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Administrator Training for Apache Hadoop

Administrator Training for Apache Hadoop

Master the core techniques and concepts of Hadoop administration in this comprehensive training. Delve deep into HDFS, YARN, cluster planning, and installation.

Learn the art of effective resource management and master the intricacies of monitoring and logging. Prepare to administer powerful, scalable Hadoop clusters and harness their potential.

What will you learn?

This Apache Hadoop Administrator Training course is your gateway to mastering the ins and outs of the world's most popular distributed data-handling platform. After completing the course, participants will:

  • Understand HDFS: Dive into the core daemons and operational capabilities of the Hadoop File System.
  • Get Acquainted with YARN and MRv2: Upgrade and transition smoothly from Hadoop 1 to Hadoop 2.
  • Plan Your Hadoop Cluster: Choose the best hardware, OS, and network topology tailored for your needs.
  • Install and Administer Your Cluster: Know the tools and techniques to keep your cluster in prime condition.
  • Optimize Resource Management: Learn about the FIFO, Fair, and Capacity Schedulers.
  • Master Monitoring and Logging: Utilize Hadoop’s metrics, Web UIs, and log files to ensure your cluster’s health.

Requirements:

Basic IT knowledge: Familiarity with operating systems, hardware configurations, and basic network operations.

Optional: Previous experience with distributed systems will be beneficial but not mandatory.

Course Outline*:

*We know each team has their own needs and specifications. That is why we can modify the training outline per need.

HDFS – The Heart of Hadoop
  • Introduction to HDFS: Understanding its foundational role in Hadoop.
  • Daemons of HDFS: Deep dive into NameNode, DataNode, and SecondaryNameNode and their responsibilities.
  • Hadoop Cluster Operation: How data is stored and processed effectively.
  • Hadoop's Evolution: Why modern computing systems necessitate platforms like Hadoop.
  • Design Principles of HDFS: Emphasizing reliability, scalability, and fault-tolerance.
  • Exploring HDFS Federation: Enhancing namespace isolation and scalability.
  • High Availability with HDFS HA-Quorum: Ensuring data durability and cluster availability.
  • Securing HDFS: Introduction to Kerberos-based authentication.
  • Serialization in Hadoop: Optimal data serialization choices for different scenarios.
  • Hands-on with Hadoop File System Shell: Commands to manipulate and manage files.
YARN and MapReduce version 2 (MRv2) – Powering Processing
  • Transitioning between Versions: Key differences between Hadoop 1 and Hadoop 2.
  • Deploying YARN: Setting up the next-generation Hadoop computational framework.
  • Designing with MRv2: Strategies to optimize data processing tasks.
  • Inside YARN Resource Allocation: How resources are dynamically allocated and managed.
  • MapReduce on YARN: Comprehensive breakdown of a job's lifecycle.
  • Migration Guidelines: Ensuring smooth transitions from MRv1 to MRv2.
Strategic Hadoop Cluster Planning
  • Optimal Hardware Selection: Understanding server specifications for different Hadoop workloads.
  • Choosing the Right OS: Recommendations for stability and performance.
  • Tuning for Performance: Kernel adjustments for optimized operations.
  • Workload Analysis: Determining hardware and software needs based on workload patterns.
  • Diverse Ecosystem: An overview of complementary components enhancing Hadoop.
  • Storage Considerations: JBOD vs. RAID, disk sizing, and more.
  • Network Planning for Hadoop: Ensuring bandwidth and fault tolerance.
Hands-on Cluster Installation and Administration
  • Ensuring Fault Tolerance: Techniques to ensure uptime during failures.
  • Logging Mechanisms in Hadoop: Setting up, reading, and analyzing logs.
  • Hadoop Health Check: Tools and strategies to monitor cluster health.
  • Cluster Management Tools: An introduction to platforms like Ambari.
  • Ecosystem on CDH 5: Setting up components like Impala, Flume, and Hive.
Resource Management – Maximizing Efficiency
  • Overview of Hadoop Schedulers: Understanding their role in resource allocation.
  • FIFO Scheduler Deep Dive: How it allocates cluster resources sequentially.
  • Fair and Capacity Schedulers: Ensuring efficient and priority-based resource allocation.
Monitoring, Logging, and Troubleshooting
  • Metrics in Hadoop: Leveraging built-in tools for performance insights.
  • Web UIs for Monitoring: Navigating and interpreting the NameNode and JobTracker interfaces.
  • Daemon Monitoring: Tools and techniques to ensure daemon health.
  • CPU and Memory Health: Monitoring and optimization techniques.
  • Logs Deciphered: Reading, managing, and deriving insights from Hadoop logs.

Hands-on learning with expert instructors at your location for organizations.

0
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Level: 
Intermediate
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Duration: 
35
Hours (days:
5
) 
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Training customized to your needs
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Immersive hands-on experience in a dedicated setting
*Price can range depending on number of participants, change of outline, location etc.
Get Quote

Master new skills guided by experienced instructors from anywhere.

0
Graph Icon - Education X Webflow Template
Level: 
Intermediate
Clock Icon - Education X Webflow Template
Duration: 
35
Hours (days:
5
) 
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Training customized to your needs
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Reduced training costs
*Price can range depending on number of participants, change of outline, location etc.
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You can participate in a Public Course with people from other organisations.

0

/per trainee

Number of Participants

1 Participant

Thanks for the numbers, they could be going to your emails. But they're going to mine... Thanks ;D
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Graph Icon - Education X Webflow Template
Level: 
Intermediate
Clock Icon - Education X Webflow Template
Duration: 
35
Hours (days:
5
) 
Camera Icon - Education X Webflow Template
Fits ideally for individuals and small groups
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Networking opportunities with fellow participants.
*Price can range depending on number of participants, change of outline, location etc.
Get Quote