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Data Science Hadoop Operations E-learning
Hadoop Operations E-learning
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Hadoop Operations E-learning

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Hadoop Operations E-learning

Order this unique E-learning Hadoop Operations course online, 1 year 24/7 access to rich interactive videos, voice, progress monitoring through reports and tests per chapter to directly test the knowledge.

Designing Hadoop Clusters

  • start the course
  • describe the principles of supercomputing
  • recall the roles and skills needed for the Hadoop engineering team
  • recall the advantages and shortcomings of using Hadoop as a supercomputing platform
  • describe the three axioms of supercomputing
  • describe the dumb hardware and smart software, and the share nothing design principles
  • describe the design principles for move processing not data, embrace failure, and build applications not infrastructure
  • describe the different rack architectures for Hadoop.
  • describe the best practices for scaling a Hadoop cluster.
  • recall the best practices for different types of network clusters
  • recall the primary responsibilities for the master, data, and edge servers
  • recall some of the recommendations for a master server and edge server
  • recall some of the recommendations for a data server
  • recall some of the recommendations for an operating system
  • recall some of the recommendations for hostnames and DNS entries
  • describe the recommendations for HDD
  • calculate the correct number of disks required for a storage solution
  • compare the use of commodity hardware with enterprise disks
  • plan for the development of a Hadoop cluster
  • set up flash drives as boot media
  • set up a kickstart file as boot media
  • set up a network installer
  • identify the hardware and networking recommendations for a Hadoop cluster

Hadoop in the Cloud

  • start the course
  • describe how cloud computing can be used as a solution for Hadoop
  • recall some of the most come services of the EC2 service bundle
  • recall some of the most common services that Amazon offers
  • describe how the AWS credentials are used for authentication
  • create an AWS account
  • describe the use of AWS access keys
  • describe AWS identification and access management
  • set up AWS IAM
  • describe the use of SSH key pairs for remote access
  • set up S3 and import data
  • provision a micro instance of EC2
  • prepare to install and configure a Hadoop cluster on AWS
  • create an EC2 baseline server
  • create an Amazon machine image
  • create an Amazon cluster
  • describe what the command line interface is used for
  • use the command line interface
  • describe the various ways to move data into AWS
  • recall the advantages and limitations of using Hadoop in the cloud
  • recall the advantages and limitations of using AWS EMR
  • describe EMR End-user connections and EMR security levels
  • set up an EMR cluster
  • run an EMR job from the web console
  • run an EMR job with Hue
  • run an EMR job with the command line interface
  • write an Elastic MapReduce script for AWS

Deploying Hadoop Clusters

  • start the course
  • describe the configurations management tools
  • simulate a configuration management tool
  • build an image for a baseline server
  • build an image for a DataServer
  • build an image for a master server
  • provision an admin server
  • describe the layout and structure of the Hadoop cluster
  • provision a Hadoop cluster
  • distribute configuration files and admin scripts
  • use init scripts to start and stop a Hadoop cluster
  • configure a Hadoop cluster
  • configure logging for the Hadoop cluster
  • build images for required servers in the Hadoop cluster
  • configure a MySQL database
  • build the Hadoop clients
  • configure Hive daemons
  • test the functionality of Flume, Sqoop, HDFS, and MapReduce
  • test the functionality of Hive and Pig
  • configure Hcatalog daemons
  • configure Oozie
  • configure Hue and Hue users
  • install Hadoop on to the admin server

Hadoop Cluster Availability

  • start the course
  • describe how Hadoop leverages fault tolerance
  • recall the most common causes for NameNode failure
  • recall the uses for the Checkpoint node
  • test the availability for the NameNode
  • describe the operation of the NameNode during a recovery
  • swap to a new NameNode
  • recall the most common causes for DataNode failure
  • test the availability for the DataNode
  • describe the operation of the DataNode during a recovery
  • set up the DataNode for replication
  • identify and recover from a missing data block scenario
  • describe the functions of Hadoop high availability
  • edit the Hadoop configuration files for high availability
  • set up a high availability solution for NameNode
  • recall the requirements for enabling an automated failover for the NameNode
  • create an automated failover for the NameNode
  • recall the most common causes for YARN task failure
  • describe the functions of YARN containers
  • test YARN container reliability
  • recall the most common causes of YARN job failure
  • test application reliability
  • describe the system view of the Resource Manager configurations set for high availability
  • set up high availability for the Resource Manager
  • move the Resource Manager HA to alternate master servers

Securing Hadoop Clusters

  • start the course
  • describe the four pillars of the Hadoop security model
  • recall the ports required for Hadoop and how network gateways are used
  • install security groups for AWS
  • describe Kerberos and recall some of the common commands
  • diagram Kerberos and label the primary components
  • prepare for a Kerberos installation
  • install Kerberos
  • configure Kerberos
  • describe how to configure HDFS and YARN for use with Kerberos
  • configure HDFS for Kerberos
  • configure YARN for Kerberos
  • describe how to configure Hive for use with Kerberos
  • configure Hive for Kerberos
  • describe how to configure Pig, Sqoop, and Oozie for use with Kerberos
  • configure Pig and HTTPFS for use with Kerberos
  • configure Oozie for use with Kerberos
  • configure Hue for use with Kerberos
  • describe how to configure Flume for use with Kerberos
  • describe the security model for users on a Hadoop cluster
  • describe the use of POSIX and ACL for managing user access
  • create access control lists
  • describe how to encrypt data in motion for Hadoop, Sqoop, and Flume
  • encrypt data in motion
  • describe how to encrypt data at rest
  • recall the primary security threats faced by the Hadoop cluster
  • describe how to monitor Hadoop security
  • configure Hbase for Kerberos

Operating Hadoop Clusters

  • start the course
  • monitor and improve service levels
  • deploy a Hadoop release
  • describe the purpose of change management
  • describe rack awareness
  • write configuration files for rack awareness
  • start and stop a Hadoop cluster
  • write init scripts for Hadoop
  • describe the tools fsck and dfsadmin
  • use fsck to check the HDFS file system
  • set quotas for the HDFS file system
  • install and configure trash
  • manage an HDFS DataNode
  • use include and exclude files to replace a DataNode
  • describe the operations for scaling a Hadoop cluster
  • add a DataNode to a Hadoop cluster
  • describe the process for balancing a Hadoop cluster
  • balance a Hadoop cluster
  • describe the operations involved for backing up data
  • use distcp to copy data from one cluster to another
  • describe MapReduce job management on a Hadoop cluster
  • perform MapReduce job management on a Hadoop cluster
  • plan an upgrade of a Hadoop cluster

Stabilizing Hadoop Clusters

  • start the course
  • describe the importance of event management
  • describe the importance of incident management
  • describe the different methodologies used for root cause analysis
  • recall what Ganglia is and what it can be used for
  • recall how Ganglia monitors Hadoop clusters
  • install Ganglia
  • describe Hadoop Metrics2
  • install Hadoop Metrics2 for Ganglia
  • describe how to use Ganglia to monitor a Hadoop cluster
  • use Ganglia to monitor a Hadoop cluster
  • recall what Nagios is and what it can be used for
  • install Nagios
  • manage Nagios contact records
  • manage Nagios Push
  • use Nagios commands
  • use Nagios to monitor a Hadoop cluster
  • use Hadoop Metrics2 for Nagios
  • describe how to manage logging levels
  • describe how to configure Hadoop jobs for logging
  • describe how to configure log4j for Hadoop
  • describe how to configure JogHistoryServer logs
  • configure Hadoop logs
  • describe the problem management lifecycle
  • recall some of the best practices for problem management
  • describe the categories of errors for a Hadoop cluster
  • conduct a root cause analysis on a major problem
  • use different monitoring tools to identify problems, failures, errors and solutions

Capacity Management for Hadoop Clusters

  • start the course
  • compare the differences of availability versus performance
  • describe different strategies of resource capacity management
  • describe how schedulers perform various resource management
  • set quotas for the HDFS file system
  • recall how to set the maximum and minimum memory allocations per container
  • describe how the fair scheduling method allows all applications to get equal amounts of resource time
  • describe the primary algorithm and the configuration files for the Fair Scheduler
  • describe the default behavior of the Fair Scheduler methods
  • monitor the behavior of Fair Share
  • describe the policy for single resource fairness
  • describe how resources are distributed over the total capacity
  • identify different configuration options for single resource fairness
  • configure single resource fairness
  • describe the minimum share function of the Fair Scheduler
  • configure minimum share on the Fair Scheduler
  • describe the preemption functions of the Fair Scheduler
  • configure preemption for the Fair Scheduler
  • describe dominant resource fairness
  • write service levels for performance
  • use the fail scheduler with multiple users

Performance Tuning of Hadoop Clusters

  • start the course
  • recall the three main functions of service capacity
  • describe different strategies of performance tuning
  • list some of the best practices for network tuning
  • install compression
  • describe the configuration files and parameters used in performance tuning of the operating system
  • describe the purpose of Java tuning
  • recall some of the rules for tuning the datanode
  • describe the configuration files and parameters used in performance tuning of memory for daemons
  • describe the purpose of memory tuning for YARN
  • recall why the Node Manager kills containers
  • performance tune memory for the Hadoop cluster
  • describe the configuration files and parameters used in performance tuning of HDFS
  • describe the sizing and balancing of the HDFS data blocks
  • describe the use of TestDFSIO
  • performance tune HDFS
  • describe the configuration files and parameters used in performance tuning of YARN
  • configure Speculative execution
  • describe the configuration files and parameters used in performance tuning of MapReduce
  • tune up MapReduce for performance reasons
  • describe the practice of benchmarking on a Hadoop cluster
  • describe the different tools used for benchmarking a cluster
  • perform a benchmark of a Hadoop cluster
  • describe the purpose of application modeling
  • optimize memory and benchmark a Hadoop cluster

Cloudera Manager and Hadoop Clusters

  • start the course
  • describe what cluster management entails and recall some of the tools that can be used
  • describe different tools from a functional perspective
  • describe the purpose and functionality of Cloudera Manager
  • install Cloudera Manager
  • use Cloudera Manager to deploy a cluster
  • use Cloudera Manager to install Hadoop
  • describe the different parts of the Cloudera Manager Admin Console
  • describe the Cloudera Manager internal architecture
  • use Cloudera Manager to manage a cluster
  • manage Cloudera Manager's services
  • manage hosts with Cloudera Manager
  • set up Cloudera Manager for high availability
  • user Cloudera Manager to manage resources
  • use Cloudera Manager's monitoring features
  • manage logs through Cloudera Manager
  • improve cluster performance with Cloudera Manager
  • install and configure Impala
  • install and configure Sentry
  • implement security administration using Hive
  • perform backups, snapshots, and upgrades using Cloudera Manager
  • configure Hue with My SQL
  • import data using Hue
  • use Hue to run a Hive job
  • use Hue to edit Oozie workflows and coordinators
  • format HDFS, create an HDFS directory, import data, run a WordCount, and view the results
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Specifications
General properties
Availabilty: 42 hours
Language: English
Certificate of participation: Yes
Online access: 90 days
Progress monitoring: Yes
Award Winning E-learning: Yes
Suitable for mobile: Yes
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