What’s the difference between batch vs streaming data processing

Batch and stream data processing are two distinct yet complementary approaches used by companies to handle data from various sources, each serving different purposes within an organization's data architecture. Batch processing, a method dating back to mainframe computing, is ideal for processing large, discrete datasets and is often used for tasks such as generating backups, creating data repositories, and performing big data analytics, where latency is not a critical factor. This approach efficiently utilizes compute resources, typically scheduling jobs during off-peak times to avoid resource competition. In contrast, stream processing handles continuous data from real-time sources like social media and IoT sensors, enabling near real-time analysis with minimal latency, crucial for time-sensitive applications such as fraud detection, predictive analysis, and automated trading. Technologies like Apache Kafka and Apache Flink facilitate stream processing, while Starburst and Trino enhance both batch and stream processing by simplifying data transformation and storage, offering features like fault tolerance and automatic data ingestion into Iceberg tables. Both processing methods are integral to modern data architectures, with batch processing focusing on historical analysis and stream processing enabling immediate insights and predictive modeling.