What Is OLAP? - Online Analytical Processing Explained

Want to build your career in Data Analytics but don’t know what OLAP (Online Analytical Processing) is?

Don’t worry we are here to help, reading this article will help you to understand the topic of OLAP(online analytical processing) clearly.

Table of Contents

Key Takeaways

You will learn about the basics of Online Analytical Processing (OLAP)
Understanding the types of OLAP servers and their operations.
Understanding the Advantages and Disadvantages of Online Analytical Processing (OLAP).

What OLAP is?

OLAP (Online Analytical Processing) is a technology used in data analysis and business intelligence. It enables users to interactively analyze large volumes of multidimensional data from different perspectives, providing fast access to aggregated information for decision-making. Unlike traditional databases optimized for transactional processing, OLAP systems are designed for complex queries and ad hoc analysis.

OLAP(Online Analytical Processing) organizes data into multidimensional cubes, where each dimension represents a different information of Data. Users can navigate these dimensions using operations like drill down, roll up, slice, and dice data. This helps in gaining insights into trends, patterns, and relationships.

OLAP is crucial for tasks like data exploration, reporting, ad hoc analysis, predictive analytics, budgeting, and planning. It helps organizations to get related insights, monitor performance, and make informed decisions based on a comprehensive understanding of their data.

Characteristics of OLAP

Multidimensional Data Model: OLAP (Online Analytical Processing) helps to organize data into multidimensional structures, these multidimensional structures are mainly represented as cubes that allow the analysis of data along multiple dimensions simultaneously.
Fast Query Performance: OLAP(Online Analytical Processing) is optimized for fast query performance, especially for complex analytical queries that involve multiple calculations, and comparisons across large datasets.
Aggregation Support: OLAP (Online Analytical Processing) supports aggregation operations such as sum, average, count, min, and max across various dimensions. This Aggregated data is stored at different levels that enable users to view data at different levels of detail.
Online Analysis: OLAP systems enable online analysis, allowing users to interactively explore and analyze data in real-time. Users can perform ad hoc queries, create custom reports, and visualize data using charts, graphs, and dashboards.
Support for Complex Calculations: OLAP systems support complex calculations and expressions, such as mathematical formulas, conditional statements, etc. This capability is essential for performing advanced analytics and modeling tasks.
Data Integration: OLAP(Online Analytical Processing) can combine a set of Data from multiple sources, including relational databases, data warehouses, and external data sources. This integrated data can be combined and analyzed within the OLAP environment.
User-Friendly Interfaces: OLAP(Online Analytical Processing) provides user-friendly interfaces, tools, and applications for data analysis and exploration.

OLAP Operations

As we know, OLAP servers are based on a multidimensional Data view, we will discuss operations performed in OLAP to Extract and Store data:

Here is the list of OLAP operations −

Roll-up
Drill-down
Slice and dice
Pivot (rotate)

Types of OLAP Servers

We mainly have three types of OLAP servers −

Relational OLAP (ROLAP)
Multidimensional OLAP (MOLAP)
Hybrid OLAP (HOLAP)

Relational OLAP

ROLAP servers are placed between relational back-end servers and client front-end tools. ROLAP uses a Relational Database management system to store data in it.

Multidimensional OLAP

MOLAP uses array-based multidimensional storage engines for multidimensional views of data. With multidimensional data stores, the storage utilization may be low if the data set is sparse. Therefore, many MOLAP servers use two levels of data storage representation to handle dense and sparse data sets.

Hybrid OLAP

Hybrid OLAP is considered as a combination of both ROLAP and MOLAP. It offers higher scalability of ROLAP and faster computation of MOLAP. HOLAP servers allow to store the large data volumes of detailed information. The aggregations are stored separately in the MOLAP store.

Advantages of OLAP

OLAP(Online Analytical Processing) helps in fast query processing as it has optimized capacity and a multi-dimensional data storing facility.
OLAP (Online Analytical Processing) allows users to analyze data across multiple dimensions simultaneously, This multidimensional analysis provides deeper insights and trends related to data.
OLAP (Online Analytical Processing) supports real-time analysis of Data, enabling users to interactively analyze and explore data as it is updated and refreshed.
OLAP makes the data very compact. Instead of storing every single piece of information separately, OLAP groups similar data together, which saves a lot of space and makes it faster.
Pre-structuring data in an OLAP (Online Analytical Processing) enables efficient analysis and extraction of Data. This helps users to slice and dice data along various dimensions.

Disadvantages Of OLAP

In certain OLAP (Online Analytical Processing) setups, especially when dealing with large volumes of data, the initial processing step to load Data can take a long time. This means it can be slow to get all the data into the OLAP system and ready for analysis.
In some OLAP techniques, there can be data redundancy. This means the same data or information may be stored in multiple places within the OLAP system.

Also Despite all these disadvantages, OLAP (Online Analytical Processing) remains a powerful and widely used technology for multidimensional analysis, decision support, and Business intelligence strategies.

Companies are widely using OLAP to grow their businesses to the next level.

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FAQs on OLAP

What is OLAP, and how does it differ from OLTP?

OLAP stands for Online Analytical Processing, which is a technology used for analyzing and querying large volumes of data to get insights and support decision-making. OLTP (Online Transaction Processing), on the other hand, focuses on managing and processing transactional data in real-time.

What are the types of OLAP?

OLAP can be categorized into three main types: ROLAP (Relational OLAP), MOLAP (Multidimensional OLAP), and HOLAP (Hybrid OLAP). ROLAP uses relational databases, MOLAP uses multidimensional databases, and HOLAP combines elements of both ROLAP and MOLAP.

What are OLAP cubes, and how are they used?

OLAP cubes are data structures used in OLAP systems to store and organize multidimensional data. They consist of dimensions and measures. OLAP cubes enable users to perform multidimensional analysis, drill down into data, and generate reports and visualizations.