Deep dives into data engineering, cloud architecture, AI/ML, and enterprise technology — straight from the engineers building it.
How we designed and deployed an end-to-end Bronze → Silver → Gold data platform processing 1-2 million orders daily for a ₹20Cr+ FMCG enterprise. Architecture decisions, PySpark optimizations, and lessons learned.
Read Full ArticleDeep dive into our Kafka-based real-time ingestion pipeline — partitioning strategy, consumer groups, exactly-once semantics, and monitoring setup.
How we built an AI-powered support assistant that handles 80% of customer queries automatically using Retrieval Augmented Generation.
Our approach to Infrastructure as Code — module structure, state management, workspaces, and CI/CD integration with GitHub Actions.
An honest comparison from a team that uses both daily — pricing, performance, ecosystem, use cases, and when to choose which.
The story of how a masala company's internal tech team evolved into a full-scale enterprise technology company — challenges, decisions, and the road ahead.
Architecture decisions, performance optimizations, role-based access, and real-time features behind our vendor management web application.
Practical PySpark performance tips from production — partitioning, caching, broadcast joins, predicate pushdown, and more.
How we built a demand prediction system achieving 87% accuracy for 5000+ vendors — feature engineering, model selection, and deployment with MLflow.
Our hiring philosophy, interview process, and the qualities that make a great data engineer — skills over degrees, builders over talkers.