What Is OLAP In Data Warehouse: Data is the new oil, and businesses today are driven by insights extracted from massive datasets. But raw data alone is not enough; organisations need advanced tools to analyse, interpret, and make sense of data.
This is where OLAP (Online Analytical Processing) comes in data warehousing and business intelligence. It allows users to perform complex queries, generate reports, and analyse trends efficiently. But what exactly is OLAP in data warehouse, and how does it work?
In this blog, we will explore what is OLAP in data warehouse, its types, operations, tools, and architecture, helping you understand how businesses use OLAP to make data-driven decisions.
What is OLAP in Data Warehouse?
OLAP Full Form in Data Warehouse
OLAP stands for Online Analytical Processing.
It is a data processing technique that allows users to interact with large datasets in a multi-dimensional manner, helping them extract meaningful insights. Now that you know the OLAP full form in data warehouse, let’s learn what it is really!
Definition of OLAP in Data Warehousing
According to Ralph Kimball, a pioneer in data warehousing:
“OLAP provides users with the ability to analyse large amounts of data interactively, enabling quick and insightful decision-making.”
In simple words, OLAP in data warehouse is a system that helps businesses analyse historical and real-time data efficiently, enabling users to perform complex queries, drill-down reports, and generate dashboards.
How Does OLAP Work in Data Warehousing?
- OLAP processes data stored in a data warehouse and organises it into multidimensional structures called OLAP cubes.
- Users can explore data from different angles (dimensions) such as time, geography, product categories, sales, and customers.
- OLAP helps in decision-making by providing real-time insights into sales performance, trends, and forecasting.
Example: Imagine a retail company wants to analyse monthly sales data across different regions. Instead of writing complex SQL queries, they can use OLAP tools to quickly generate reports, compare data, and identify trends effortlessly.
Types of OLAP Servers in Data Warehouse
OLAP systems can be classified into three major types based on how they store and process data:
1. MOLAP (Multidimensional OLAP)
- Stores data in a multidimensional cube format rather than relational databases.
- Provides fast query performance because data is pre-aggregated.
- Best for complex analytical queries and high-speed data retrieval.
Example: Microsoft Analysis Services (SSAS) is a widely used MOLAP server.
2. ROLAP (Relational OLAP)
- Uses relational databases (RDBMS) like MySQL, Oracle, or PostgreSQL.
- Queries are processed dynamically instead of pre-aggregating data.
- Best for handling large datasets with flexible query execution.
Example: IBM Cognos, SAP Business Objects use ROLAP.
3. HOLAP (Hybrid OLAP)
- A combination of MOLAP and ROLAP techniques.
- Stores frequently used data in MOLAP cubes for fast access.
- Stores detailed data in ROLAP databases for deep analysis.
Example: Microsoft’s SQL Server Analysis Services (SSAS) supports HOLAP.
OLAP Operations in Data Warehouse
OLAP enables users to perform several data analysis operations:
1. Roll-up (Aggregation)
- Summarises data at a higher level by grouping values.
Example: Aggregating daily sales data to monthly sales.
2. Drill-down (Detailed Analysis)
- Moves from summary-level data to more detailed data.
Example: From yearly sales → quarterly sales → monthly sales → daily sales.
3. Slice (Filter a Dimension)
- Selects a single dimension from a cube to analyse specific data.
Example: Viewing sales data only for “India”.
4. Dice (Filter Multiple Dimensions)
- Applies multiple filters on data for in-depth analysis.
Example: Analysing “January sales” for “Electronics category” in “Mumbai region”.
5. Pivot (Rearrange Data)
- Changes the view of data by rotating dimensions.
Example: Switching between “Sales by Region” to “Sales by Product Category”.
These OLAP operations in data warehouse provide users with powerful information to improve business strategies.
OLAP Architecture in Data Warehouse
The OLAP architecture in data warehouse consists of the following:
- Data Sources – Extracts data from multiple databases and applications.
- ETL (Extract, Transform, Load) – Cleans and loads data into a data warehouse.
- Data Warehouse – Central storage for business data.
- OLAP Server – Performs processing and multidimensional analysis.
- OLAP Client – Dashboards, reports, and visualisation tools used by business users.
This architecture ensures efficient data processing, quick query execution, and insightful reporting.
Popular OLAP Tools in Data Warehouse
Some of the widely used OLAP tools in data warehouse include the following:
- Microsoft Power BI – A powerful business intelligence tool for OLAP reporting.
- IBM Cognos Analytics – A comprehensive OLAP and data analysis solution.
- SAP BusinessObjects – Advanced OLAP tool for enterprise analytics.
- Oracle Hyperion – A high-performance OLAP system for financial reporting.
- Tableau – A user-friendly OLAP tool for visual data analytics.
These OLAP tools in data warehouse help businesses make informed decisions by generating interactive dashboards and reports.
Why is OLAP Important for Businesses?
- Faster decision-making – Quick data analysis for improved strategies.
- Data consolidation – Integrates data from multiple sources into one platform.
- Trend analysis & forecasting – Helps in understanding business trends.
- Improved performance – Reduces query execution time for large datasets.
- Better reporting – Generates highly interactive and dynamic reports.
With the increasing volume of data, OLAP in data warehouse is becoming an essential tool for businesses to stay competitive and data-driven.
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On A Final Note…
The question “What is OLAP in data warehouse?” is crucial for anyone interested in data analytics, business intelligence, and decision-making.
By understanding OLAP full form in data warehouse, its types, operations, tools, and architecture, businesses can improve their data processing capabilities.
Want to learn more? Enrol in ZELL’s course today!