Executive Development Programme in Big Data Streaming and Event Processing

This course is designed for senior executives and business leaders seeking to understand and leverage big data streaming and event processing technologies. Participants will gain strategic insights into selecting and implementing these technologies to enhance business agility and decision-making.

Upon completion, attendees will be equipped with the knowledge to evaluate and integrate big data streaming platforms, understand real-time analytics, and develop strategies for operational efficiency and competitive advantage.

1. 1. Introduction to Big Data Streaming and Event Processing: Learners will study the basics of big data streaming and event processing, including key concepts, terminologies, and architectures. They will gain foundational knowledge and practical skills to understand and implement basic streaming and event processing systems.
2. 2. Data Streaming Architectures and Systems: This module focuses on various data streaming architectures and systems such as Apache Kafka, Apache Pulsar, and Amazon Kinesis. Learners will gain hands-on experience in setting up, configuring, and deploying streaming systems.
3. 3. Real-Time Data Processing with Apache Flink: Learners will explore Apache Flink, a powerful framework for both batch and real-time data processing. They will learn to write and optimize real-time data processing applications and understand Flink’s streaming capabilities and state management.
4. 4. Event-driven Architecture and Design Patterns: This module introduces learners to event-driven architectures and common design patterns used in event processing. They will learn how to design and implement event-driven systems that can handle and process high volumes of events efficiently.
5. 5. Stream Processing with Apache Spark Streaming: Learners will delve into Apache Spark Streaming, learning to process streams of data in Spark applications. They will gain skills in setting up Spark Streaming jobs and optimizing them for real-time data processing.
6. 6. Advanced Topics in Event Processing: This module covers advanced topics such as event correlation, anomaly detection, and complex event processing (CEP). Learners will learn how to use tools and techniques to detect and respond to complex patterns and events in real-time data streams.
7. 7. Distributed Data Processing with Apache Storm: Learners will study Apache Storm, a real-time big data processing system. They will understand how to architect and implement distributed data processing systems using Storm, and gain practical experience through hands-on projects.
8. 8. Stream Processing with Apache Kafka Streams: This module focuses on Apache Kafka Streams, a library for building scalable, mission-critical applications that process and manage real-time streaming data. Learners will learn to write Kafka Streams applications and understand Kafka’s role in stream processing.
9. 9. Security and Privacy in Big Data Streaming: Learners will explore security and privacy challenges in big data streaming environments. They will gain knowledge on securing streaming data, implementing access controls, and ensuring compliance with data privacy regulations.
10. 10. Capstone Project: Building a Real-Time Data Processing System: In this capstone project, learners will apply their knowledge and skills to design, implement, and deploy a real-time data processing system. They will work on a comprehensive project that integrates multiple components of the programme, including data ingestion, processing, and analysis.