Codice del corso

hadoopadm1

Durata

21 ore (generalmente 3 giorni pause incluse)

Requisiti

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

Overview

Apache Hadoop è il framework più popolare per l'elaborazione di Big Data su cluster di server. In questo corso di tre (facoltativamente, quattro) giorni, i partecipanti apprenderanno i vantaggi aziendali e i casi d'uso per Hadoop e il suo ecosistema, come pianificare la distribuzione e la crescita dei cluster, come installare, gestire, monitorare, risolvere i problemi e ottimizzare Hadoop . Eserciteranno inoltre il caricamento di dati in blocco dei cluster, acquisiranno familiarità con varie distribuzioni Hadoop e si eserciteranno nell'installazione e nella gestione degli strumenti dell'ecosistema Hadoop . Il corso termina con la discussione sulla protezione del cluster con Kerberos.

“… I materiali sono stati preparati molto bene e sono stati accuratamente coperti. Il laboratorio è stato molto utile e ben organizzato ”
- Andrew Nguyen, ingegnere principale di integrazione DW, Microsoft Online Advertising

Pubblico

Amministratori di Hadoop

Formato

Lezioni frontali e esercitazioni pratiche, bilancio approssimativo 60% lezioni frontali, 40% laboratori.

Machine Translated

Struttura del corso

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)

Recensioni

★★★★★

Cosa ti è piaciuto di più della formazione ?: Dettagliato su ogni strumento.

Amber Mehrotra - NIIT Limited

Corso:Hadoop Administration

Machine Translated

Il fatto che tutti i dati e il software fossero pronti per l'uso su una VM già preparata, fornita dal trainer in dischi esterni.

vyzVoice

Corso:Hadoop for Developers and Administrators

Machine Translated

Mi è piaciuto soprattutto l'allenatore dando esempi reali dal vivo.

Simon Hahn