OLAP vs OLTP
OLAP (Online Analytical Processing) and OLTP (Online Transaction Processing) are two workload categories: OLTP handles many small, concurrent transactional writes on normalized schemas, while OLAP handles large read-heavy analytical queries on denormalized, columnar data.
Overview
OLTP and OLAP describe two fundamentally different database workloads, and most organizations run both, connected by a data pipeline.
OLTP (Online Transaction Processing) runs the operational side of a business: placing an order, updating a balance, logging an event. Transactions are small, numerous, and concurrent, schemas are normalized to reduce redundancy and keep writes safe, and storage is typically row-oriented since a transaction usually touches a whole row at once. Consistency (ACID) is critical.
OLAP (Online Analytical Processing) runs the analytical side: "what was revenue by region last quarter," "which products are trending." Queries scan and aggregate large volumes of historical data, schemas are denormalized (star schemas, wide tables) to minimize joins, and storage is typically columnar, since an aggregation usually only needs a few columns out of many, across huge numbers of rows.
| OLTP | OLAP | |
|---|---|---|
| Purpose | Run the business | Analyze the business |
| Query pattern | Many small reads/writes | Fewer, large aggregations/scans |
| Schema | Normalized (3NF) | Denormalized (star schema, wide tables) |
| Storage | Row-oriented | Columnar |
| Example engines | PostgreSQL, MySQL | DuckDB, Snowflake, BigQuery |
Example
Copy code
-- OLTP-style query: one row, fast, from a normalized table
SELECT balance FROM accounts WHERE account_id = 42;
-- OLAP-style query: full-table aggregation, from a denormalized fact table
SELECT region, DATE_TRUNC('quarter', order_date) AS quarter, SUM(revenue) AS revenue
FROM fact_sales
GROUP BY ALL
ORDER BY ALL;
Running the second query pattern directly and repeatedly against a production OLTP database risks locking contention and slows down the live application — which is exactly why data gets extracted, transformed, and loaded into a separate OLAP-oriented warehouse.
DuckDB angle
DuckDB is squarely an OLAP engine: vectorized execution and columnar storage make the GROUP BY/aggregation query pattern above fast, while it's not designed for the high-concurrency, single-row-transaction workload OLTP systems handle. The typical architecture pairs an OLTP database (Postgres, MySQL) as the system of record with DuckDB or MotherDuck as the analytical layer fed from it.
Related terms
OLTP (Online Transaction Processing) refers to systems designed for fast, high-concurrency, small read/write transactions — like inserting an order or updating an account balance — typically backed by normalized relational schemas.
Online Analytical Processing (OLAP) →A complete guide to Online Analytical Processing (OLAP). Learn about OLAP cubes, the differences between OLAP and OLTP, and the types of OLAP systems (MOLAP, ROLAP, HOLAP).
HTAP →HTAP (Hybrid Transactional/Analytical Processing) describes database systems designed to handle both transactional (OLTP) and analytical (OLAP) workloads on the same data, without a separate ETL pipeline moving data between two systems.
analytical database →An analytical database, also known as an Online Analytical Processing (OLAP) database, is designed to efficiently handle complex queries and data analysis…
Data warehouse →A data warehouse is a centralized system designed to store structured, cleaned data and support fast, complex analytical queries (OLAP), as opposed to the high-volume transactional workloads of an operational database.
DuckDB →DuckDB is an embeddable SQL database management system designed for analytical workloads.
FAQS
OLAP. DuckDB's columnar, vectorized engine is built for analytical scans and aggregations, not for the high-concurrency, single-row transactional writes that define OLTP workloads.
OLTP systems run day-to-day operations reliably and consistently, but aren't efficient for large analytical queries. OLAP systems are optimized for exactly that kind of analysis, but aren't built for high-concurrency transactional writes. Data typically flows from OLTP source systems into an OLAP warehouse.
