OLAP is a data technology that turns raw data into actionable insights in seconds. Everyone from sales managers to financial analysts can use natural language queries to analyze data from multiple sources and get detailed, strategic insights without waiting for reports or needing data science expertise. Cloud-based OLAP services are making this accessible even for small businesses—eliminating the need for expensive hardware and dedicated maintenance teams.
Key takeaways
- Many think OLAP is a database, but it's not; it’s a processing layer that sits on top of existing data warehouses to convert raw information into instant, multidimensional insights businesses can use immediately.
- OLTP vs. OLAP: Online transaction processing (OLTP) systems are designed for transactional workloads, such as processing sales orders or banking transactions, while OLAP systems are tailored for analytical workloads.
- There are three main models of OLAP—MOLAP (stores data in ready-to-use "cubes" for fast retrieval), ROLAP (queries the database in real time), and HOLAP (a hybrid of both).
- OLAP products include Microsoft Power BI, Tableau, IBM Cognos, and Oracle OLAP, and they have intuitive dashboards that make them easy to use.
Table of contents
- Is OLAP a database?
- What can you use OLAP for?
- OLAP vs. OLTP systems—what’s the difference?
- MOLAP, ROLAP, or HOLAP—which OLAP architecture should you choose?
- How do OLAP systems work?
- What operations can you perform with OLAP?
- What new capabilities do modern OLAP systems offer?
- What does the future of OLAP look like?
- FAQs
- Related resources
Is OLAP a database?
No. It’s often discussed in conjunction with data platforms, but OLAP is not a database (or a server). It’s a processing layer that sits on top of an existing relational database or data warehouse, converting business data into instant insights.
What can you use OLAP for?
Businesses use OLAP for four primary purposes: sales and marketing analysis, financial reporting and budgeting, business process management, and retail intelligence. OLAP allows users to ask the application strategic business questions like: “How did our sales perform by region last quarter?” or “What product categories are trending this year?”—and the application returns actionable analytics, trend analysis, and interactive, dynamic charts that would've taken a data team hours or days to produce manually.
Popular use cases for OLAP software include:
- Sales and marketing analysis: Tracks your product performance, identifies seasonal trends, and runs comparisons of sales channels.
- Financial reporting, planning, and budgeting: Compares actual vs. budget performance, forecasts future trends based on historical data, and analyzes profitability across your business segments.
- Business process management: Monitors key performance indicators in real-time, identifies bottlenecks in your operations, and tracks the impact of process improvements.
- Retail intelligence: Analyzes product performance by attributes like color and size, and evaluates how placement affects sales.
Related: See how Zebra technologies is utilizing operational data on MongoDB Atlas in combination with OLAP warehousing on Google BigQuery
OLAP vs. OLTP systems—what’s the difference?
OLAP and online transaction processing (OLTP) are different, and they each have a separate job to do. OLTP handles real-time transactions like orders and payments. OLAP analyzes that transaction data to answer strategic questions. Both are essential—OLTP captures the data, OLAP interprets it.
MOLAP, ROLAP, or HOLAP—which OLAP architecture should you choose?
OLAP systems offer three main architectural approaches, each optimized for different business needs and data scenarios. The right choice depends on your data patterns, query complexity, and performance priorities.
What is MOLAP and when should you use it?
MOLAP—multidimensional OLAP—is the fastest type of multidimensional data analysis and stores data in pre-aggregated cubes for lightning-fast queries. Use MOLAP when you need quick analysis of known data patterns, such as financial analysis, sales forecasts, and budgeting. It works best with stable, predictable data.
What is ROLAP and when should you use it?
ROLAP—relational OLAP—performs analysis directly on relational database management systems (RDBMS); it doesn't rely on pre-aggregated cubes like MOLAP. Use ROLAP when you need flexibility for complex, ad-hoc queries across multiple dimensions. It’s slower than MOLAP but more adaptable to complex data relationships.
What is HOLAP and when should you use it?
HOLAP—hybrid OLAP—combines the benefits of both MOLAP and ROLAP. Use HOLAP when you need speed for standard reports and flexibility for complex queries. It offers the best of both approaches.
How do OLAP systems work?
Regardless of which architecture you choose—ROLAP, MOLAP, or HOLAP—all OLAP systems follow the same workflow. Here’s how raw data is prepared to provide actionable insights.
Step 1: Getting data ready for OLAP—ETL
Before OLAP analysis can occur, raw data from multiple data sources across your organization must be collected, cleaned, and organized using ETL (extract, transform, load). This foundational step prepares your data for fast analytical processing.
How does the extraction, transformation, and loading (ETL) process work?
- Extraction pulls data from multiple sources—transactional databases like MongoDB, relational databases, spreadsheets, IoT devices, web analytics, and other business systems—into one central location.
- Transformation cleans and standardizes your data by correcting inconsistencies, filling in missing information, and converting it into consistent formats. For example, it ensures all dates follow the same format and customer names remain consistent.
- Loading moves the cleaned data into a centralized data warehouse designed specifically for online analytical processing rather than for day-to-day operations.
How do you structure data for OLAP analysis?
Once the data is loaded in the data warehouse, it's ready for the next step: choosing the schema (or structure). This choice is critical because it determines how effectively the OLAP system produces the types of results your business expects and needs.
The most common approach to data analysis is dimensional modeling, which organizes data into two primary components: fact tables and dimension tables.
- A central fact table contains quantitative, measurable data related to a business event, such as sales transactions.
- Dimension tables connect to the fact table and contain descriptive attributes that provide context, such as time, products, customers, and locations.
The two most prevalent dimensional models used for OLAP are the star schema and the snowflake schema. The choice between them represents a trade-off between query performance and simplicity on one hand and storage efficiency and data integrity on the other:
- Use a star schema for speed and simplicity.
- Use a snowflake schema for more complex data structures where efficiency is important.
Step 2: Working with OLAP
Once your raw business data is structured and safely stored in your data warehouse, it's ready for OLAP. The OLAP system operates in three interconnected tiers to transform that data into fast, flexible analysis.
How does OLAP process data? A three-tier process
Tier 1: Data storage and organization
This tier takes your warehouse data and processes it again to prepare it for online analytical processing and analysis. During this stage, data is organized in relational tables (ROLAP), converted into specialized multidimensional OLAP cubes (MOLAP), or uses a combination of approaches (HOLAP), depending on your business needs.
Tier 2: Query processing
Queries may use pre-built OLAP cubes in MOLAP systems or SQL queries in ROLAP systems. HOLAP combines MOLAP and ROLAP approaches to achieve a balance of speed and flexibility. Regardless of the approach, the goal is the same: to give you the ability to switch between different views of your data, compare performance across categories such as time or location, and drill into details without constructing complex queries or waiting for manual reports.
Tier 3: User interaction
This tier lets you put your business intelligence (BI) tools and analytics platforms to work. Most BI software is intuitive, allowing you to easily create dashboards, generate reports, and explore data through visualizations, regardless of how the data is stored (OLAP cubes or virtual relational database models).
Related: see how Carrefour is utilizing MongoDB Atlas for transactional workloads and using Google BigQuery as an analytics database
What operations can you perform with OLAP?
OLAP lets you integrate data in various ways for multidimensional analysis and predictive analytics—a powerful reporting tool that would traditionally require data scientists or data analysts to perform.
The following backend OLAP operations makes these complex analytics possible:
- Slice: Selects a subset of data from the MOLAP data cube based on specific criteria or values of one or more dimensions, which lets you quickly examine a particular segment of the data. For example, you can examine sales data for Q2 only or review customer data for a specific region.
- Dice: Similar to slice, but involves creating a new data cube by selecting specific dimensions and their corresponding values. It allows you to examine data across multiple dimensions simultaneously, such as analyzing sales in a particular city and a specific product. For example, you could filter the data to examine electronics sales in Texas stores, specifically during the fourth quarter.
- Drill-down or drill-up: These operations expand the view, allowing you to see more specific information and different levels of granularity. For example, you can zoom in to examine more specific details (e.g., moving from annual sales to daily performance) or zoom out to view a broader overview (e.g., from daily sales to yearly trends).
- Pivot: This rotates the axes of the multidimensional data cube to view different dimensions, which allows you to analyze data from alternate perspectives. For example, you might view sales by region, then pivot to look at sales by product category instead.
How do these operations work together in practice?
You might start with annual sales data (high level), drill down to see the monthly performance, slice to focus on just Q4, dice to examine electronics' sales in your top three regions during that period, drill down to daily trends, then pivot to explore the same data organized by product category instead.
These fundamental OLAP operations are the foundation for all systems, but modern OLAP platforms are doing far more than fundamental multidimensional analysis.
What new capabilities do modern OLAP systems offer?
As data volumes grow, OLAP systems are growing, too. Cloud data warehouse capabilities, cloud-native platforms, real-time analytics, and seamless integrations with business intelligence (BI) tools make it easier to scale, respond to business changes, and generate insights on demand. It's like having a team of data engineers or data scientists at your disposal.
- Cloud-native OLAP platforms like ClickHouse, Google BigQuery, and Snowflake offer fast and scalable solutions for modern businesses.
- Real-time OLAP allows businesses to make decisions based on current conditions.
- BI tool integration—OLAP systems are designed to seamlessly integrate with popular BI tools like Tableau, Power BI, MongoDB Atlas, and ThoughtSpot.
MongoDB Atlas can support analytics workflows by integrating with OLAP tools, making it easier to bring document-based data into your business intelligence stack.
What does the future of OLAP look like?
AI and augmented analytics are democratizing OLAP. Machine learning now automates complex analytical calculations and predictions, while natural language summaries and embedded intelligence bring powerful insights directly into business applications—no data scientist required. This means non-technical users across your organization can tap into real-time insights across any data type, making faster, more accurate decisions from relational databases to flexible document models like MongoDB Atlas.