Big Data Hadoop Administration Certification Training Course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
Overview: This comprehensive course provides hands-on training in Hadoop administration, covering deployment, security, high availability, and performance optimization of enterprise-grade Big Data clusters. Designed for beginners with Linux familiarity, it spans 8 modules over approximately 56 hours of structured learning and lab work. Each module combines theoretical concepts with practical exercises using industry-standard tools like Ambari, Ranger, and ZooKeeper. Lifetime access ensures ongoing reference and skill development.
Module 1: Hadoop Architecture & Setup
Estimated time: 7 hours
- Hadoop ecosystem overview
- Node roles and architecture components
- Install Java and Hadoop prerequisites
- Configure single-node and pseudo-distributed clusters
Module 2: HDFS Administration
Estimated time: 7 hours
- HDFS commands
- Block replication
- Storage policies and quotas
- Create directories and files, simulate DataNode failure, and verify automatic replication
Module 3: YARN & MapReduce Management
Estimated time: 7 hours
- YARN ResourceManager/NodeManager
- Application lifecycles
- MapReduce job monitoring
- Submit and monitor MapReduce jobs; tune memory and container settings
Module 4: Ecosystem Component Administration
Estimated time: 7 hours
- Hive metastore setup
- HBase schema design
- Oozie workflows, Sqoop imports/exports, Flume agents
- Deploy and configure Hive, create HBase tables, schedule an Oozie workflow, and ingest data with Flume/Sqoop
Module 5: High Availability & Federation
Estimated time: 7 hours
- NameNode HA with ZooKeeper
- ResourceManager HA
- HDFS federation architecture
- Configure a two-NameNode HA cluster and test failover; set up multiple namespaces with federation
Module 6: Security & Access Control
Estimated time: 7 hours
- Kerberos fundamentals
- HDFS ACLs
- Ranger/Knox integration, SSL encryption
- Secure the cluster with Kerberos, define HDFS ACLs, and apply Ranger policies for Hive access
Module 7: Cluster Monitoring & Performance Tuning
Estimated time: 7 hours
- Metrics collection (Ambari/Grafana)
- Log analysis
- JVM tuning, network/file system optimization
- Set up Ambari dashboards, analyze slow jobs, and apply tuning knobs for HDFS and YARN
Module 8: Backup, Recovery & Disaster Planning
Estimated time: 7 hours
- HDFS snapshots
- Metadata backup
- Rolling upgrades, cluster rollback
- Create and restore HDFS snapshots; simulate upgrade and perform rollback
Prerequisites
- Familiarity with Linux system administration
- Basic understanding of command-line operations
- Working knowledge of networking and file systems
What You'll Be Able to Do After
- Install, configure, and manage Hadoop clusters (HDFS, YARN, MapReduce) on Linux
- Administer Hadoop ecosystem components including Hive, HBase, Oozie, Sqoop, and Flume
- Monitor cluster health, tune performance, and troubleshoot common issues
- Secure Hadoop deployments using Kerberos, HDFS ACLs, and Ranger policies
- Implement high availability, federation, and disaster recovery strategies