Course Overview
Apache Hadoop is the most popular framework for processing Big Data on clusters of servers. In this course, attendees will learn about the business benefits and use cases for Hadoop and its ecosystem, how to plan cluster deployment and growth, how to install, maintain, monitor, troubleshoot, and optimize Hadoop.
What you’ll learn
During the Hadoop for Administrators course, students will learn:
- Hadoop & Big Data
- Installing Hadoop
- Managing and Monitoring Hadoop
- Loading data in HDFS
- Managing ecosystem
- Securing Hadoop
Requirements
- comfortable with basic Linux system administration
- basic scripting skills
Target audiences
- Hadoop Administrators
Curriculum
- 1 Section
- 8 Lessons
- 3 Days
Expand all sectionsCollapse all sections
- Topics8
- 1.1Introduction Hadoop history, concepts Ecosystem Distributions High-level architecture Hadoop myths Hadoop challenges (hardware/software) Labs: discuss your Big Data projects and problems
- 1.2Planning and installation Selecting software, Hadoop distributions Sizing the cluster, planning for growth Selecting hardware and network Rack topology Installation Multi-tenancy The directory structure, logs Benchmarking Labs: cluster install, run performance benchmarks
- 1.3HDFS 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
- 1.4Data 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
- 1.5MapReduce operations and administration Parallel computing before MapReduce: compare HPC vs Hadoop administration MapReduce cluster loads Nodes and Daemons (JobTracker, TaskTracker) MapReduce UI walkthrough Mapreduce configuration Job config Optimizing MapReduce Fool-proofing MR: what to tell your programmers Labs: running MapReduce examples
- 1.6YARN: 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
- 1.7Advanced 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
- 1.8Optional 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)
Instructor
