What’s the difference between OLAP and OLTP?

Online analytical processing (OLAP) and online transaction processing (OLTP) are data processing systems that help you store and analyze business data. You can collect and store data from multiple sources—such as websites, applications, smart meters, and internal systems. OLAP combines and groups the data so you can analyze it from different points of view. Conversely, OLTP stores and updates transactional data reliably and efficiently in high volumes. OLTP databases can be one among several data sources for an OLAP system.

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What are the similarities between OLAP and OLTP?

Both online analytical processing (OLAP) and online transaction processing (OLTP) are database management systems for storing and processing data in large volumes. They require efficient and reliable IT infrastructure to run smoothly. You can use them both to query existing data or store new data. Both support data-driven decision-making in an organization.

Most companies use OLTP and OLAP systems together to meet their business intelligence requirements. However, the approach to and purpose of data management differ significantly between OLAP and OLTP.

Key differences: OLAP vs. OLTP

The primary purpose of online analytical processing (OLAP) is to analyze aggregated data, while the primary purpose of online transaction processing (OLTP) is to process database transactions.

You use OLAP systems to generate reports, perform complex data analysis, and identify trends. In contrast, you use OLTP systems to process orders, update inventory, and manage customer accounts.

Other major differences include data formatting, data architecture, performance, and requirements. We’ll also discuss an example of when an organization might use OLAP or OLTP.

Data formatting

OLAP systems use multidimensional data models, so you can view the same data from different angles. OLAP databases store data in a cube format, where each dimension represents a different data attribute. Each cell in the cube represents a value or measure for the intersection of the dimensions.

In contrast, OLTP systems are unidimensional and focus on one data aspect. They use a relational database to organize data into tables. Each row in the table represents an entity instance, and each column represents an entity attribute.

Data architecture

OLAP database architecture prioritizes data read over data write operations. You can quickly and efficiently perform complex queries on large volumes of data. Availability is a low-priority concern as the primary use case is analytics.

On the other hand, OLTP database architecture prioritizes data write operations. It’s optimized for write-heavy workloads and can update high-frequency, high-volume transactional data without compromising data integrity.

For instance, if two customers purchase the same item at the same time, the OLTP system can adjust stock levels accurately. And the system will prioritize the chronological first customer if the item is the last one in stock. Availability is a high priority and is typically achieved through multiple data backups.


OLAP processing times can vary from minutes to hours depending on the type and volume of data being analyzed. To update an OLAP database, you periodically process data in large batches then upload the batch to the system all at once. Data update frequency also varies between systems, from daily to weekly or even monthly.

In contrast, you measure OLTP processing times in milliseconds or less. OLTP databases manage database updates in real time. Updates are fast, short, and triggered by you or your users. Stream processing is often used over batch processing.

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Read about batch processing »


OLAP systems act like a centralized data store and pull in data from multiple data warehouses, relational databases, and other systems. Storage requirements measure from terabytes (TB) to petabytes (PB). Data reads can also be compute-intensive, requiring high-performing servers.

On the other hand, you can measure OLTP storage requirements in gigabytes (GB). OLTP databases may also be cleared once the data is loaded into a related OLAP data warehouse or data lake. However, compute requirements for OLTP are also high.

Example of OLAP vs. OLTP

Let's consider a large retail company that operates hundreds of stores across the country. The company has a massive database that tracks sales, inventory, customer data, and other key metrics.

The company uses OLTP to process transactions in real time, update inventory levels, and manage customer accounts. Each store is connected to the central database, which updates the inventory levels in real time as products are sold. The company also uses OLTP to manage customer accounts—for example, to track loyalty points, manage payment information, and process returns.

In addition, the company uses OLAP to analyze the data collected by OLTP. The company’s business analysts can use OLAP to generate reports on sales trends, inventory levels, customer demographics, and other key metrics. They perform complex queries on large volumes of historical data to identify patterns and trends that can inform business decisions. They identify popular products in a given time period and use the information to optimize inventory budgets.

When to use OLAP vs. OLTP

Online analytical processing (OLAP) and online transaction processing (OLTP) are two different data processing systems designed for different purposes. OLAP is optimized for complex data analysis and reporting, while OLTP is optimized for transactional processing and real-time updates.

Understanding the differences between these systems can help you make informed decisions about which system meets your needs better. In many cases, a combination of both OLAP and OLTP systems may be the best solution for businesses that require both transaction processing and data analysis. Ultimately, choosing the right system depends on the specific needs of your business, including data volume, query complexity, response time, scalability, and cost.


Summary of differences: OLAP vs. OLTP






OLAP helps you analyze large volumes of data to support decision-making.

OLTP helps you manage and process real-time transactions.

Data source

OLAP uses historical and aggregated data from multiple sources.

OLTP uses real-time and transactional data from a single source.

Data structure

OLAP uses multidimensional (cubes) or relational databases.

OLTP uses relational databases.

Data model

OLAP uses star schema, snowflake schema, or other analytical models.

OLTP uses normalized or denormalized models.

Volume of data

OLAP has large storage requirements. Think terabytes (TB) and petabytes (PB).

OLTP has comparatively smaller storage requirements. Think gigabytes (GB).

Response time

OLAP has longer response times, typically in seconds or minutes.

OLTP has shorter response times, typically in milliseconds

Example applications

OLAP is good for analyzing trends, predicting customer behavior, and identifying profitability.

OLTP is good for processing payments, customer data management, and order processing.

How can AWS support your OLAP and OLTP requirements?

Analytics on Amazon Web Services (AWS) provides various managed cloud services for online analytical processing (OLAP) and online transaction processing (OLTP) operations. From data movement, data storage, data analytics, and more, AWS offers purpose-built services that provide the best price performance, scalability, and lowest cost.

Here are examples of AWS services that can support your OLAP and OLTP needs:

  • Amazon Redshift is a cloud data warehouse designed specifically for OLAP.
  • Amazon Relational Database Service (Amazon RDS) is a relational database with OLAP functionality. You can use it to run OLTP workloads or with Oracle OLAP to perform complex queries on dimensional cubes.
  • Amazon Aurora is a MySQL- and PostgreSQL-compatible cloud relational database that can run both OLTP and complex OLAP workloads.

Get started with OLTP and OLAP on AWS by creating an AWS account today.

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