Kurs Code

hadoopadm1

Dauer

21 hours (üblicherweise 3 Tage inklusive Pausen)

Voraussetzungen

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

Überblick

Apache Hadoop ist das beliebteste Framework für die Verarbeitung von Big Data auf Serverclustern. In diesem dreitägigen (optionalen) Kurs lernen die Teilnehmer die Geschäftsvorteile und Anwendungsfälle für Hadoop und sein Ökosystem kennen, wie Clusterbereitstellung und -wachstum geplant werden, wie Hadoop installiert, gewartet, überwacht, Fehler behoben und optimiert wird. Sie üben auch das Laden von Cluster-Massendaten, machen sich mit verschiedenen Hadoop Distributionen vertraut und üben das Installieren und Verwalten von Hadoop Ökosystemtools. Der Kurs endet mit einer Diskussion über das Sichern von Clustern mit Kerberos.

„… Die Materialien waren sehr gut vorbereitet und gründlich abgedeckt. Das Labor war sehr hilfreich und gut organisiert. “
- Andrew Nguyen, Hauptintegrations-DW-Ingenieur, Microsoft Online Advertising

Publikum

Hadoop Administratoren

Format

Vorlesungen und praktische Übungen, ungefähr 60% Vorlesungen, 40% Übungen.

Machine Translated

Schulungsübersicht

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)

Erfahrungsberichte

★★★★★

Was hat Ihnen an dem Training am meisten gefallen ?: Detailliert zu jedem Tool.

Amber Mehrotra - NIIT Limited

Kurs:Hadoop Administration

Machine Translated

Die Tatsache, dass alle Daten und Software auf einer bereits vorbereiteten VM bereit waren, die vom Trainer auf externen Festplatten bereitgestellt wurde.

vyzVoice

Kurs:Hadoop for Developers and Administrators

Machine Translated

Ich mochte den Trainer am liebsten, wenn er reale Live-Beispiele gab.

Simon Hahn