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Big Data Hadoop Admin

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Big Data Hadoop Admin

Big Data Hadoop Admin

Duration
60 Hours

Course Description

                A Big Data Hadoop Administrator manages and maintains Hadoop clusters and related resources in a production environment. This includes tasks like installing, configuring, monitoring, securing, and troubleshooting Hadoop clusters and their ecosystem components like Hive, Pig, and HBase. They ensure the availability, performance, and security of the cluster, enabling organizations to effectively utilize big data. 

Course Outline For Big Data Hadoop Admin

1. Big data and Hadoop basics

  • Introduction to Big Data: Understanding the characteristics of Big Data (volume, velocity, variety, veracity), challenges it poses, and the limitations of traditional databases.
  • Introduction to Hadoop: Explaining Hadoop as an open-source framework for distributed storage and processing of large datasets on clusters of commodity hardware.
  • Hadoop Ecosystem Components: Deep diving into the key components:
    • HDFS (Hadoop Distributed File System): Understanding its architecture (NameNode, DataNode), distributed storage concepts, and how it handles file blocks.
    • YARN (Yet Another Resource Negotiator): Learning about its role in resource management and job scheduling in the Hadoop cluster.
    • MapReduce: Exploring the programming model for parallel data processing.
    • Other important tools like Hive, Pig, HBase, Sqoop, Flume, Spark, Kafka, and Zookeeper, their functionalities, and how they integrate within the Hadoop ecosystem. 

2. Hadoop cluster setup and configuration

  • Cluster Planning and Sizing: Determining the appropriate number of nodes, hardware specifications (RAM, CPU, disk space), and network requirements for a Hadoop cluster based on workload and usage patterns.
  • Installation and Deployment: Learning to install and configure Hadoop in different modes (pseudo-distributed, multi-node) on platforms like Linux or Windows and potentially cloud environments like Amazon EC2.
  • Hadoop Configuration Files: Understanding and modifying the core configuration files (core-site.xml, hdfs-site.xml, yarn-site.xml, mapred-site.xml) to tune performance and functionality.
  • OS Tuning for Hadoop Performance: Implementing operating system level optimizations for better Hadoop cluster performance. 

3. Hadoop cluster administration and maintenance

  • HDFS Operations: Managing HDFS commands, File System Checks (FSCK), High Availability (HA) features like NameNode Federation and ResourceManager HA, and decommissioning and recommissioning DataNodes.
  • YARN Management: Monitoring NodeManagers, understanding YARN schedulers (Capacity Scheduler, Fair Scheduler), and managing resource allocation for applications and jobs.
  • Backup and Recovery: Implementing strategies for data backup and recovery using tools like HDFS snapshots, distcp, and metadata backups to NFS mounts.
  • Troubleshooting: Diagnosing and resolving common issues and errors encountered in a Hadoop cluster, including analyzing log files and using monitoring tools.
  • Upgrades and Patching: Keeping the Hadoop cluster updated with the latest versions and patches to ensure security and optimal performance. 

4. Hadoop security

  • Hadoop Security Concepts: Understanding authentication, authorization, data encryption, and auditing within the Hadoop ecosystem.
  • Kerberos Authentication: Implementing and configuring Kerberos for authentication between users, services, and applications in the cluster.
  • Apache Ranger: Using Apache Ranger for centralized authorization management, including role-based access control (RBAC) and auditing across different Hadoop components.
  • Data Encryption: Implementing data encryption at rest (Transparent Data Encryption for HDFS) and in transit (SSL/TLS).
  • HDFS ACLs: Configuring HDFS Access Control Lists to manage permissions for files and directories. 

5. Hadoop monitoring and performance tuning

  • Cluster Monitoring Tools: Utilizing tools like Apache Ambari, Cloudera Manager, Ganglia, Prometheus, and Nagios to monitor the health and performance of the Hadoop cluster.
  • Monitoring Metrics: Tracking key metrics for HDFS (capacity, usage, block status), MapReduce (job progress, task completion, resource utilization), YARN (cluster and application metrics), and ZooKeeper.
  • Log Analysis: Collecting and analyzing logs for troubleshooting and performance optimization, .
  • Performance Tuning Techniques: Applying various methods to optimize cluster performance, including:
    • Memory Tuning and Disk IO optimization
    • Mapper and Reducer task tuning
    • Using Combiners and Partitioners
    • Addressing data skew
    • Speculative execution
    • Choosing appropriate HDFS block size and replication factors
    • Data compression (LZO, BZIP, Snappy) 

6. Hadoop ecosystem component administration

  • Installation and Configuration: Installing and configuring Hadoop ecosystem components like Hive, HBase, Sqoop, Flume, and Oozie.
  • HBase Administration: Managing HBase operations (DDL, DML) and understanding its data model.
  • Hive Administration: Managing Hive metastore, configurations, and optimizing query performance.
  • Oozie Administration: Setting up and managing Oozie workflows for scheduling Hadoop jobs.
  • Data Ingestion and Export: Utilizing Sqoop for moving data between Hadoop and relational databases and Flume for log data ingestion. 
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