Database system
OLAP
OLTP

OLTP vs OLAP: Comparing the Two Data Processing Systems

intro

Let's learn everything you need to know about two of the most popular database systems in this OLTP vs OLAP comparison.

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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:

  • OLAP (Online Analytical Processing) and OLTP (Online Transaction Processing) are two distinct types of database systems.
  • A database system is a collection of software components that enables the storage, retrieval, updating, and deletion of data on a computer or server.
  • A database system is usually managed by a DBMS (Database Management System), which acts as an interface to interact with and manage the data efficiently.
  • There are several types of DBMS, including Relational DBMS (e.g., MySQL, Microsoft SQL Server, Oracle Database), Object-Relational DBMS (e.g., PostgreSQL), NoSQL DBMS (e.g., MongoDB, Couchbase), Object-Oriented DBMS, Cloud-Based DBMS, In-Memory DBMS, and others.

OLTP vs OLAP: How to Compare the Two Approaches

For each of the two types of database systems, we will compare the following aspects:

  • History: A bit of history behind the database system type
  • Purpose: The primary goal or reason why this database approach has become popular
  • Key characteristics: The defining features that set this type of database apart
  • Use cases: Common scenarios where OLAP or OLTP systems are most effective
  • Examples: A list of databases that implement the specific type of system

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:

  • Data is stored in relational tables, which can be normalized to reduce redundancy.
  • Can handle large numbers of short, frequent transactions, such as order processing, banking, and customer interactions.
  • Transactions are processed immediately, ensuring up-to-date data and quick responses.
  • Follows ACID properties.
  • Multiple users can access the system simultaneously without data conflicts, thanks to transaction management protocols.
  • Optimized for fast retrieval, insertion, updating, and deletion of transactional data.
  • Ensures accurate and consistent data, even during system failures or concurrent transactions.

Use Cases

Here are some of the most common OLTP use cases:

  • Companies: Store data on employees, payroll, benefits, and other company-related information.
  • E-commerce: Processing orders, payments, and inventory management in real-time.
  • Banking: Managing transactions, account balances, withdrawals, deposits, and customer records.
  • Supply chain management: Tracking orders, shipments, and inventory levels in real-time.
  • Government services: Managing citizen records, licenses, tax payments, and other public services.
  • Web applications: Handle user interactions, logins, session management, and real-time updates.

Examples

Popular OLTP databases include:

  • MySQL
  • PostgreSQL
  • SQL Server
  • Oracle Database
  • MongoDB

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.

Example of a data cube
Example of a data cube

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:

  • Data is stored in multidimensional structures, allowing analysis from different perspectives.
  • Primarily deals with historical and aggregated data, often from multiple sources.
  • Supports sophisticated analysis with complex calculations and data comparisons.
  • Designed for read-heavy operations and intensive analytical tasks.
  • Generally optimized to maintain consistent performance as the database size or number of dimensions increases.
  • Used by analysts and decision-makers, with fewer users compared to OLTP systems.
  • Often connected to BI (Business Intelligence) tools to present OLAP results in various formats, including charts and graphs.

Use Cases

Here are the most common OLAP use cases:

  • Sales and revenue analysis: Analyze sales data across different dimensions, such as time, store, location, or product, to identify trends and patterns.
  • Supply chain management: Monitor inventory levels, supplier performance, and demand forecasts.
  • Financial reporting: Aggregate financial data for budgeting, forecasting, and performance evaluation.
  • Market research: Study customer demographics, preferences, and behavior across multiple dimensions to inform marketing strategies.
  • Customer segmentation: Split customers based on various attributes (e.g., purchase behavior, demographics) for targeted marketing or sales strategies.
  • Performance measurement: Track KPIs (Key Performance Indicators) and business metrics to evaluate organizational success.
  • Executive decision-making: Provide high-level insights and summary reports to support strategic business decisions.

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:

FeatureOLTPOLAP
Data modelNormalized data model for faster database operationsMulti-dimensional data model for query, reporting, and aggregations
ProcessingHandles a large number of small transactions, including inserts, updates, and deletesHandles large volumes of data with complex queries to drive business decisions
QueryingSimple, standardized queriesComplex queries for deep analysis
FocusInsert, update, delete records from the databaseSelect and aggregate data for reporting
Data sourceOperational data fed through client applicationsAggregated data from various OLTP, data warehouses, and streaming systems
ObjectiveControl and run essential business operations in real-timePlanning, problem solving, decision support, data discovery, reporting, and insights
Data updatesSmall, quick updates initiated by client applicationsPeriodically loads data using schedulers (newer systems may support real-time data loads)
Storage requirementsGenerally smaller, as only the current snapshot of the record is storedGenerally large, aggregating large historical datasets
UsersDatabase administrators, developers, end-usersAnalysts, 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:

  • ROLAP (Relational OLAP): Uses relational databases to store data. It generates OLAP queries dynamically using SQL, supporting large data volumes but with slower performance for complex queries compared to other OLAP types.
  • MOLAP (Multidimensional OLAP): Stores data in multidimensional cubes, offering fast query performance through pre-aggregated data. It is ideal for handling complex calculations and slicing/dicing operations.
  • HOLAP (Hybrid OLAP): A combination of ROLAP and MOLAP, HOLAP stores large data sets in a relational database but uses multidimensional cubes for faster querying.

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:

  • OLAP (Online Analytical Processing): Focuses on complex queries for data analysis and reporting, handling large volumes of historical and aggregated data.
  • OLTP (Online Transaction Processing): Deals with real-time transaction processing, managing small, frequent transactions. It focuses on fast insert, update, and delete operations.
  • HPC (High-Performance Computing): Involves performing complex calculations and simulations using advanced computing power. It manages massive datasets and intensive computations.

What are the most common OLAP operations?

Common OLAP operations include:

  • Slice: Extract a specific subset of data based on a single dimension (e.g., sales data for a specific year).
  • Dice: Select data based on multiple dimensions (e.g., sales by region and product category). This can be seen as a more complex slice.
  • Drill-down: Explore data at a more detailed level (e.g., viewing monthly sales from yearly data).
  • Roll-up: Aggregate data to a higher level (e.g., summing daily sales to get monthly totals).

What are the most popular OLTP operations?

The most popular OLTP operations are CRUD operations:

  • Create: Add new records to the database.
  • Read: Select and extract data from the database with queries.
  • Update: Modify existing records.
  • Delete: Remove records.
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About the author
Antonello Zanini

Antonello is a software engineer, and often refers to himself as a technology bishop. His mission is to spread knowledge through writing.

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