Batch vs. Streaming Data Processing Architectures

Data processing systems fundamentally choose between waiting for complete data sets (batch processing) or processing data as it arrives (streaming processing). This decision impacts system latency, complexity, and how data correctness is handled, especially when dealing with continuous data flows that lack clear end points.

Batch Processing: Completeness Over Latency

Batch processing involves collecting data over a defined period or until a natural boundary (e.g., end-of-day, file completion) is reached, and then processing the entire dataset at once. This approach guarantees data completeness for a given batch but introduces higher latency. It's suitable for historical analysis, reporting, and tasks where real-time insights aren't critical.

• Full Loads: Processing an entire dataset from scratch, often used for initial data ingestion or complete rebuilds.
• Incremental Loads: Processing only the data that has changed or arrived since the last batch, improving efficiency.
• Large-Window Aggregation: Performing computations over large, fixed time windows (e.g., daily, hourly).
• Micro-batching: A hybrid approach where data is collected into small batches and processed frequently, offering a balance between batch and streaming characteristics.

Streaming Processing: Latency Over Completeness

Streaming processing continuously computes results from data as it arrives, prioritizing low latency and real-time insights. This approach introduces challenges related to data completeness, ordering, and handling late-arriving data. Systems must estimate data arrival and implement strategies to correct or reprocess data that arrives out of order or after initial computation.

• Tumbling Windows: Fixed, non-overlapping time intervals (e.g., every 5 minutes) where data is processed independently.
• Sliding Windows: Fixed-size windows that overlap, useful for calculating moving averages or trends.
• Session Windows: Data-driven windows that group events from the same user or entity, closing after a period of inactivity.
• Watermarks: Mechanisms to signal data completeness up to a certain point in time, helping systems manage late data.
• Late Data Handling: Strategies to reprocess or update results when data arrives after its designated window.
• Exactly-Once Processing: A critical but often misunderstood concept aiming to ensure each record is processed exactly once, even in the event of failures, typically achieved through idempotency and transaction management.

Architectural Patterns for Data Processing