3. Batch vs Streaming: Understand the Trade-Offs

Reis and Housley clearly outline the trade-offs between batch and streaming data systems. Batch processing (e.g., with Apache Spark or dbt) is robust and suitable for most business use cases. Streaming (e.g., Apache Kafka, Flink, or Beam) enables real-time decisions but requires complexity in design and fault tolerance. Engineers must evaluate latency needs, cost of processing, and system reliability. The key takeaway: don’t adopt real-time pipelines for novelty. Use streaming only when real business value demands it. Choose tools based on data freshness requirements, not hype.