intro
Let's learn everything you need to know about two of the most popular database systems in this OLTP vs OLAP comparison.
If you have worked with or studied databases, you have probably heard of the terms “OLTP” and “OLAP.” While these two acronyms are similar, they represent distinct types of database systems—each designed for different use cases and processing requirements. What is OLAP? What is OLTP? When should you use one over the other? Find out in this OLTP vs OLAP guide!
By the end of this article, you will be able to answer these questions and choose the best database system for your needs.
Let's dive in!
OLAP and OLTP: Preliminary Information
Here is some prerequisite information you should know before diving into this article:
OLTP vs OLAP: How to Compare the Two Approaches
For each of the two types of database systems, we will compare the following aspects:
OLTP vs OLAP database: What is the difference? Let’s find out!
OLTP: Online Transaction Processing
The OLAP vs OLTP comparison starts here—with OLTP.
History
Online Transaction Processing — or OLTP — systems trace their roots to the 1960s with early database management systems like IBM's IMS. These systems were built to handle high volumes of real-time transactions, such as financial transactions and order processing. Over time, OLTP evolved together with relational (and, in part, non-relational) databases. Today, OLTP supports key business operations across several industries.
Purpose
OLTP (Online Transaction Processing) systems are designed to manage and process high-volume, real-time transactional data. A transaction in OLTP refers to a sequence of operations that are executed as a single unit of work, ensuring that all changes are completed or none at all.
OLTP achieves its purpose through transaction management techniques. In relational databases, it relies on ACID (Atomicity, Consistency, Isolation, Durability) properties to ensure data consistency and integrity during concurrent transactions.
Although traditional relational databases are typically the first choice for OLTP, this database system can be based on NoSQL databases as well. For more information, read our guide on SQL vs NoSQL databases.
Key Characteristics
Below are the key aspects of OLTP databases, particularly in relational databases:
Use Cases
Here are some of the most common OLTP use cases:
Examples
Popular OLTP databases include:
OLAP: Online Analytical Processing
Continue the OLAP vs OLTP comparison by exploring OLTP.
History
OLAP's origins trace back to 1970 with the release of Express, a multi-dimensional database technology later acquired by Oracle. At the same time, the term "OLAP" was coined only in 1993, by Edgar F. Codd, who is also considered the "father of the relational database." The late 1990s saw rapid OLAP market growth, further propelled in 1998 by the launch of Analysis Services—Microsoft's first OLAP solution.
Purpose
OLAP databases aim to facilitate complex data analysis with fast, interactive querying. They organize data into multidimensional cubes, where each dimension represents a specific attribute, such as time, location, and type of product.

An OLAP cube is a data structure that allows for rapid analysis of large datasets. It provides aggregated values (e.g., sums or averages) and enables users to "slice" and "dice" the data, exploring different perspectives and zooming into specific data points. This flexibility supports efficient summarization, trend analysis, and forecasting.
By storing pre-aggregated data and optimizing for read-heavy operations, OLAP systems support quick retrieval and complex calculations. That makes them ideal for data-driven decision-making.
Key Characteristics
The main characteristics of OLAP are as follows:
Use Cases
Here are the most common OLAP use cases:
Examples
Popular OLAP databases include:
OLTP vs OLAP Database: Summary Table
For a quick analysis, take a look at the OLTP vs OLAP database system comparison table below:
Feature | OLTP | OLAP |
---|---|---|
Data model | Normalized data model for faster database operations | Multi-dimensional data model for query, reporting, and aggregations |
Processing | Handles a large number of small transactions, including inserts, updates, and deletes | Handles large volumes of data with complex queries to drive business decisions |
Querying | Simple, standardized queries | Complex queries for deep analysis |
Focus | Insert, update, delete records from the database | Select and aggregate data for reporting |
Data source | Operational data fed through client applications | Aggregated data from various OLTP, data warehouses, and streaming systems |
Objective | Control and run essential business operations in real-time | Planning, problem solving, decision support, data discovery, reporting, and insights |
Data updates | Small, quick updates initiated by client applications | Periodically loads data using schedulers (newer systems may support real-time data loads) |
Storage requirements | Generally smaller, as only the current snapshot of the record is stored | Generally large, aggregating large historical datasets |
Users | Database administrators, developers, end-users | Analysts, business intelligence users, decision-makers |
How to Interact With OLTP and OLAP Databases
As you learned here, OLTP and OLAP databases serve different purposes and follow distinct approaches to database systems. Regardless of which database system you use, having a reliable database client to query them both would be great. This is where DbVisualizer comes in!
DbVisualizer is a top-rated, visual, full-featured database client that supports over 50 databases, including: Oracle, MySQL, MariaDB, Microsoft SQL Server, PostgreSQL, MongoDB, Snowflake, Elasticsearch, IBM Db2, SQLite, Databricks, Cassandra, ClickHouse, and Azure Synapse (both serverless and dedicated).
For a complete list, visit the official database support page.
Conclusion
In this guide, you gained valuable insights into the comparison between OLTP and OLAP database systems. You now understand what OLTP and OLAP are, how they differ, and in which aspects. Regardless of the database system you choose, you need a powerful client that can handle the most popular databases, like DbVisualizer!
DbVisualizer supports multiple DBMS technologies and offers advanced features such as visual data exploration, query optimization, SQL formatting, and ERD-like schema generation. Try DbVisualizer for free today!
FAQ
What are the types of OLAP databases?
The most common types of OLAP databases are:
Can OLTP database be NoSQL?
Yes, OLTP databases can be NoSQL. While relational databases are commonly used for OLTP due to ACID compliance, NoSQL databases can also handle OLTP workloads, especially when high scalability and flexibility are needed.
What is the difference between OLAP vs OLTP vs HPC?
The OLAP vs OLTP vs HPC comparison can be broken down as follows:
What are the most common OLAP operations?
Common OLAP operations include:
What are the most popular OLTP operations?
The most popular OLTP operations are CRUD operations: