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OLTPvsOLAP
What is the difference between OLTP and OLAP?
Updated 2 min read6 differences
In short
OLTP systems handle many small, fast transactions such as orders and payments, while OLAP systems run large analytical queries over historical data.
OLTP
Online Transaction Processing
OLTP (online transaction processing) describes databases built for many small, fast reads and writes from everyday operations, such as placing orders.
Read the page on OLTPOLAP
Online Analytical Processing
OLAP (online analytical processing) describes systems built to answer complex analytical questions over large amounts of historical data quickly.
Read the page on OLAPOLTP and OLAP compared
| Aspect | OLTP | OLAP |
|---|---|---|
| Purpose | Run daily operations | Analyze history and trends |
| Typical query | Read or write a few rows | Scan and aggregate millions of rows |
| Storage layout | Row-oriented | Usually column-oriented |
| Schema | Normalized | Denormalized, often a star schema |
| Data freshness | Real time | Minutes to a day behind, via pipelines |
| Examples | PostgreSQL, MySQL, SQL Server, Oracle | BigQuery, Snowflake, Redshift, ClickHouse |
The difference, explained
OLTP, online transaction processing, is what runs a business moment to moment: creating an order, updating a balance, booking a seat. Each transaction touches a few rows and must be fast and correct even with thousands of users at once. OLAP, online analytical processing, is what explains the business: questions such as which products grew fastest last quarter, answered by scanning and aggregating millions or billions of rows.
Their designs follow from these workloads. OLTP databases, such as PostgreSQL, MySQL and SQL Server, store data by row, use normalized schemas and indexes for quick lookups, and rely on ACID transactions. OLAP systems, such as BigQuery, Snowflake, Redshift and ClickHouse, usually store data by column, use denormalized star schemas, and spread queries across many machines.
Data usually flows from OLTP to OLAP. ETL or streaming pipelines copy operational data into a data warehouse, where it is cleaned and combined with other sources. Keeping them separate protects customer transactions from slow reports and lets analysts query freely.
A common misconception is that one database can do both equally well at scale. Some systems, called HTAP, try to combine them, and small companies often run reports on a replica of their OLTP database. As data grows, a dedicated analytical system usually becomes worth it.
Which one should you use?
Choose OLTP when…
- You record orders, payments, bookings or user actions.
- You need fast, consistent reads and writes of individual records.
- Many users change data at the same time.
Choose OLAP when…
- You build reports, dashboards or business intelligence.
- You analyze large volumes of historical data.
- You combine data from several sources for analysis.
Readers ask
Is a data warehouse OLAP?
Yes. A data warehouse is the most common type of OLAP system: a store designed for analytical queries over cleaned, historical data.
Can I run analytics on my OLTP database?
For small data, yes, ideally on a read replica so reports don't slow down customers. As data grows, moving analytics to an OLAP system is usually faster and cheaper.
What is HTAP?
Hybrid transactional and analytical processing: databases that aim to handle both workloads in one system, typically by keeping data in both row and column formats.