We’re evaluating analytics architecture for our TC 13.1 enterprise deployment serving 2,800 users. Current batch-processed reports run nightly with 6-hour processing windows, delivering insights by 8 AM. Business stakeholders now demand real-time dashboards for production metrics, quality trends, and change order velocity.
Our infrastructure team raises concerns about real-time dashboard architecture impacting transactional database performance. We’ve explored materialized views for pre-aggregated data, but refresh strategies remain unclear. Some teams advocate hybrid approaches-real-time for critical metrics, batch for complex analytics.
Looking for experiences balancing real-time requirements against system performance. How do you architect dashboards without degrading operational throughput? What materialized view refresh patterns work at enterprise scale? Interested in both technical implementations and organizational decision frameworks for determining which metrics truly need real-time versus batch processing optimization.
Synthesizing this excellent discussion into our architectural decision framework:
Real-Time Dashboard Architecture (for <5% of metrics):
- Implement CDC-based event streaming for high-velocity operational data
- Use incremental materialized view refresh (5-15 minute cycles) for simple aggregations
- Limit to truly actionable metrics where latency blocks immediate decisions
- Example use cases: production line status, critical ECO tracking, active workflow bottlenecks
Batch Processing Optimization (for 95% of analytics):
- Migrate to incremental batch processing using audit timestamps (3x daily cycles achievable)
- Complex trend analysis, predictive models, cross-module reporting run against analytics replica
- Schedule optimization: business hours every 4 hours, overnight hourly for background jobs
- Delivers ‘near real-time’ perception (4-hour max latency) for vast majority of requirements
Materialized Views Strategy:
- ON COMMIT REFRESH: Only for critical, simple aggregations on high-velocity tables (<10 views)
- ON DEMAND REFRESH: Standard pattern with 15-30 minute business hour cycles, hourly overnight
- Dedicated analytics schema isolates all reporting queries from production transactional load
- Implement refresh job monitoring dashboard-track duration, failures, row counts as leading indicators
Performance Monitoring Framework:
- Establish query governance requiring all analytics to use analytics schema (no direct production queries)
- Implement BI tool-layer caching (2-3 minute TTL acceptable for most ‘real-time’ dashboards)
- Monitor refresh job performance, database CPU/IO impact, dashboard response times
- Target: <2% production CPU impact from analytics workload during business hours
Decision Matrix for Real-Time vs Batch:
Score each metric on: response time requirement, data volatility, query complexity, user concurrency
High scores trigger cost-benefit analysis-infrastructure investment justified by business value?
Default to optimized batch processing; real-time requires explicit business case
Key Lesson: Technical capability shouldn’t drive requirements. Most ‘real-time’ requests are satisfied by well-optimized batch processing (4-hour latency). True real-time architecture should serve <5% of metrics where immediate visibility drives operational decisions. This balanced approach delivers perceived real-time performance while maintaining system stability and controlling infrastructure costs.
Our implementation roadmap: Phase 1 (Q2) - optimize batch to 3x daily cycles; Phase 2 (Q3) - implement CDC for 8 critical operational dashboards; Phase 3 (Q4) - establish analytics governance and monitoring framework. Projected outcome: 95% of stakeholder requirements met with optimized batch, 5% with targeted real-time architecture, <2% production system impact.
We implemented a tiered analytics architecture that balances both approaches. Critical operational dashboards (production line status, active ECO counts, overdue approvals) refresh every 5 minutes using incremental materialized view updates. These hit only 8-12 key metrics with simple aggregations. Complex trend analysis, predictive quality reports, and cross-module analytics run in batch overnight against a dedicated analytics database replica. Performance monitoring showed negligible impact-under 2% CPU increase during refresh windows. The key was ruthlessly limiting real-time scope to truly actionable metrics where 24-hour latency blocks decisions.
The organizational framework matters as much as technical architecture. We created a decision matrix: response time requirement (seconds/minutes/hours/days), data volatility (updates per hour), query complexity (table joins, aggregation depth), and user concurrency. Metrics scoring high on all dimensions go through a cost-benefit analysis-is real-time visibility worth the infrastructure investment? Often the answer is no. For example, stakeholders initially demanded real-time cost variance dashboards, but analysis showed finance reviews happen weekly. We delivered daily batch updates, saved significant infrastructure costs, and stakeholders were satisfied. Technical capability shouldn’t drive requirements-business value should.
Batch processing optimization often gets overlooked in the real-time hype. We invested in incremental batch processing-instead of full table scans nightly, we process only changed records since last run using audit timestamps. This reduced our batch window from 6 hours to 90 minutes, enabling three daily batch cycles (morning, midday, evening) that feel ‘near real-time’ for most use cases. Combined with strategic real-time dashboards for truly time-sensitive metrics (production line status, critical path ECOs), this hybrid approach delivers perceived real-time performance at fraction of the infrastructure cost and complexity.
Materialized view refresh strategy is critical. We use ON COMMIT REFRESH for high-velocity tables (change objects, workflow tasks) but only for simple aggregations-counts, sums, latest status. Complex joins and historical trend calculations use ON DEMAND REFRESH scheduled every 15-30 minutes during business hours, hourly overnight. This prevents refresh cascades that can lock tables. Also implemented a monitoring dashboard for the refresh jobs themselves-tracking duration, row counts, failure rates. Two refresh jobs consistently exceeded 10-minute windows, so we redesigned those views with better indexing and partition pruning.
Performance monitoring revealed our biggest bottleneck wasn’t the dashboards-it was unoptimized queries hitting production tables directly. We established strict governance: all analytics queries must use the dedicated analytics schema (materialized views + replicated dimension tables). Direct production queries require architecture review. This separation protects transactional performance while giving analytics teams flexibility to experiment with complex queries. We also implemented query result caching at the BI tool layer-many ‘real-time’ dashboards actually tolerate 2-3 minute staleness, so caching eliminates redundant database hits when multiple users view the same dashboard.