Hadoop Administration Online Training Course Content
Understanding Big Data and Hadoop
Learning Objectives: In this module, you will understand what is Big Data and Apache Hadoop. You will also learn how Hadoop solves the Big Data problems, Hadoop Cluster Architecture, its core components & ecosystem, Hadoop data loading & reading mechanism and role of a Hadoop Cluster Administrator.
Introduction to big data
limitations of existing solutions
Hadoop components and ecosystem
data loading & reading from HDFS
rack awareness theory
Hadoop cluster administrator: Roles and responsibilities
Hadoop Architecture and Cluster setup
Learning Objectives: In this module, you will understand different Hadoop components; understand the working of HDFS, Hadoop cluster modes, configuration files, and more. You will also understand the Hadoop 2.0 cluster setup and configuration, setting up Hadoop Clients using Hadoop 2.0 and resolve problems simulated from a real-time environment.
Hadoop server roles and their usage
Hadoop installation and initial configuration
Deploying Hadoop in a pseudo-distributed mode
Deploying a multi-node Hadoop cluster
Installing Hadoop Clients
Understanding the working of HDFS and resolving simulated problems
Hadoop Cluster Administration and Understanding MapReduce
Learning Objectives: In this module you will understand the working of the secondary namenode, working with Hadoop distributed cluster, enabling rack awareness, maintenance mode of Hadoop cluster, adding or removing nodes to your cluster in an ad-hoc and recommended way, understand the MapReduce programming model in the context of Hadoop administrator and schedules.
Understanding secondary namenode
Working with Hadoop distributed cluster
Decommissioning or commissioning of nodes
Understanding schedulers and enabling them
Backup, Recovery and Maintenance
Learning Objectives: In this module, you will understand the day to day cluster administration tasks, balancing data in a cluster, protecting data by enabling trash, attempting a manual failover, creating backup within or across clusters, safeguarding your metadata and doing metadata recovery or manual failover of NameNode recovery, learn how to restrict the usage of HDFS in terms of count and volume of data, and more.
Key Hadoop Admin Commands
Import Check Point
Distcp, data backup, and recovery
Namespace count quota or space quota
Manual failover or metadata recovery
Cluster planning and management
Learning Objectives: In this module, you will gather insights around cluster planning and management; learn about the various aspects one needs to remember while planning a setup of a new cluster, capacity sizing, understanding recommendations and comparing different distributions of Hadoop, understanding workload and usage patterns and some examples from the world of big data.
Planning a Hadoop 2.0 cluster
Cluster sizing, hardware
Network and software considerations
Popular Hadoop distributions
Workload and usage patterns
Hadoop 2.0 and features
Learning Objectives: In this module, you will learn more about the new features of Hadoop 2.0, HDFS High Availability, YARN framework and job execution flow, MRv2, federation, limitations of Hadoop 1.x and setting up Hadoop 2.0 Cluster setup in pseudo-distributed and distributed mode.
Limitations of Hadoop 1.x
Features of Hadoop 2.0
Hadoop high availability and federation
YARN ecosystem and Hadoop 2.0 Cluster setup
Setting up Hadoop 2.X with highly availability and upgrading Hadoop
Learning Objectives: In this module, you will learn to setup Hadoop 2 with high availability, upgrading from v1 to v2, importing data from RDBMS into HDFS, understand why Oozie, Hive, and HBase are used and working on the components.
Configuring Hadoop 2 with high availability
upgrading to Hadoop 2
working with Sqoop
working with Hive
working with HBase
Project: Cloudera manager and Cluster setup, Overview on Kerberos
Learning Objectives: In this module, you will learn about Cloudera manager to setup Cluster, optimizations of Hadoop/Hbase/Hive performance parameters and understand the basics on Kerberos. You will learn to setup Pig to use in local/distributed mode to perform data analytics.
Pig setup and working with a grunt, why Kerberos and how it helps
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