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Production Readiness Roadmap

Purpose: This document outlines the next priorities for transitioning this case study architecture into a production system.

Status: Roadmap (not implemented in case study)

Overview​

The current design addresses business case requirements (§1-4) with production-grade patterns. The following roadmap items would be prioritized for actual production deployment. Part of Operational Runbooks.

GenAI: Runbook step expansion, alarm runbook suggestions, log summarization, and SLA breach explanations are natural fits for Bedrock. See GenAI in the Ohpen Case & Opportunities.

1. SLAs & Monitoring​

Priority: HIGH (Foundation for operational excellence)

Define SLAs​

  • ETL duration < 30 min
  • Quarantine rate < 0.5%
  • End-to-end latency < 60 min

Implement SLA Dashboards​

  • Threshold alerts for SLA violations
  • Real-time monitoring with CloudWatch custom dashboards
  • Executive summary reports (daily/weekly)

Create Runbooks​

  • Glue job stuck or timeout
  • High quarantine rate investigation
  • S3 sync failures
  • Backfill procedures (already implemented: Backfill Playbook)
  • Rollback procedures (already implemented: Rollback Playbook)

2. Data Contracts & Governance​

Priority: HIGH (Risk mitigation for upstream changes)

Formalize Data Contracts​

  • Schema guarantees with upstream systems
  • Delivery windows and SLA commitments
  • Change notification protocols
  • Breaking change escalation process

Implement Lake Formation​

  • Fine-grained access control (column-level security)
  • Row-level filtering for PII/sensitive data
  • Tag-based access control for regulatory compliance

Add Data Lineage Tooling​

  • AWS Glue Data Catalog metadata tracking
  • Integration with data catalog (e.g., Amundsen, DataHub)
  • End-to-end lineage visualization (source → Bronze → Silver → Gold → consumption)

3. Incremental Processing & CDC​

Priority: MEDIUM (Efficiency improvement at scale)

Replace Full-File Ingestion​

  • Evaluate CDC (Change Data Capture) if upstream supports it
  • Implement incremental processing (only new/changed records)
  • Add watermark tracking for incremental reads

Deduplication Strategy​

  • TransactionID uniqueness enforcement
  • Business key deduplication logic
  • Idempotency guarantees for reprocessing

4. Advanced Table Formats​

Priority: MEDIUM (Evaluate when requirements emerge)

Evaluate Apache Iceberg​

  • ACID transactions for concurrent writes (not required for this batch OLAP workload; assumed at source)
  • Time travel for historical queries
  • Schema evolution (column renames, type changes)
  • Partition evolution (dynamic partitioning)

Migration Path​

  • Pilot Iceberg on Gold layer first (lowest risk)
  • Validate performance vs Parquet + Glue Catalog
  • Migrate Silver layer if benefits outweigh complexity
  • Bronze remains immutable (no migration needed)

5. Cost Optimization at Scale​

Priority: LOW (Optimize after baseline established)

Right-Size Glue DPU Allocation​

  • Performance benchmarks: 1 DPU G.1X vs 2 DPUs vs 4 DPUs
  • Auto-scaling policies for variable workloads
  • Optimize partition file size (target 128MB per file)

Query Result Caching​

  • Athena query result caching for repeated queries
  • CloudFront caching for BI dashboard queries
  • Materialized views for frequent aggregations

Reserved Capacity​

  • Glue DPU reservations for predictable workloads (20% discount)
  • Athena capacity reservations (30% discount for committed throughput)

6. Disaster Recovery & Business Continuity​

Priority: MEDIUM (Regulatory requirement for financial data)

Cross-Region Replication​

  • S3 cross-region replication for Bronze layer (immutable audit trail)
  • Terraform state replication for infrastructure recovery
  • RTO/RPO definition and testing

Backup & Recovery​

  • Glue Catalog backup and versioning
  • CloudFormation drift detection
  • Quarterly disaster recovery drills

7. Advanced Monitoring & Alerting​

Priority: MEDIUM (Mature operational model)

Anomaly Detection​

  • CloudWatch Anomaly Detection for quarantine rate, row counts, duration
  • Bedrock-powered quarantine analysis (already implemented in case study)
  • Predictive alerting for capacity planning

Distributed Tracing​

  • AWS X-Ray for end-to-end request tracing
  • Step Functions → Glue → Lambda correlation
  • Performance bottleneck identification

Implementation Sequence​

Phase 1 (Months 1-3): SLAs, monitoring, data contracts Phase 2 (Months 4-6): Lake Formation, incremental processing, runbooks Phase 3 (Months 7-12): Iceberg evaluation, cost optimization, DR planning Phase 4 (Year 2+): Advanced monitoring, anomaly detection, continuous improvement

See also​

Version: 1.0
Last Updated: 2026-01-30
Owner: Platform Engineering Team

© 2026 Stephen Adei•CC BY 4.0