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OLAP in a data warehouse:
an end-to-end technology overview
December 30, 2025
OLAP in a data warehouse is the technology that enables organizations to analyze large volumes of business data from multiple perspectives. This way, online analytical processing helps consolidate and structure dispersed data to make complex analysis both simple and fast to derive actionable insights.
Providing data warehousing services for over 15 years, Itransition delivers high-performing, scalable, and secure data storage solutions and equips them with OLAP capabilities to enable detailed data analysis and informed decision-making.
Critical elements of an analytical system architecture with OLAP
ETL/ELT tools
Extract, transform, and load (ETL) and extract, load, and transform (ELT) tools are used to automatically ingest information from various data sources, clean, and standardize it to prepare it for storage in a data warehouse and processing by OLAP tools.
Data warehouse
A data warehouse is an analytical data store that contains current and historical data gathered from corporate SQL/NoSQL databases, files, business software, and data sources outside the company. It organizes data to simplify further querying and analytics and contains a database, as well as a metadata repository and management system, making it easier for people and software to better understand, categorize, and locate the data.
OLAP server
An OLAP server is the engine that powers the OLAP system and organizes data from the data warehouse into required data structures. It also processes user queries, performing analytical calculations and enabling roll-up, drill-down, slice, dice, and pivoting operations.
OLAP cubes & database
An OLAP cube represents a precalculated, multidimensional data model that includes predefined dimensions, hierarchies, and measures organized along a three-way axis. An OLAP cube can have more than three dimensions to provide more details about the data. In this case, it is sometimes called a hypercube.
Analytical tools
The analytical layer comprises tools for users, such as SQL clients, pivot tables, reporting tools, and data mining tools, enabling them to query, analyze, and visualize data, discover valuable insights, and build reports.
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OLAP data models
The following models define how information is structured in data warehouses that organize it into fact and dimension tables, enabling analysts to build multidimensional data models based on them for OLAP analysis.
Star schema
The star schema consists of a single, central fact table that contains measurable, quantitative data about a business, which is surrounded by many dimension tables with descriptive attributes related to fact data. Such an arrangement resembles a star constellation, which is why it’s called a star schema.
Image title: Star schema
Snowflake schema
In a snowflake schema, there is a central fact table, but dimension tables can be normalized into several related tables, forming a shape similar to a snowflake. An extension of the star schema, the snowflake schema breaks the data down into greater detail.
Image title: Snowflake schema
Galaxy schema
A galaxy schema, also referred to as a fact constellation schema, is a collection of star schemas. It contains several fact tables that share one or more dimension tables. This schema has a more complex structure than a star schema and a snowflake schema, is used for data analysis across complex datasets, and allows integration of cross-functional data that mirrors dynamic business ecosystems.
Image title: Fact constellation schema
OLAP vs OLTP: an in-depth comparison
OLAP and online transaction processing (OLTP) represent two primary approaches to processing business data. However, OLTP and OLAP differ in terms of the tasks they perform, with the former designed for transactional workloads, while the latter is aimed at in-depth data analysis and calculating aggregate statistics.
OLTP systems are designed to handle day-to-day business operations, including processing orders, updating inventory, recording payments, and managing user accounts. OLAP systems, on the other hand, are built for providing big-picture insights, including analyzing sales patterns over time, comparing regional performance, forecasting future trends, or identifying which customer segments are most profitable. The following table summarizes the differences between OLAP and OLTP systems.
OLAP | OLTP | |
|---|---|---|
Purpose | Multidimensional analysis of data | Processing multiple streams of everyday transactions |
Data type | Both transactional and historical summarized data | Up-to-date, primitive, and highly detailed transactional data, such as financial records, service subscriptions, and customer feedback |
Database optimization type | Denormalization, which involves adding redundant copies of data or grouping data for enhanced query performance | Normalization, which entails eliminating redundant data and storing records in a clean and consistent structure |
Data storage | Column-oriented storage | Row-oriented storage |
Database size | Larger amounts of data, reaching terabytes or more | Smaller amounts of data |
Number of records processed | Millions of rows of data at a time | A few rows of data per query |
Data processing speed | Seconds to minutes (sometimes hours) | High (sub-second response) |
Data processing frequency | Periodic, ad-hoc (on-demand or scheduled) | Continuous, high-frequency (thousands per second) |
Query type | Complex queries | Standardized and simple |
Updates | Scheduled, periodic updates | Real-time or near-real-time synchronization |
Backups | Less frequent backups | Regular backups |
Number of users | Tens to hundreds due to lower concurrency | Thousands to millions or more, thanks to high concurrency |
User types | Executives, managers, data scientists, and analysts | Customers and employees |
Use cases | Business intelligence, decision support, and business performance and trend forecasting | Processing of data from banking applications, ATMs, ecommerce software, credit card payment gateways, reservation systems, and other sources of transactional data |
Key types of OLAP systems
There are three main types of OLAP systems that store and process data differently, each with its use cases, advantages, and disadvantages.
ROLAP
Instead of relying on precalculated data cubes, relational OLAP tools store base data and dimension tables as relational tables, enabling users to perform multidimensional data analysis directly on a relational database using SQL queries.
Benefits
- Support for any type of query, as there are no predefined cubes
- Ability for people to drill down to the lowest level of detail
- Great scalability to handle large volumes of nuanced data
- Simplifies data structure changes
Limitations
- Slower query performance compared to MOLAP
MOLAP
Multidimensional OLAP represents a classic form of OLAP that presents a multifaceted view of data. The data is pre-computed, pre-summarized, and stored in data cubes that contain answers to a predefined set of inquiries.
Benefits
- Near-real-time responses to user queries
- Support for complex calculations
Limitations
- Limited flexibility, since data dimensions can be changed only by rebuilding the entire data cube
- Limited default data storage capacity
HOLAP
Hybrid OLAP unites MOLAP and ROLAP in a single architecture. Specifically, data aggregations are stored in data cubes for quick retrieval, while large amounts of data that is highly detailed are kept in relational tables.
Benefits
- Provides the benefits of both ROLAP (detailed analysis of large data quantities) and MOLAP (fast query performance)
- Enables users to switch from the cube to relational tables
Limitations
- Complex architecture to build and manage due to the need to support two types of data structures
- Expensive to deploy and maintain
- Requires more storage space
- More difficult to sync data between MOLAP and ROLAP servers
Key OLAP operations in a data warehouse
An OLAP system facilitates several critical analytical operations that enable users to access information at different granularity levels and perform complex calculations.
Roll-up
The roll-up operation summarizes data by moving up a concept hierarchy or reducing the number of dimensions to create a general overview of the data, allowing users to assess overall corporate health, market trends, and customer information.
Example
Grouping data by country rather than by city
Drill-down
The drill-down operation involves moving down in the concept hierarchy or adding a new dimension to the OLAP cube, allowing users to get more detailed insights. This operation is useful for identifying root causes of certain events, measuring more specific business metrics, and facilitating efficient problem-solving, risk mitigation, and business planning.
Example
Viewing information by month rather than by year
Slice
Slicing is aimed at extracting a specific set of data from the OLAP cube and presenting it in a two-dimensional, spreadsheet-like format. This operation allows companies to get a specific context around the data to spot deviations and assess particular aspects of the business.
Example
Analyzing sales data by salesperson, date, customer, product, or region
Dice
Similar to slicing, the dice operation is used to isolate a certain set of data necessary for the given analytical task from the OLAP cube. However, dicing involves creating a smaller sub-cube on the basis of the original OLAP cube to analyze data from multiple dimensions simultaneously.
Example
Highlighting all sales data about a certain product for a particular period in a given region
Pivot
When pivoting, or rotating the data cube, data analysts reorient it to get an alternative perspective on the data model. This involves changing axes to explore the data from a different angle, enabling users to uncover trends overlooked in the original model.
Example
Moving from comparing product sales data across different stores to analyzing store sales data by product
Major benefits of the OLAP technology
Comprehensive data analysis
By breaking down vast volumes of data into different dimensions, OLAP systems enable users to perform more in-depth data analyses than traditional data processing tools can support.
Fast data querying & analysis
Thanks to aggregating and precalculating data, OLAP systems enable faster data querying and analysis compared to multiple relational databases.
Data democratization
OLAP systems enable users to explore data and generate reports without writing SQL queries, making it possible for non-technical staff, like business managers, to also perform data analysis.
Unified business view
OLAP systems aggregate massive volumes of versatile data, providing an all-encompassing view of business operations, market trends, and customer behavior and enabling users to see how decisions made by different teams impact the overall business performance.
Leading OLAP technologies to consider
Microsoft Fabric
Microsoft Fabric is a unified, cloud-based data management and analytics platform that provides end-to-end data services, including data ingestion, preparation, storage, analysis, and visualization, and uses AI capabilities to automate each process.
Key capabilities
- Seamless connectivity to Microsoft Azure, Power BI, and Microsoft 365 applications, such as Excel and Teams, to create a seamless user experience
- Customizable tools and interfaces for different user roles, including database administrators, data scientists, data engineers, and business analysts
- Built-in software-as-a-service, multi-cloud data lake called OneLake that offers a centralized data storage option
- Integrated AI assistant that assists with code writing, insight generation, and task automation
- Out-of-the-box AI models and tools for creating custom machine learning solutions
Amazon Redshift
Amazon Redshift is a fully managed data warehouse with OLAP capabilities designed to store massive amounts of data and execute complex data analysis tasks through SQL-based queries and business intelligence tools.
Key capabilities
- Support for real-time analytics through zero-ETL integrations and streaming data ingestion
- Seamless scalability of compute resources in line with increasing analytics workloads enabled by Redshift Serverless
- Ability to run SQL queries directly on data located in Amazon S3
- Compatibility with BI tools, such as Amazon QuickSight, Tableau, Microsoft Power BI, and Looker
- Ability to integrate Amazon Bedrock and SageMaker to invoke AI-powered models for advanced natural language processing tasks, such as text summarization, entity extraction, and sentiment analysis
Google BigQuery
BigQuery is a fully managed data warehouse that supports online analytical processing, automating the entire data lifecycle, from ingestion to insight generation.
Key capabilities
- Support for natural language-based data discovery, transformation, query, and visualization
- AI-based suggestions for data enrichment, AI-powered text generation and summarization, and automated data error identification and remediation
- Forecasting capabilities
- Built-in streaming capabilities for real-time data analysis
- Automatic failover coordination, ongoing near real-time data replication to a backup region, and recovery during outages
- Off-the-shelf data governance capabilities, such as automatic metadata harvesting, data profiling, data quality management, lineage tracking, and AI-assisted metadata augmentation
IBM Cognos Analytics
IBM® Cognos® Analytics is a business intelligence solution that enables end-to-end data analysis and facilitates the creation of OLAP data structures. The platform’s capabilities include data modeling, analysis, visualization, exploration, storytelling, and reporting, all delivered through a user-friendly interface.
Key capabilities
- Seamless connectivity to diverse data sources, such as SAP BW/4HANA, Microsoft Fabric, and Oracle
- Native integration with IBM® Cognos® Cube Designer and IBM Cognos Transformer for ROLAP and multidimensional modeling
- Built-in AI-powered pattern identification and anomaly detection in data, trend analysis, and forecasting
- Drag-and-drop dashboard creation
- AI assistant that interacts with users in natural language
- Self-service data analytics capabilities
- Robust security features, including user authentication and authorization, data encryption, and fine-grained access permissions
Best practices for implementing OLAP-based systems
Select the right data modeling approach
To choose the appropriate data model for organizing the data in a data warehouse, begin by identifying your data analytics requirements and query patterns.
If you prioritize query performance and simplicity, use a star schema, as it involves fewer complex joins and offers faster response times during data analysis. When data integrity and storage efficiency are more important, especially when dealing with intricate hierarchical dimensions, choose a snowflake structure. If you need to analyze data pertaining to many interconnected business processes, select the galaxy schema.
Ingest only essential data
Limit data ingestion to only the one you need to perform the analytical task to consume fewer storage resources. When creating OLAP cubes, identify which fields, records, and historical data is truly necessary for your business intelligence needs. Also, apply filters during the ingestion process to remove irrelevant data at the source rather than storing and filtering it later. Additionally, regularly audit your cubes to eliminate unused dimensions, measures, and data that no longer serves active analytical purposes.
Regularly review & optimize the OLAP system performance
OLAP system performance depends on its models, so one of the strategies to ensure the efficiency of the OLAP system in the long run is to regularly optimize model design by applying denormalization and pre-calculating common aggregations.
For the OLAP system to return the results rapidly, you can also employ database partitioning and sharding, use columnar compression techniques, leverage and optimize indexing strategies, and rewrite complex queries. Additionally, consider caching frequently run queries to accelerate task execution.
Leverage data history tracking
Data history tracking is a data management approach that enables data engineers to keep track of changes in the data stored in a data warehouse, which is essential for accurate historical reporting and trend analysis. To enable data history tracking, slowly changing dimensions (SCDs) are used that capture and record current and historical data over time. For example, Type 2 SCDs create another dimension record, keeping the full history of values, while Type 3 SCDs store previous and recent versions of values simultaneously.
Another way to monitor changes in data is snapshotting. This strategy involves automatically capturing what a dataset in a data warehouse looked like at a specific point in time, creating an audit trail of data history. Thanks to snapshotting, data analysts can see how the data evolved with time, which helps them analyze historical trends and quickly restore data in the event of data corruption or loss.
Want to get a tailored online analytical processing solution?
Itransition’s data analytics services
We provide advisory and practical support to help companies deploy robust analytical solutions with OLAP capabilities to organize business data and extract meaningful insights from it.
Consulting
We provide advisory services, helping you define analytical needs, identify data management issues, evaluate possible analytical tools, and prioritize solution functionality that supports your goals. As part of our service offering, we develop a holistic strategy for implementing data analytics solutions and provide guidance throughout the project, from architecture design to solution development, configuration, customization, and post-launch support, ensuring that the resulting solution brings the expected value.
Implementation
Itransition’s specialists deliver data analytics solutions tailored to your analytical needs. We begin our cooperation with a thorough analysis of your analytical expectations and current issues, as well as a data management infrastructure research, and proceed with implementing the data analytics solution. We develop custom solutions or configure and customize platform-based ones, establish efficient data pipelines, and set up robust data governance and security mechanisms. Post-launch, we conduct user training and onboarding, as well as monitor and support the analytical solution on demand to ensure its efficiency and reliability.
Derive deeper insights from your data with OLAP
OLAP in a data warehouse enables companies to structure various data types from heterogeneous sources and simplifies its understanding and usage. OLAP systems organize data in a multidimensional format in the form of OLAP cubes, allowing businesses to analyze it in a logical and orderly manner to support business decision-making.
However, implementing a data warehouse, and OLAP in particular, can be a challenging task that needs careful planning, execution, and support. At Itransition, we offer our multidisciplinary expertise to help you implement a high-performing DWH equipped with OLAP technology.
FAQs
The process of analyzing data using OLAP systems can be broken down into the following steps:
- Data collection from various sources, such as transactional databases and external systems
- Transformation, cleansing, and enrichment of the extracted data
- Loading of the data into a centralized data warehouse
- Formatting the data into OLAP cubes for fast and efficient querying
- Deploying the OLAP cubes to an OLAP server that performs initial calculations to prepare the data for further analysis
- Enabling users to pull the data from the OLAP cubes and run roll-up, drill-down, slicing, dicing, and pivoting operations
OLAP plays a vital role in business intelligence, decision support, business forecasting, and reporting across various fields, such as banking and finance, healthcare, insurance, retail, and manufacturing. The technology is also applied in different business units, including marketing, finance, sales, supply chain management, and human resources.
OLAP refers to enabling multidimensional analysis of summarized data. Data mining, in turn, involves using machine learning and statistical models to uncover hidden patterns and relationships between data points and making accurate predictions of future trends. Data mining tools can use OLAP data, which is structured, cleansed, and aggregated by an OLAP system, to carry out analytical functions with greater speed and accuracy.
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