---
title: "OLAP vs OLTP"
description: "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."
canonical: "https://motherduck.com/glossary/olap-vs-oltp/"
related:
  - title: "Why Use DuckDB for Analytics?"
    url: "https://motherduck.com/blog/six-reasons-duckdb-slaps/"
  - title: "Environment management | MotherDuck Docs"
    url: "https://motherduck.com/docs/key-tasks/data-warehousing/environment-management/"
  - title: "DuckLake Architecture Deep Dive: Catalog, Storage, Compute | MotherDuck"
    url: "https://motherduck.com/videos/ducklake-architecture-deep-dive/"
---

# 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 |

<glossary-callout guide="duckdb-book-brief" />

### Example

```sql
-- 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.