OLAP vs OLTP — Analytical vs Transactional Databases

OLAP and OLTP are two fundamentally different approaches to databases. OLTP for transactions, OLAP for analytics — understand the differences and choose correctly.

OLTP — Transaction Processing¶

• Purpose: reading/writing individual records
• Storage: row-oriented
• Queries: SELECT WHERE id = 123
• Examples: PostgreSQL, MySQL, MongoDB
• Use cases: e-commerce, banking, CRM

OLAP — Analytical Processing¶

• Purpose: aggregation over large datasets
• Storage: columnar
• Queries: SELECT SUM(revenue) GROUP BY region
• Examples: ClickHouse, DuckDB, Snowflake
• Use cases: reporting, dashboards, ad-hoc analysis

-- OLTP: fast single record read
SELECT * FROM orders WHERE order_id = 12345;  -- <1ms

-- OLAP: aggregation over millions of rows
SELECT region, SUM(total_czk) AS revenue,
       COUNT(DISTINCT customer_id) AS customers
FROM orders
WHERE order_date >= '2026-01-01'
GROUP BY region;  -- <1s in ClickHouse

Why Not One Database for Both¶

Row-oriented storage is efficient for reading entire rows, columnar for reading selected columns. The physical data layout cannot be optimized for both simultaneously.

Summary¶

OLTP for transactions (PostgreSQL), OLAP for analytics (ClickHouse). Modern architecture separates both workloads.