ID de curs

hadoopadm1

Durata

21 ore (usually 3 days including breaks)

Cerințe

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

Sinoptic

Apache Hadoop este cel mai popular cadru pentru procesarea Big Data pe clustere de servere. În acest curs de trei (opțional, patru) zile, participanții vor afla despre beneficiile și cazurile de utilizare pentru Hadoop și ecosistemul său, cum să planifice desfășurarea și creșterea clusterului, cum să instalați, să mențineți, să monitorizați, să depanați și să optimizați Hadoop . Ei vor practica, de asemenea, încărcarea de date în grup, vor cunoaște diverse distribuții Hadoop și vor practica instalarea și gestionarea instrumentelor de ecosistem Hadoop . Cursul se încheie cu discuția despre asigurarea unui cluster cu Kerberos.

"... Materialele au fost foarte bine pregătite și acoperite cu atenție. Laboratorul a fost foarte util și bine organizat "
- Andrew Nguyen, Inginer Principal de Integrare DW, Microsoft Online Advertising

Public

Administratorii Hadoop

Format

Prelegeri și laboratoare, echilibru aproximativ 60%, 40% laboratoare.

Machine Translated

Schița de curs

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)

Mărturii

★★★★★

Ce ti-a placut cel mai mult despre antrenament ?: Detalii despre fiecare instrument.

Amber Mehrotra - NIIT Limited

Curs:Hadoop Administration

Machine Translated

Faptul că toate datele și software-ul erau gata de utilizare pe un VM deja pregătit, oferit de antrenor pe discuri externe.

vyzVoice

Curs:Hadoop for Developers and Administrators

Machine Translated

Mi-a plăcut mai mult antrenorul oferind Exemple reale live.

Simon Hahn