Beyond the Hype: How Swiggy Slashed Query Runtimes from 2 Hours to 15 Minutes India’s tech landscape moves faster than global benchmarks. Swiggy recently overhauled its unified data backbone across Food Delivery, Instamart, and Dineout, moving heavy query latencies from 120 minutes down to 15 minutes and shrinking batch processing cycles from 6 hours to near real-time. For systems engineers, engineering leads, and data architects, the lesson isn't simply "adopt a modern cloud data platform". It is about dismantling monolithic batch anti-patterns: Decoupling Compute from Storage: Traditional data warehouses force you to scale storage capacity whenever compute demand spikes. Moving to multi-cluster, shared-data architectures ensures transactional ingestion (like high-velocity delivery pings) doesn't throttle operational dashboards or downstream feature stores. Zero-Copy Governance at the Edge: High-concurrency platforms cannot duplicate data sets for different business units. Implementing centralized role-based access control (RBAC), column masking, and row-level security directly at the ingestion layer allows hundreds of operational teams to run ad-hoc analytics safely without waiting for data engineering tickets. Treating Latency as a First-Class Feature: At scale, data is valuable only when it reaches the decision point in time. Moving processing pipelines closer to real-time turns analytics from passive hindsight into active automated dispatching, fraud scoring, and dynamic catalog routing. If your systems are still waiting on nightly cron batches to understand midday platform traffic, your infrastructure is already creating operational drag. Discussion Question What is the single biggest bottleneck in your current data pipeline—storage lock-in, legacy compute queues, or compliance-driven access controls? CTA (Join Techawks India) Join Techawks India to dissect real-world infrastructure designs, benchmark scalable architectures, and connect with engineers building India's digital core.