What is OLAP? Online Analytical Processing – Types & Benefits

By | June 28, 2020
Online Analytical Processing

OLAP (Online Analytical Processing)

OLAP (Online Analytical Processing) is a particular category of the software that offers users to analyze information from various database systems at the exact time. OLAP is a type of technology that allows analysts for extracting and viewing business data from various points of view. Analysts usually require being grouped aggregated and joined data and these performances in the relational database are resource-intensive. Through OLAP, you can pre-aggregate, and pre-calculated data to make analysis faster or easier. It is divided into one or more extra cubes. These cubes are designed to create and view reports that become simple.



Basic Analytical operations of OLAP

There are four types of analytical operations in (Online Analytical Processing) OLAP are:

  1. Roll-up
  2. Drill-down
  3. Slice and dice
  4. Pivot (rotate)

1. Roll-up

It is also called consolidation or aggregation and this particular operation can be executed in 2 ways:

  1. Decreasing dimensions
  2. Climbing up the concept hierarchy and concept hierarchy is the system of grouping objects based on their order and level.

2. Drill-down

In the drill-down, data is fragmented in tinier parts, and it is the inverse of the roll-up process. Drill-down can be done through:

  1. Moving down the theory hierarchy
  2. Developing a dimension

3.1 Slice:

In this type of OLAP, one dimension is selected. A new sub-cube is designed. In this diagram, we can see that operation performed by Slice.

  1. Dimension time is sliced with the Q1 as the filter.
  2. A new cube is designed altogether.

3.2 Dice:

This particular operation is related to a slice. The variation in dice is that you select 2 or more dimensions which result in the production of the sub-cube.

4. Pivot

In this type of OLAP, you rotate the data axes for providing a substitute presentation of data.

Types of OLAP


ROLAP runs with data that exists in the relational database. Dimension and fact tables are saved as relational tables. ROLAP also provides a multidimensional analysis of data and is the fastest-growing OLAP. ROLAP refers to an extended RDBMS with the multidimensional data mapping for performing the standard relational operation.

Advantages of role models

  • High data efficiency: It offers high data efficiency due to query performance, and access language is individually optimized for multidimensional data analysis.
  • Scalability: It is a type of OLAP system that offers scalability to manage a large volume of data, also when the data is continuously growing.

Role model drawbacks:

  • High resource demand: It needs a high utilization of manpower hardware and software resources.
  • Aggregate data limits: ROLAP tools utilize SQL for calculating total data, but there is no set limit for dealing with computation.
  • Slow Query Performance: It is slower in this model when compared with MOLAP


MOLAP utilizes array-based multidimensional storage engines for displaying multidimensional views of data. Basically, they use an OLAP cube.

Hybrid OLAP

It is a combination of ROLAP and MOLAP. This provides faster computation of MOLAP and higher scalability of ROLAP. HOLAP uses two databases.

The data collected or calculated is saved in the multidimensional OLAP cube

Detailed information is stored in a relational database.

Benefits of Hybrid OLAP:

  • This type of OLAP helps reduce disk space, and also remains compact, which helps avoid issues related to access speed and convenience.
  • Hybrid HOLAP uses Cube technology that allows faster performance for all types of data.
  • ROLAP is updated immediately, and HOLAP users have access to instantly updated data in this real-time. MOLAP brings data cleanliness and conversion that improves data relevance. It brings the best of both worlds.

Drawbacks of hybrid OLAP

  • Higher complexity level: The major drawback of HOLAP systems is that it supports both ROLAP and MOLAP tools and applications. Thus, it is very complicated.
  • Potential Overlaps: Especially their functionality is more likely to be overlapping.

Desktop OLAP (DOLAP)

A user downloads a portion of data from a database locally or analyzes it on their desktop in the desktop OLAP. DOLAP is relatively inexpensive to deploy because it offers very limited functionality compared to other OLAP systems.


Web OLAP is an OLAP system accessible through a web browser. WOLAP is a three-tier architecture. It consists of three components, such as client, middleware, and database server.

Mobile OLAP

Mobile OLAP helps users to access and analyze OLAP data using their mobile devices.

Spatial OLAP

SOLAP is created to facilitate the management of both spatial and non-spatial data in a Geographic Information System (GIS).

Benefits and Limitations of OLAP


  • It is a stage for all types of business that includes the plan, budget, report, and analysis.
  • Information and computation are compatible with an OLAP cube, and it is a significant benefit.
  • It quickly analyzes “what if” scenarios.
  • Quickly search OLAP databases for broad and specific terms.
  • OLAP provides a building block for data mining tools, business modeling tools, and performance reporting tools.
  • It enables users to slice and dice cube data by different dimensions, measures, and filters.
  • This is good for analyzing time series.
  • Some clusters and outliers are easy for finding with OLAP.
  • It is a robust visual online analytical process system that offers quicker response times.


  • OLAP needs data to be organized into a snowflake schema, and these schemas are complex for implementing and administering.
  • You cannot have a huge number of dimensions in a single OLAP cube.
  • Transaction data cannot be accessed from the OLAP system.
  • Any modification to the OLAP cube requires a full update of the cube. It is a time-consuming process.


As we have already discussed, OLAP is a type of technology that allows analysts to extract and view business data from various points of view. Analysts usually require being grouped, aggregated, and joined data. These operations in the relational database are resource-intensive. In this article, we have provided complete information regarding OLAP.

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