Introduction

Real-time data processing offers immediate insights compared to traditional batch processing, enabling analysis of data as it arrives.

Core Architecture Components

• Redpanda: A Kafka-compatible event streaming platform that's simpler to operate
• Apache Flink: A stream processing framework with Python API (PyFlink)
• PostgreSQL: Used for persistent storage and analysis

Key Technical Concepts

Event Streaming Fundamentals

Data flows through "topics" that serve as channels between producers and consumers.

System Integration Skills

• Python applications connected to streaming platforms via Kafka client library
• PyFlink configuration for Redpanda and PostgreSQL integration
• Message serialization and deserialization management

Time-Based Processing

Implementation of session windows for grouping time-based events, including:

• Watermark configuration for late data handling
• Session window parameters setup
• Window-based data aggregation

Practical Application