Designing data pipelines for machine learning requires deciding between high-throughput batch processing and sub-second stream processing. While deep foundational model training relies on massive offline batch runs, applications like real-time fraud detection, recommendation engines, and dynamic pricing require streaming feature pipelines.
1. Batch vs. Stream: Core Trade-Offs
Batch pipelines maximize compute efficiency across terabytes of static logs, whereas stream pipelines process continuous event logs (Apache Kafka / Flink) for sub-second feature inference.
2. Unifying Batch & Stream with the Kappa Architecture
Modern MLOps architectures favor the Kappa Architecture, where all data is treated as an immutable log stream. Apache Kafka acts as the central log, while Apache Flink processes real-time features and streams raw events into long-term Parquet storage for batch model retraining.
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This technical analysis is fact-checked and maintained under GRAP Solutions' Data Governance & Editorial Standards. Peer-reviewed for accuracy across synthetic data, MLOps, and multimodal pipeline engineering.
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