Big Data Analytics

Session 1: Understanding Big Data (1 hour)

·       Definition of Big Data

·       Characteristics of Big Data (Volume, Velocity, Variety, Veracity, and Value)

·       Historical context and evolution

Session 2: Big Data Technologies and Ecosystem (1.5 hours)

·       Overview of Hadoop and MapReduce

·       Introduction to Apache Spark

·       Other key components in the Big Data ecosystem (e.g., HDFS, Hive, Pig)

Session 3: Data Ingestion and Processing (0.5 hours)

·       Sources of Big Data

·       Data ingestion techniques

·       Basics of data processing

Day 2: Data Storage and Management (3 hours)

Session 1: NoSQL Databases (1 hour)

·       Introduction to NoSQL databases

·       Types of NoSQL databases (e.g., MongoDB, Cassandra)

·       Use cases for NoSQL databases

Session 2: Hadoop Distributed File System (HDFS) (1.5 hours)

·       Overview of HDFS

·       Data storage in HDFS

·       Replication and fault tolerance

Session 3: Data Management and Quality (0.5 hours)

·       Data governance and metadata management

·       Maintaining data quality in Big Data environments

Day 3: Big Data Analytics (2.5 hours)

Session 1: Introduction to Big Data Analytics (1 hour)

·       Overview of Big Data analytics

·       Types of analytics (descriptive, predictive, prescriptive)

·       Use cases for Big Data analytics

Session 2: Machine Learning with Big Data (1 hour)

·       Integrating machine learning with Big Data

·       Algorithms for Big Data analytics

·       Practical applications and case studies

Session 3: Real-time Analytics and Streaming Data (0.5 hours)

·       Understanding real-time analytics

·       Tools for streaming data processing (e.g., Apache Flink, Apache Kafka)

Day 4: Big Data Security and Future Trends (2.5 hours)

Session 1: Security in Big Data Environments (1 hour)

·       Challenges and considerations for Big Data security

·       Role of encryption and access controls

Session 2: Scalability and Performance Optimization (1 hour)

·       Strategies for scaling Big Data systems

·       Performance optimization techniques

Session 3: Future Trends in Big Data (0.5 hours)

·       Edge computing and Big Data

·       Emerging technologies and trends

·       Discussion on the future of Big Data

This curriculum is designed to provide a comprehensive overview of Big Data concepts, technologies, and applications. Adjustments can be made based on the audience’s prior knowledge and the specific focus of the course.