Course Outline:
Introduction to Big Data and Hadoop:
Overview of big data concepts and challenges
Introduction to Apache Hadoop and its ecosystem
History and evolution of Hadoop
Use cases and applications of Hadoop
Hadoop Architecture and Components:
Understanding Hadoop Distributed File System (HDFS)
Introduction to MapReduce programming model
Hadoop ecosystem components (YARN, HBase, Hive, Pig, Sqoop, Flume, Zookeeper, Oozie)
Setting Up Hadoop Cluster:
Installing and configuring Hadoop
Understanding Hadoop modes (standalone, pseudo-distributed, fully distributed)
Setting up a multi-node Hadoop cluster
Hadoop cluster management and administration
HDFS (Hadoop Distributed File System):
HDFS architecture and components (NameNode, DataNode)
File operations in HDFS (read, write, delete)
HDFS commands and shell
HDFS data replication and fault tolerance
MapReduce Programming:
Introduction to MapReduce concepts
Writing MapReduce programs (Mapper, Reducer, Driver)
MapReduce job execution and lifecycle
Advanced MapReduce features (combiner, partitioner, input/output formats)
YARN (Yet Another Resource Negotiator):
Introduction to YARN and its architecture
Resource management and scheduling in YARN
Running applications on YARN
Monitoring and managing YARN applications
Data Ingestion and ETL:
Importing data using Sqoop
Real-time data ingestion using Flume
Data transformation and processing with Pig
Data integration and ETL workflows
Data Storage and Management:
Introduction to Apache HBase
HBase architecture and data model
CRUD operations in HBase
Integrating HBase with MapReduce
Data Warehousing and Querying:
Introduction to Apache Hive
Hive architecture and components
HiveQL: Querying data with Hive
Managing and optimizing Hive tables and partitions
Advanced Hadoop Ecosystem Tools:
Apache Spark for big data processing
Real-time stream processing with Apache Kafka
Workflow scheduling and management with Apache Oozie
Data governance and cataloging with Apache Atlas
Hadoop Security:
Securing Hadoop cluster with Kerberos
Hadoop authorization and authentication
Data encryption and access control
Auditing and monitoring Hadoop security
Monitoring and Troubleshooting:
Hadoop cluster monitoring tools (Ambari, Cloudera Manager)
Performance tuning and optimization
Troubleshooting common Hadoop issues
Best practices for Hadoop cluster maintenance
Practical Applications and Case Studies:
Real-world examples and case studies
Practical exercises and projects
Best practices for deploying and managing Hadoop in production
Skills Gained:
Proficiency in setting up and managing Hadoop clusters
Ability to write and optimize MapReduce programs
Skills in data ingestion, storage, and management using Hadoop ecosystem tools
Knowledge of Hadoop security and cluster monitoring
Competence in using advanced Hadoop tools like Spark, Kafka, and Oozie
Target Audience:
Data engineers
Data analysts
Big data developers
IT professionals
Students and professionals interested in big data technologies