   Hadoop For Administrators培训

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)