, TaskTracker)
- Walk-through of the MapReduce UI
- MapReduce configuration
- Job configuration
- Optimising MapReduce
- Fool-proofing MapReduce: guidance for programmers
- Labs: Running MapReduce examples
-
YARN: New architecture and capabilities
- YARN design goals and implementation architecture
- New actors: ResourceManager, NodeManager, Application Master
- Installing YARN
- Job scheduling under YARN
- Labs: Investigating job scheduling
-
Advanced topics
- Hardware monitoring
- Cluster monitoring
- Adding and removing servers, and 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
-
Optional tracks
- Cloudera Manager for cluster administration, monitoring, and routine tasks; installation and usage. In this track, all exercises and labs are performed 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
- Comfortable with basic Linux system administration
- Basic scripting skills
Prior knowledge of Hadoop and Distributed Computing is not required, as these topics are introduced and explained during the course.
Lab environment
Zero Install: Students do not need to install Hadoop software on their own machines. A working Hadoop cluster will be provided.
Students will need the following:
- An SSH client (Linux and Mac systems come with SSH clients; PuTTY is recommended for Windows)
- A 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
