Building Maintainable Data Pipelines: Ingestion, Modeling, and Data Contracts
How modern data platform teams prevent breaking upstream schema changes and build scalable analytics infrastructure.
INSIGHTS
Technical writing for practical decisions.
Notes on software engineering, AI, data and cloud. Written to clarify trade-offs and implementation choices rather than to sell a trend.
LATEST
Ideas worth keeping close to the architecture.
The articles below are original editorial content for the GreyRocks knowledge base. Company-specific case claims are intentionally excluded.
Evaluating RAG Pipelines in Production: Beyond Vanity Benchmarks
A systematic framework for measuring retrieval precision, context recall, and generation faithfulness in enterprise AI systems.
Designing Resilient Event-Driven Systems for Mission-Critical Backends
Practical engineering patterns for idempotency, outbox patterns, and fault isolation in distributed backend architectures.
Designing reliable data systems for growing businesses
A practical architecture checklist for moving from one-off analysis toward repeatable data workflows.
Architecting Cloud Infrastructure for Predictable Cost and High Availability
How engineering teams balance uptime SLAs with cloud infrastructure spend across compute, networking, and storage.
RAG vs fine-tuning: choosing the right approach
A system-level comparison of retrieval-augmented generation and fine-tuning for practical AI applications.
When should a business integrate AI into an existing workflow?
A practical way to decide where AI belongs in an existing business process—and where it should not.