Nossos Clientes

Código do Curso

hadoopadm1

Duration

21 hours (usually 3 days including breaks)

Requisitos

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

O Apache Hadoop é a estrutura mais popular para processar Big Data em clusters de servidores. Neste curso de três (opcionalmente, quatro) dias, os participantes aprenderão sobre os benefícios de negócios e casos de uso do Hadoop e seu ecossistema, como planejar a implantação e o crescimento do cluster, como instalar, manter, monitorar, solucionar problemas e otimizar o Hadoop . Eles também praticarão o carregamento de dados em massa do cluster, se familiarizarão com várias distribuições do Hadoop e praticarão a instalação e o gerenciamento de ferramentas do ecossistema do Hadoop . O curso termina com a discussão de proteger o cluster com o Kerberos.

“… Os materiais foram muito bem preparados e cobertos completamente. O laboratório foi muito prestativo e bem organizado ”
- Andrew Nguyen, engenheiro principal de integração da DW, Microsoft Online Advertising

Público

Administradores do Hadoop

Formato

Palestras e laboratórios práticos, saldo aproximado de 60% de palestras, 40% de laboratórios.

Machine Translated

Programa do Curso

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)

Declaração de Clientes

★★★★★

O que você mais gostou no treinamento ?: Detalhado em cada ferramenta.

Amber Mehrotra - NIIT Limited

Curso:Administração Hadoop

Machine Translated

O fato de que todos os dados e software estavam prontos para uso em uma VM já preparada, fornecida pelo instrutor em discos externos.

vyzVoice

Curso:Hadoop for Developers and Administrators

Machine Translated

Eu gostei principalmente do treinador dando exemplos reais ao vivo.

Simon Hahn