課程代碼

hadoopadm1

課程時長

21 時間：同常來說包括休息是 3天

最低要求

comfortable with basic Linux system administration
basic scripting skills

Knowledge of Hadoop and Distributed Computing is not required, but will be introduced and explained in the course.

Lab environment

Zero Install : There is no need to install hadoop software on students’ machines! A working hadoop cluster will be provided for students.

Students will need the following

an SSH client (Linux and Mac already have ssh clients, for Windows Putty is recommended)
a browser to access the cluster. We recommend Firefox browser with FoxyProxy extension installed

概觀

Apache Hadoop是用於處理服務器群集上的Big Data的最流行的框架。在這三天（可選擇四天）課程中，與會者將了解Hadoop及其生態系統的業務優勢和用例，如何規劃群集部署和增長，如何安裝，維護，監控，排除故障和優化Hadoop 。他們還將練習集群批量數據加載，熟悉各種Hadoop發行版，並練習安裝和管理Hadoop生態系統工具。本課程結束時討論了使用Kerberos保護集群。

“......材料準備得很充分，並且要徹底覆蓋。該實驗室非常有幫助，組織良好“
- Andrew Nguyen，微軟在線Advertising首席集成DW工程師

聽眾

Hadoop管理員

格式

講座和動手實驗，大約平衡60％的講座，40％的實驗室。

Machine Translated

課程簡介

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)