OLAP vs. OLTP: Differences in data warehouse architecture

from | 10 June 2024 | Basics

Did you know that the global data volume will grow to 180 zettabytes in 2025 - a significant increase from 64.5 zettabytes in 2020? With this increase in data, mastering efficient data processing is critical as organisations increasingly rely on data to gain insights and drive strategic business initiatives. Online Analytical Processing (OLAP) and Online Transaction Processing (OLTP) are two fundamental approaches that help with data processing. OLAP specialises in complex analytical queries, while OLTP is used for transactional processes. These systems fulfil different but complementary purposes in the processing of data. 

What is OLAP?  

OLAP is software that enables the rapid multidimensional analysis of large volumes of data from a Data Warehousedata mart or a central data storage facility. 

Organisations collect and store data from various data sources, including applications, websites, smart meters, internal systems, etc. OLAP groups and categorises this data Datato gain valuable insights that are crucial for strategic decision-making. 

OLAP works with a multidimensional data model that represents data in more than two categories or dimensions. An OLAP system consists of the following elements:

  • Data WarehouseA centralised repository that stores large amounts of historical and real-time data from various sources, structured to facilitate analysis. For example, the data warehouse could contain data from a relational Database which is structured in tables with rows and columns.
  • ETL tools (Extract, Transform, Load): These tools are responsible for extracting data from various sources, transforming it into a format suitable for analysis and loading it into the data warehouse. Using data warehouses ETL processesto convert and standardise data from different sources and ensure its compatibility with OLAP tools.
  • OLAP serverThis is the server responsible for managing and processing OLAP queries. It converts the data in relational databases using ETL tools and prepares it for OLAP operations. 
  • OLAP databaseThis database is a separate database that is connected to the data warehouse so as not to burden it with OLAP analyses. An OLAP database can also simplify the creation of OLAP models. 
  • OLAP cubesThese are multidimensional structures that represent data in the form of a cube, with key figures at the intersections of the dimensions. These cubes have a certain rigidity, as it is not possible to change the dimensions and the underlying data after modelling. For example, adding a warehouse dimension to a cube with demographic customer data, product categories and sales regions requires the entire cube to be reorganised.
  • OLAP analysis toolsThese tools provide the user interface for interacting with OLAP systems. They are used to perform operations such as dicing, slicing and pivoting in order to obtain details within the OLAP cube and gain further insights.
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What is an OLAP cube?

An OLAP cube is an array-based, multidimensional data structure developed for fast and efficient data processing and analysis. It consists of multiple dimensions to improve performance. While relational databases struggle with querying and analysing multidimensional data as they grow, the design of the OLAP cube enables seamless data exploration across different dimensions.

Their design enables OLAP cubes to store data in aggregated form. In addition, the pre-calculated compilation of values in the cube means that it already has answers to user queries. The schematic representation of this system with OLAP cubes can be seen in the following illustration. 

The OLAP cube supports operations such as drill-down, roll-up, slice, dice and pivot and offers users options for interacting with data. Drill-down displays the data in more detail by moving conceptual hierarchies downwards, while roll-up summarises the data by moving hierarchies upwards. 

Slicing and dicing can be used to create sub-cubes by selecting dimensions. Pivot allows users to rotate the cube view to obtain versatile, multi-dimensional data visualisations. These operations allow users to gain sophisticated insights from complex data sets. 

Popular OLAP tools 

Let's take a look at three notable applications that have been recognised for their versatility and performance in the Business Intelligence are known.

IBM Cognos

IBM Cognos is one of the leading web-based reporting and multidimensional analysis systems. It is an enterprise software that provides an integrated toolkit for data exploration, analysis, reporting and scorecarding. With components such as Cognos Report Studio for versatile report creation, it efficiently fulfils the various information requirements of companies. Cognos Analysis Studio enables in-depth analysis of complex data sources with important OLAP functions such as roll-up and drill-down.

Microsoft SQL Server Analysis Services (SSAS)

Microsoft SSAS is a multidimensional OLAP tool within SQL Server. It enables IT professionals to analyse large data sets and organise them into easily navigable cubes. SSAS provides a unified tool for comprehensive analyses and decision-making based on data distributed across different databases, tables or files. 


Oracle's Business Intelligence Enterprise Edition (OBIEE) is a robust tool developed by Oracle Corporation for the collection, storage and visualisation of enterprise data. OBIEE is an open source platform that provides users with deeper insights. It also facilitates rapid decision-making through interactive dashboards, metadata search, real-time alerts and operational reports.

Business Intelligence illustration with laptop in a café - in the foreground a coffee cup and in the centre a data visualisation application on the laptop monitor - the logo of Alexander Thamm GmbH in the upper right corner.

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What is OLTP? 

OLTP is a database management approach that processes and records daily transaction activity. It facilitates the immediate execution of numerous database transactions by a large user base, often via the Internet. 

A database transaction in OLTP involves changing, inserting, deleting or querying data. It is the basis of our financial interactions, including online banking, in-store purchases, hotel reservations, flight bookings and much more. 

OLTP systems often rely on relational database management systems, chosen for their ability to handle numerous queries and updates while ensuring fast response times. This approach is proving to be effective, especially for frontline staff such as cashiers and bank clerks who conduct online transactions.

Online transaction processing systems typically utilise a three-tier architecture to support these functions. This includes:

  • Presentation levelThis level is responsible for the user interface (UI) and interactions and provides users with a platform for entering data or initiating transactions. User interfaces can range from traditional desktop applications to web-based interfaces for online transactions. They can differ in terms of design aesthetics, navigation structures or functionality to adapt to different platforms and devices. 
  • Business logic levelThis level processes the business logic and manages the data flow between the presentation and data levels. The transaction processing logic is implemented at this level to ensure the correctness and integrity of the data during updates. OLTP applications often contain validation, authorisation and error checking mechanisms to ensure accurate and secure transaction processing.
  • Data levelThe data layer is the foundation of OLTP and consists of a database management system (DBMS). The DBMS stores and retrieves data efficiently and supports the high volume of queries and updates required for OLTP applications.

The three-tier architecture of OLTP ensures a structured and efficient processing model. Each level works independently and contributes to the responsiveness and accuracy of the transaction data.

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Differences and similarities between OLAP and OLTP

As mentioned earlier, OLTP operates on the front end of day-to-day transactions, while OLAP helps to gain valuable insights and information. 

A closer look at the main features shows clear differences and illustrates how OLTP and OLAP work together to handle different aspects of data processing.

PurposeInformative - data analysis and reporting for strategic decisionsOperational - efficient processing of everyday transactions
Query typesComplex queries, drill-downs and data splitting for analytical insightsSimple queries for inserting, updating and deleting data records
Data sourceAggregated, historical data from various sources, including data warehousesCurrent, detailed transaction data from real-time processes
Response timeLonger response times, usually in seconds or minutesRequires fast response times for immediate processing of transactions
Data structure Multidimensional with cubesRelational databases with normalised tables
Data volume Manages large amounts of historical data, usually terabytes (TB) and petabytes (PB)Manages smaller amounts of current data, usually gigabytes (GB)
Data modelDenormalised, multidimensional model with star scheme, snowflake scheme or other analytical modelsNormalised models
Data backupRequires less frequent backupsFrequent backups and fast recovery to ensure consistency of transaction data
Space requirementRequires extensive storage space for historical and aggregated dataSpace-saving design to enable frequent transactions
Application examplesBusiness intelligence, data warehousing and reporting systemsOrder processing, inventory management and online banking systems
OLAP vs. OLTP: differences and similarities

OLAP and OLTP: a practical choice

The choice between OLAP and OLTP systems depends on the specific needs of the organisation and the nature of the tasks. OLAP is ideal for scenarios that require in-depth analyses, trend recognition and strategic decisions. It is ideal for applications such as business intelligence where historical data needs to be analysed. 
OLTP, on the other hand, is ideal for everyday processes such as order processing, inventory management and online banking, where immediate transaction processing with short response times is required. The two models are not mutually exclusive. Companies often use both models to effectively fulfil various operational and analytical requirements.



Pat has been responsible for Web Analysis & Web Publishing at Alexander Thamm GmbH since the end of 2021 and oversees a large part of our online presence. In doing so, he beats his way through every Google or Wordpress update and is happy to give the team tips on how to make your articles or own websites even more comprehensible for the reader as well as the search engines.

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