RDBMS vs Columnar Databases | OLTP vs OLAP Explained
Not all databases are built for the same workload.
In this video, we break down the fundamental differences between row‑oriented databases (RDBMS) and columnar databases, and explain how each one fits into modern data architectures.
📊 What you’ll learn:
Why row‑based databases are the backbone of OLTP systems
How columnar storage powers high‑performance analytics (OLAP)
Differences in compression, query execution, and data access patterns
Real‑world platforms like Snowflake, ClickHouse, and Databricks
How predicate pushdown and zone skipping reduce data scans
The rise of hybrid architectures combining ACID transactions with fast analytics
🔍 Key Takeaways:
Row‑oriented databases excel at frequent updates and point lookups
Columnar databases scan only required columns, enabling faster aggregation queries
Modern cloud data platforms blend both approaches for scalable, cost‑efficient analytics
🚀 Whether you’re designing transactional systems, analytical platforms, or lakehouse architectures, understanding these storage models is critical for performance and scalability.
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💬 Comment: RDBMS or Columnar—what do you use most?
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