What do you prioritize when choosing between batch, streaming, or hybrid data processing pipelines?

30 Tem 22:58

In my work as a cloud developer, I typically prefer using a **hybrid** model when business requirements vary between the need for large-batch stability and the ability to provide immediate responses for certain critical events. For example, in a user behavior analysis project, I leverage Apache Spark for batch processing to generate reliable weekly reports, while integrating it with Apache Flink or Kafka Streams to update a real-time dashboard when important activities like purchases or security breaches occur. The decision depends on three key factors: **data volume**, **required response time**, and **transformation complexity**. If the data volume exceeds terabytes and requires complex aggregation and ETL operations, batch processing offers stable performance and lower costs. However, if records are generated at high velocity and require immediate action (e.g., fraud detection), streaming becomes indispensable. When you need both sides—periodic reports with real-time updates—the hybrid approach is the optimal choice. My practical advice is to build a modular architecture based on **event-driven architecture**, where data can be routed to either a batch or streaming path based on a specific flag or key. Use a data orchestration system like **Delta Lake** or **Apache Iceberg** to store accumulated versions while providing concurrent read/write capabilities. This way, you can unify the code, reduce maintenance complexity, and control costs by running each path only when needed.