What is OLAP (online analytical processing)?

OLAP (online analytical processing) is a technology for fast, multidimensional data analysis. It organizes data in so-called cubes, which can be pre-aggregated and queried simultaneously from multiple perspectives. OLAP provides an important technological foundation for powerful business intelligence applications, reporting, and business management.

Characteristic	Details
Category	Database architecture / business intelligence / data analysis
Application areas	Controlling, financial analysis, sales management, management reporting, BI platforms
Typical use cases	Variance analysis, sales analysis, planning and forecasting, KPI dashboards, drill-down analysis
Related terms	Data warehouse, ETL, OLTP, MDX, business intelligence, multidimensional data model
Benefits	Fast query response times, flexible multidimensional analysis, consistent KPIs, scalable analytics

At a glance

Enables multidimensional analysis across dimensions such as time, region, and product.
Data is pre-aggregated and stored in OLAP cubes to enable fast query response times.
Queries can be performed using specialized languages such as MDX or SQL.
Three main variants are MOLAP, ROLAP, and HOLAP.
Provides a foundation for business intelligence, controlling, and decision support in organizations.

OLAP definition

OLAP stands for “online analytical processing.” It refers to a technology that enables fast and comprehensive analysis using multidimensional data structures, allowing data to be viewed from different perspectives. OLAP data structures are specifically designed to process complex queries efficiently by pre-aggregating data and storing it in a form optimized for analysis. OLAP databases are therefore particularly useful for business intelligence applications that require fast insights and detailed reports on business data.

OLAP typically uses a denormalized data structure designed to reduce query response times. OLAP systems use specialized data structures, such as data cubes, to enable fast queries and in-depth analysis. By handling complex queries efficiently, OLAP systems help organizations make informed decisions and improve strategic planning.

What is an OLAP cube?

An OLAP cube, a central element of OLAP technology, is a multidimensional data structure that enables complex data to be analyzed quickly and efficiently from different perspectives. Unlike conventional two-dimensional tables, an OLAP cube provides a multidimensional structure that combines dimensions such as time, location, and product category. This multidimensionality facilitates complex OLAP queries and fast analysis.

Various OLAP operations can be performed on OLAP cubes. With “slice and dice” operations, users can filter specific data or change perspectives to gain new insights. For example, a cube can be “sliced” to display a specific subset of data, or “diced” to select a smaller subset across multiple dimensions and analyze several dimensions simultaneously. OLAP cubes also provide powerful functions such as drill-down and roll-up, which allow users to examine data at a more detailed level or view it at a higher level of aggregation. A drill-down can be used to move from an overview to detailed data, such as sales figures for individual branches. A roll-up, by contrast, aggregates the data to provide a higher-level view, such as sales at country level.

Operation	Description	Example
Slice	Filtering by a single dimension	Display sales for the year 2024 only
Dice	Selecting a sub-cube across multiple dimensions	Sales for the northern region, product A, Q1–Q2
Drill-down	Moving to a more detailed level	From annual sales to monthly sales by branch
Roll-up	Aggregating to a higher level	From branch data to country-level data
Pivot	Rotating the perspective	Switching rows and columns to obtain a different view

Overall, OLAP reduces the need for complex SQL queries and can minimize the number of JOIN operations, thereby improving query performance. Although OLAP cubes are now partly complemented by modern data warehousing technologies, they remain an important tool in business intelligence, enabling organizations to make informed decisions quickly.

How does OLAP work?

A typical OLAP process consists of three phases:

Data extraction and transformation: Data is typically extracted from various data sources, such as relational databases and data warehouses, using ETL processes and loaded into an OLAP database.
Loading data into the OLAP database: The OLAP server then works with the loaded and structured data. The data is cleaned and aggregated using ETL tools.
Querying and analysis: The data is organized in an OLAP cube that incorporates multiple dimensions. Users or BI systems query the data using specialized languages, typically MDX (Multidimensional Expressions) for multidimensional models or SQL for relational OLAP variants.

The OLAP server answers queries based on precomputed aggregations rather than performing computationally intensive calculations on raw data in real time. This makes OLAP particularly powerful for complex analyses across large volumes of data.

What types of OLAP are there? – ROLAP, MOLAP, HOLAP

OLAP systems use different approaches to data storage and analysis. The three main types are MOLAP, ROLAP, and HOLAP:

Type	Full name	Data storage	Strengths	Weaknesses
MOLAP	Multidimensional OLAP	Proprietary multidimensional cubes	Very fast queries, pre-aggregated data	Time-consuming cube creation, limited flexibility when dimensions change
ROLAP	Relational OLAP	Relational database	High flexibility, large data volumes	Slower queries, strong dependency on database performance
HOLAP	Hybrid OLAP	Combination of cube and relational storage	Combines speed and flexibility	More complex implementation and maintenance

MOLAP is the classic approach in which data is stored in multidimensional OLAP cubes. These cubes provide fast query response times because the data is pre-aggregated and organized. However, cube creation and preprocessing can be time-consuming, and changes to dimensions may require the cubes to be reprocessed.
ROLAP works directly with relational databases without first transforming the data into OLAP cubes. ROLAP is well suited to large and highly detailed datasets, although query performance can be slower than with MOLAP. Performance also depends heavily on the underlying relational database.
HOLAP combines elements of both approaches by using a hybrid architecture in which some data is stored in OLAP cubes while other data remains in relational databases. This allows it to combine the fast calculations of MOLAP with the flexibility of ROLAP. However, the hybrid architecture can make HOLAP more complex to implement and maintain.

Overall, each OLAP approach offers specific advantages: MOLAP for fast, complex OLAP queries; ROLAP for processing large volumes of data without cubes; and HOLAP for a balanced combination of speed and flexibility. The choice of OLAP architecture depends on factors such as data volume, query complexity, real-time requirements, and the existing infrastructure.

OLTP vs. OLAP: what is the difference between OLAP and OLTP?

OLAP and OLTP (online transaction processing) are two approaches to data processing that are used in different scenarios. The main difference is their purpose: OLTP is designed for transaction processing, while OLAP is designed for analysis.

Characteristic	OLAP	OLTP
Purpose	Analysis and decision support	Operational transaction processing
Typical users	Controllers, analysts, management	Clerical staff, operational systems
Data structure	Denormalized, aggregated	Normalized, optimized for transactions
Query type	Complex, multidimensional analyses	Simple, fast individual transactions
Data volume	Historical, large-scale	Current, granular
Updates	Periodic, e.g. daily	Continuous, typically in real time
Examples	Sales analysis, budget comparisons, KPI dashboards	Accounting entries, orders, payment transactions

OLTP systems process large numbers of transactions quickly and efficiently and are used in real-time applications such as banking transactions and e-commerce purchases. OLTP databases store current, transaction-based data and are optimized for high transaction rates and low latency. They use normalized data structures that minimize redundancy and make data storage efficient. Because OLTP systems are critical to day-to-day business operations, they are regularly backed up to minimize downtime and ensure data security.

OLAP systems, by contrast, are optimized for the extensive analysis of large datasets. They support complex, multidimensional queries for reports, data analysis, and business intelligence applications. OLAP systems often obtain their data from OLTP databases. This data is transformed through ETL processes into a format optimized for analysis. OLAP typically uses a denormalized data structure designed for speed and analytical efficiency. OLAP systems are primarily used for analytical purposes and are therefore updated less frequently.

OLTP systems are therefore designed to support business operations by processing transactions quickly and accurately, while OLAP systems help organizations understand their business and make strategic decisions. In many cases, both types of systems are used in parallel to meet both operational and analytical requirements.

Bissantz and OLAP

Bissantz uses the multidimensional model as a foundation. The reason is a functional one: business management is inherently multidimensional. Sales are analyzed by time, region, product, and channel. Costs are analyzed by cost center, cost type, and period. Plan values are analyzed by scenario, version, and organizational unit. An OLAP model represents precisely this structure, and DeltaMaster uses it.

Bissantz’s particular approach is to make OLAP analysis automated, visual, and easy to understand. Instead of requiring users to work with complex MDX formulas or perform manual “slice and dice” operations, DeltaMaster provides functions such as automated analysis, graphical tables, and KPI dashboards that perform typical OLAP operations elegantly in the background.

A central design principle is the red-blue logic. Through intelligent use of color, typographically scaled numbers, and sparse but precise visualizations, users can immediately identify where deviations, opportunities, or risks exist without having to deal with the underlying OLAP technology.

In this way, Bissantz combines the computational power of OLAP with the requirements of modern business intelligence: intuitive to use, decision-oriented, and visually compelling. OLAP remains in the background as an architecture, providing a proven foundation for multidimensional business management.

FAQ – frequently asked questions

What is OLAP in simple terms?

OLAP is a technology that enables large volumes of data to be analyzed from different perspectives at the same time – similar to a cube that can be rotated to view it from different sides. Instead of working with a flat table of rows and columns, OLAP uses multidimensional structures. A controller can use these structures to analyze sales simultaneously by time, region, and product category – quickly, flexibly, and without complex database queries.

What is MDX and what is it used for in OLAP?

MDX (Multidimensional Expressions) is a specialized query language for OLAP systems, comparable to SQL for relational databases. MDX can be used to formulate multidimensional queries that retrieve data from specific dimensions and hierarchies within an OLAP cube.

Is OLAP still relevant with modern in-memory databases?

Yes. The principles of OLAP remain central to analytical systems. Modern in-memory technologies such as Microsoft Analysis Services, SAP HANA, and similar platforms have significantly increased OLAP performance without replacing the fundamental concept of multidimensional analysis. Many current BI platforms, including DeltaMaster, build on OLAP principles while also using modern storage and processing technologies.

How does Bissantz use OLAP?

Bissantz automates typical analytical operations such as drill-downs and variance analysis, presents results visually according to clear design principles, and extends OLAP analysis with AI-supported commentary and natural-language queries. The goal is not to make users operate an OLAP cube, but to provide direct support for business decisions.

Summary

OLAP is a proven technological foundation for multidimensional data analysis in business intelligence and controlling: fast queries, flexible changes of perspective, and consistent KPIs across dimensions. The distinction between MOLAP, ROLAP, and HOLAP allows the architecture to be adapted to different requirements. Organizations that use OLAP consistently create the foundation for reporting that does more than provide data – it enables better decisions.