Batch vs Streaming Monitoring Trade-offs

BATCH MONITORING
Run quality checks on complete datasets at scheduled intervals (hourly, daily). Process entire batch, compute statistics, compare against expectations, alert on violations.
Advantages: Simple to implement, efficient computation over large datasets, full context for statistical tests (complete samples).
Disadvantages: Detection latency equals batch interval. If you run daily checks, a problem at 8am is not detected until the next day. During that time, the model serves bad predictions.
Best for: ETL pipelines, training data validation, low-velocity data sources.
STREAMING MONITORING
Check quality in real-time as data flows through. Use stream processing (Kafka Streams, Flink) to compute rolling statistics and detect violations within seconds or minutes.
Advantages: Fast detection (sub-minute), catches issues before they propagate, enables immediate circuit-breakers.
Disadvantages: More complex infrastructure, harder to compute certain statistics (percentiles require approximations), higher operational cost.
Best for: real-time inference pipelines, high-stakes predictions (fraud, pricing), when fast detection is critical.
HYBRID APPROACHES
Combine batch and streaming. Use streaming for critical checks (schema validation, obvious violations) and batch for comprehensive analysis (distribution comparisons, complex statistics).
Typical pattern: streaming monitors for nulls, type violations, and obvious range errors. Batch monitors for distribution drift, cardinality changes, and correlation shifts.
CHOOSING YOUR APPROACH
Consider: data velocity (how fast does data arrive?), impact of latency (how bad is delayed detection?), infrastructure maturity (can you operate streaming?), and cost constraints.
When To Use: Start with batch for simplicity. Add streaming when detection latency becomes a measurable business problem. Hybrid is the mature end-state for critical systems.

💡 Key Takeaways

✓Batch: simple, efficient for large datasets, detection latency = batch interval; good for ETL and training data

✓Streaming: sub-minute detection, higher complexity and cost; good for real-time inference and high-stakes predictions

✓Hybrid: streaming for critical checks (schema, obvious violations), batch for comprehensive analysis (distributions)

📌 Interview Tips

1Interview Tip: Compare batch vs streaming tradeoffs: simplicity vs latency vs cost.

2Interview Tip: Describe a hybrid architecture—what checks run in each layer and why.

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