What are the key differences between batch and stream processing architectures?

Batch and stream processing architectures differ primarily in how they handle data timing, use cases, and technical trade-offs. Batch processing operates on bounded datasets collected over a period (e.g., hours or days), processes them in bulk, and produces results after the entire dataset is analyzed. Stream processing, in contrast, handles unbounded data arriving in real-time, processing individual records or micro-batches immediately as they are generated. This fundamental distinction drives differences in latency, tooling, and design patterns.

Data Timing and Latency Batch processing is optimized for high-throughput, latency-tolerant workloads. For example, a daily sales report aggregating transactions from a database is a classic batch use case. Tools like Apache Hadoop or Spark are designed to process large datasets efficiently but introduce delays (minutes to hours) between data ingestion and results. Stream processing systems like Apache Kafka Streams or Apache Flink prioritize low latency, processing data in milliseconds to seconds. A real-time fraud detection system analyzing credit card transactions as they occur would require stream processing to block fraudulent activity before it completes. While batch systems focus on scaling compute and storage, stream systems emphasize event-time handling and managing out-of-order data.

Use Cases and Tooling Batch architectures excel at historical analysis, ETL (Extract, Transform, Load) pipelines, and scenarios where data completeness outweighs speed. For instance, training a machine learning model on a month’s worth of user behavior data is a batch task. Stream processing suits real-time monitoring (e.g., tracking server metrics), alerting, or applications like ride-sharing apps updating driver locations live. Batch systems often rely on distributed storage (e.g., HDFS, cloud object storage) and scheduled jobs, while stream systems depend on message brokers (e.g., Kafka, Pulsar) to ingest and buffer incoming data. Stream frameworks also include built-in support for windowing (grouping events by time or count) and state management to handle continuous data flows.

Technical Trade-offs Batch processing simplifies fault tolerance because jobs can be rerun if failures occur, and data is static during processing. However, it requires managing large intermediate datasets (e.g., temporary files in Spark). Stream processing must handle stateful operations (e.g., counting events over a sliding window) and recover from failures without data loss or duplication, often using checkpointing or exactly-once processing guarantees. Resource usage also differs: batch jobs can leverage ephemeral clusters, while stream systems typically run long-lived services. Hybrid approaches like Lambda architectures combine both models but add complexity, whereas modern frameworks like Apache Flink unify batch and stream processing under a single engine using bounded streams for batch workloads.