Post by Dattaraj Bhoi

Attended a masterclass on Data Warehouses with Anurag Srivastava Sir today — technically the densest session in my #dataXbootcamp journey so far. Here's everything we covered 👇 📌 RDBMS vs Data Warehouse RDBMS = optimized for transactional workloads (frequent inserts/updates/deletes). Data Warehouses = column-oriented, built for analytical reads across millions of rows with denormalized schemas. Same SQL. Completely different purpose. 📌 ACID Properties Atomicity, Consistency, Isolation, Durability — the foundation of reliable transactional systems. Why RDBMS is trusted in banking and healthcare. 📌 OLTP vs OLAP OLTP → high-frequency, low-latency writes (orders, registrations). OLAP → complex multi-dimensional reads (revenue analysis, forecasting). Biggest beginner mistake? Running OLAP workloads on OLTP systems. 📌 Data Warehouse Limitations Schema rigidity, no native unstructured data support, high cost at scale, batch-first architecture — these gaps pushed the industry toward... 📌 Data Lakehouse Merges Lake flexibility (schema-on-read) with Warehouse governance (ACID, BI-ready). Delta Lake and BigLake are making this the default modern architecture. 📌 BigQuery — Hands-On → Partitioning — skip irrelevant data, reduce scan cost → Indexing — navigate within partitions efficiently → Query Optimization — avoid SELECT *, non-sargable predicates, missing partition filters Fundamentals aren't optional in DE. They're the difference between writing queries and understanding them. 🚀 #DataEngineering #BigQuery #OLAP #OLTP #DataLakehouse #SQL #dataXbootcamp #datawithanurag #dataXbootcamp #dataWarehouse