Real-Time Data Processing with Flink offers a comprehensive exploration of stream processing techniques, equipping participants with the skills necessary to handle and analyze data as it arrives. This course emphasizes practical applications, utilizing project-based learning to ensure that participants can apply theoretical knowledge to real-world scenarios. By engaging in hands-on projects, learners will develop a robust understanding of Flink’s capabilities, enabling them to tackle complex data challenges in various industries.
Throughout the course, participants will delve into essential topics such as data ingestion, transformation, and real-time analytics. The structure encourages collaboration and knowledge sharing, with opportunities to publish findings in Cademix Magazine. By the end of the program, attendees will be well-prepared to implement Flink in their professional roles, enhancing their employability and expertise in the field of data analytics.
Introduction to Stream Processing Concepts
Overview of Apache Flink Architecture
Setting Up the Flink Environment
Data Ingestion Techniques with Flink
Real-Time Data Transformation and Enrichment
Windowing and Time Handling in Flink
State Management and Fault Tolerance
Integrating Flink with Other Data Systems (Kafka, HDFS, etc.)
Performance Tuning and Optimization Strategies
Final Project: Building a Real-Time Analytics Application with Flink