Hadoop Administrator Practice Exam
The Hadoop Administrator exam assesses individuals' proficiency in managing, configuring, and maintaining Apache Hadoop clusters and ecosystem components. Hadoop administrators are responsible for ensuring the reliability, performance, and security of Hadoop infrastructure to support big data processing and analytics applications. This exam evaluates candidates' knowledge of Hadoop architecture, deployment best practices, troubleshooting techniques, and cluster optimization strategies.
Skills Required
- Hadoop Architecture: Understanding of Apache Hadoop architecture, including HDFS (Hadoop Distributed File System), YARN (Yet Another Resource Negotiator), and MapReduce, and their roles in distributed data processing.
- Cluster Deployment and Configuration: Proficiency in deploying, configuring, and managing Hadoop clusters using tools like Apache Ambari, Cloudera Manager, or Hortonworks Data Platform (HDP).
- Cluster Monitoring and Management: Skills in monitoring cluster health, performance, and resource utilization using monitoring tools like Nagios, Ganglia, or Prometheus, and implementing performance tuning and optimization strategies.
- Security and Access Control: Knowledge of Hadoop security mechanisms, including Kerberos authentication, role-based access control (RBAC), and data encryption, and implementing security best practices to protect Hadoop clusters and data.
- Backup and Disaster Recovery: Familiarity with backup and disaster recovery strategies for Hadoop clusters, including data backup, replication, and recovery procedures, to ensure data integrity and availability in case of failures or disasters.
Who should take the exam?
- Hadoop Administrators: IT professionals responsible for installing, configuring, and maintaining Apache Hadoop clusters in enterprise environments.
- Big Data Engineers: Data engineers, architects, and developers working with big data platforms and analytics applications built on Apache Hadoop.
- System Administrators: System administrators and network engineers interested in expanding their skills to include Hadoop cluster administration and management.
- Data Scientists and Analysts: Data scientists, analysts, and researchers seeking to gain a deeper understanding of Hadoop infrastructure to optimize data processing and analysis workflows.
- IT Managers and Decision-Makers: IT managers, directors, and decision-makers evaluating or implementing Hadoop-based solutions in their organizations.
Course Outline
The Hadoop Administrator exam covers the following topics :-
Module 1: Introduction to Apache Hadoop
- Overview of Apache Hadoop ecosystem components, including HDFS, YARN, MapReduce, and Hadoop Common.
- Understanding the distributed computing principles and scalability benefits of Hadoop for big data processing.
Module 2: Hadoop Cluster Planning and Deployment
- Planning Hadoop cluster architecture, capacity, and scalability requirements based on workload and data volume projections.
- Deploying Hadoop clusters using automated provisioning tools like Apache Ambari, Cloudera Manager, or Hortonworks Data Platform (HDP).
Module 3: Hadoop Cluster Configuration
- Configuring Hadoop cluster components, including HDFS, YARN, MapReduce, and Hadoop ecosystem services, for optimal performance and resource utilization.
- Setting up high availability (HA), fault tolerance, and resource management policies for Hadoop clusters.
Module 4: Cluster Monitoring and Performance Tuning
- Monitoring Hadoop cluster health, performance metrics, and resource utilization using monitoring tools like Nagios, Ganglia, or Prometheus.
- Implementing performance tuning and optimization strategies to improve Hadoop cluster efficiency and throughput.
Module 5: Security and Access Control
- Configuring security mechanisms, including Kerberos authentication, SSL/TLS encryption, and role-based access control (RBAC), to secure Hadoop clusters and data.
- Implementing security best practices for securing Hadoop cluster infrastructure and mitigating security threats and vulnerabilities.
Module 6: Backup and Disaster Recovery
- Developing backup and disaster recovery strategies for Hadoop clusters to ensure data integrity and availability in case of failures or disasters.
- Implementing data backup, replication, and recovery procedures using Hadoop ecosystem tools and technologies.
Module 7: Hadoop Cluster Troubleshooting
- Identifying and troubleshooting common issues, errors, and performance bottlenecks in Hadoop clusters using log analysis, debugging tools, and diagnostic techniques.
- Implementing troubleshooting best practices to resolve Hadoop cluster issues and maintain cluster uptime and reliability.
Module 8: Cluster Upgrades and Maintenance
- Planning and executing Hadoop cluster upgrades, patching, and maintenance activities to ensure cluster stability, security, and compatibility with new releases.
- Implementing rolling upgrades and maintenance procedures to minimize downtime and service disruption during cluster maintenance windows.
Module 9: Hadoop Ecosystem Integration
- Integrating Hadoop clusters with other data processing and analytics tools and platforms, including Apache Spark, Apache Hive, Apache Pig, and Apache HBase, for end-to-end data processing workflows.
- Configuring data ingestion, integration, and data pipeline management solutions to streamline data processing and analysis tasks.
Module 10: Best Practices and Case Studies
- Reviewing best practices, use cases, and real-world examples of Hadoop cluster deployment, configuration, and management.
- Analyzing case studies and success stories of organizations leveraging Hadoop for big data analytics, data warehousing, and business intelligence initiatives.