FinOps brings financial accountability to cloud infrastructure by connecting engineering decisions to their cost impact. These posts cover BigQuery reservation planning, storage cost tiering, multi-cloud egress optimisation, and the dashboards and automation that keep cloud spend visible and actionable.
5 posts tagged with finops. ← All posts
What looked like an idiomatic BigQuery MERGE was scanning the full target table on every batch. The fix was syntactic, not architectural — and it was the single biggest contributor to a 57% data-warehouse cost reduction across the Tata Group engagement.
Architecture decisions that delivered multi-million dollar savings at petabyte scale.
Architecture decisions that delivered 57% cost reduction on a Fortune 500 BigQuery data warehouse. The MERGE anti-pattern fix, partition strategy, capacity model, and the trust gap I'd handle differently next time.
We built a small Go + Python service that parses a project's INFORMATION_SCHEMA, asks Gemini to classify each top-spending query against a catalog of anti-patterns, and recommends a rewrite. It is not a magic box; it is a pipeline that cuts the human review time per query from 20 minutes to 90 seconds.
Capacity-based slot reservation is the biggest single FinOps lever for predictable batch workloads, but the transition is harder than the math. Notes from sizing reservations across enterprise GCP customers.
Storage was the second-biggest line on a large-enterprise BigQuery bill. Physical-vs-logical billing and column-level retention delivered significant savings.
All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.