Last updated
Kurskode
hadoopadm1
Varighet
21 timer (vanligvis 3 dag inkludert pauser)
Krav
- 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
Oversikt
Apache Hadoop er den mest populære rammen for behandling av Big Data på klynger av servere. I dette tre (eventuelt fire dager) kurset vil deltakerne lære om forretningsfordelene og bruke saker for Hadoop og dets økosystem, hvordan planlegge klyngedistribusjon og vekst, hvordan installere, vedlikeholde, overvåke, feilsøke og optimalisere Hadoop . De vil også øve på datalast av klynge, bli kjent med forskjellige Hadoop distribusjoner og øve på å installere og administrere Hadoop økosystemverktøy. Kurset avsluttes med diskusjon om å sikre klynge med Kerberos.
“… Materialene var veldig godt forberedt og dekket grundig. Laben var veldig hjelpsom og godt organisert ”
- Andrew Nguyen, hovedintegrasjons DW Engineer, Microsoft Online Advertising
Publikum
Hadoop administratorer
Format
Forelesninger og praktiske laboratorier, omtrentlig balanse 60% forelesninger, 40% laboratorier.
Machine Translated
Kursplan
- 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)
