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Big Data and Hadoop Administrator Course Description
A training in Big Data and Hadoop Administrator will help prepare you for the demands of the industry. New innovations in technology have made it mandatory for IT professionals to be on par with the latest developments. A Hadoop Administrator training will ensure that there is no skill gap between what you know and what the industry wants, thus making you a valuable employee. Furthermore, the demand for data analysts has seen a meteoric rise in the past few years, thus making certified Hadoop Administrators a niche resource.
Who should go for this training?
Hadoop Developers, Admins and Architects
IT Managers, Support Engineers and QA Professionals
Requirements
Computer or laptop or Smartphone with Highspeed Internet Connection
No prerequisites are required for taking up this training. Though, having a basic knowledge of Linux can help.
Big Data and Hadoop Administrator Course Syllabus
- Introduction to big data
- Common big data domain scenarios
- Limitations of traditional solutions
- What is Hadoop?
- Hadoop 1.0 ecosystem and its Core Components
- Hadoop 2.x ecosystem and its Core Components
- Application submission in YARN
- Distributed File System
- Hadoop Cluster Architecture
- Replication rules
- Hadoop Cluster Modes
- Rack awareness theory
- Hadoop cluster administrator responsibilities
- Understand working of HDFS
- NTP server
- Initial configuration required before installing Hadoop
- Deploying Hadoop in a pseudo-distributed mode
- OS Tuning for Hadoop Performance
- Pre-requisite for installing Hadoop
- Hadoop Configuration Files
- Stale Configuration
- RPC and HTTP Server Properties
- Properties of Namenode, Datanode and Secondary Namenode
- Log Files in Hadoop
- Deploying a multi-node Hadoop cluster
- Question-Answer Session
- Commisioning and Decommissioning of Node
- HDFS Balancer
- Namenode Federation in Hadoop
- High Availabilty in Hadoop
- .Trash Functionality
- Checkpointing in Hadoop
- Distcp and Disk balancer
- Different Processing Frameworks
- Different phases in Mapreduce
- Spark and its Features
- Application Workflow in YARN
- YARN Metrics
- YARN Capacity Scheduler and Fair Scheduler
- Service Level Authorization (SLA)
- Different Processing Frameworks
- Different phases in Mapreduce
- Spark and its Features
- Application Workflow in YARN
- YARN Metrics
- YARN Capacity Scheduler and Fair Scheduler
- Service Level Authorization (SLA)
- Planning a Hadoop 2.x cluster
- Cluster sizing
- Hardware, Network and Software considerations
- Popular Hadoop distributions
- Workload and usage patterns
- Industry recommendations
- Monitoring Hadoop Clusters
- Hadoop Security System Concepts
- Securing a Hadoop Cluster With Kerberos
- Common Misconfigurations
- Overview on Kerberos
- Checking log files to understand Hadoop clusters for troubleshooting
- Visualize Cloudera Manager
- Features of Cloudera Manager
- Build Cloudera Hadoop cluster using CDH
- Installation choices in Cloudera
- Cloudera Manager Vocabulary
- Cloudera terminologies
- Different tabs in Cloudera Manager
- What is HUE?
- Hue Architecture, Interface and Features
- Explain Hive
- Hive Setup and Configuration
- Working with Hive
- Setting Hive in local and remote metastore mode
- Pig setup
- Working with Pig
- What is NoSQL Database
- HBase data model and Architecture
- MemStore, WAL, BlockCache
- HBase Hfile
- Compactions
- HBase Read and Write
- HBase balancer and hbck
- HBase setup
- Working with HBase
- Installing Zookeeper
- Types of Data Ingestion
- HDFS data loading commands
- Purpose and features of Sqoop
- Perform operations like, Sqoop Import, Export and Hive Import
- Sqoop 2
- Install Sqoop
- Import data from RDBMS into HDFS
- Flume features and architecture
- Types of flow
- Install Flume
- Ingest Data From External Sources With Flume
- Flume Importing Data
- Question-Answer Session
- Knowledge Check
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