We’re designing our rules engine configuration for processing ingested IoT sensor data. The platform supports both data filtering (discarding low-value data before storage) and event enrichment (augmenting events with contextual information before analytics). I’m trying to understand best practices for balancing these approaches.
Data filtering strategies can dramatically reduce storage costs and improve query performance by eliminating noise. However, aggressive filtering might discard data that becomes valuable later for unexpected analytics use cases. Event enrichment provides richer context for analytics and reporting, but increases processing overhead and storage requirements.
What rules engine configuration patterns have others found effective? How do you balance storage efficiency with analytical flexibility? Interested in hearing about production implementations and lessons learned.
Consider tiered storage rather than aggressive filtering. Keep enriched, full-fidelity data in hot storage for recent time periods (last 30 days), then age data to cold storage with reduced enrichment. This preserves historical data for unexpected analytics needs while controlling costs. We filter only truly valueless data like malformed messages or test traffic.
Event enrichment has been incredibly valuable for our analytics use cases. We enrich sensor events with device metadata, location information, and operational context at ingestion time. This makes analytics queries much simpler and faster - we don’t need complex joins across multiple tables. The processing overhead is minimal (adds 10-15ms per event), but query performance improved by 60%. Well worth the trade-off.
From an analytics perspective, enrichment is more valuable than filtering for generating actionable insights. Enriched events with contextual metadata enable much richer analysis. We can slice data by location, device type, operational mode, etc. without complex joins. The additional storage cost is negligible compared to the analytical value. Focus enrichment on dimensions you’ll actually query - don’t enrich with every possible attribute.
We implemented aggressive filtering early on and regretted it. We filtered out sensor readings that seemed redundant (consecutive identical values), but later discovered those patterns were important for detecting sensor malfunctions. Now we use minimal filtering - only discarding malformed data or known test messages. Storage is cheaper than recreating lost historical data.