What Is OLAP?

OLAP is short for Online Analytical Processing, a type of database management in which data is stored in a multidimensional structure as opposed to a tabular format.

An OLAP database uses a multidimensional structure to facilitate the rapid analysis and retrieval of data. The term multidimensional refers to the combination of multiple fields and measures into a single database that can be queried and analyzed.

OLAP is a technology that’s used to analyze large amounts of data stored in relational databases. It does so by creating multidimensional cubes of data, which are much more analytical-friendly than 2D tables.

With this technology, it’s possible to slice up the contents of an OLAP cube in almost any way you want, including by time or by location, with each cube cell containing summary information about that particular slice.

This makes analyzing complicated datasets much more straightforward – because you don’t have to build a custom solution from scratch from your raw data every time you need to analyze something new.

Online Analytical Processing (OLAP) tools help business analysts and decision-makers explore, understand, and gain insights into the data they analyze.

OLAP makes it possible to model complex relationships, apply measures across many data dimensions, and produce in-depth analyses.

What Is A Cube In SSAS (By Example)?

A cube is a collection of related data that helps you analyze business problems flexibly.

In SSAS, the term “cube” refers to dimensions and measures.

A data source can contain multiple dimensions and measures, and you connect each dimension to a single measure. When you combine data from various sources in a single database and create a collection of data items that reference those sources, you use a cube in Analysis Services.

Example

In the AdventureWorksDW2019 sample database, the DimGeography table represents a geographic dimension containing customers’ locations.

The cube includes one measure group (FactInternetSales) and two dimensions (DimCustomer and DimDate).

You can download this sample cube from here.

Why Do We Need OLAP Cube In SSAS?

OLAP cube in SSAS is a Multi-dimensional object that stores data at different levels of granularity.

It can be used as an underlying data structure or efficiently and quickly retrieve summarized data from a large volume of raw data.

This datastore methodology enables you to slice and dice data by definition-described dimensions and measures to analyze information in your business.

OLAP cubes are used to analyze data.

In other words, they help you get a better picture of your business.

What Are Dimensions In OLAP Cube?

A dimension is a group of related attributes.

In an OLAP cube, dimensions are used in conjunction with measures to provide contextual information.

Cubes are defined by their dimensions. A cube contains measures, numerical values, and a set of dimensions to which the measures can be added together or subtracted.

OLAP allows you to examine your data in ways that are not available in relational databases.

Dimensions are virtual constructs that exist only in the context of an OLAP server. They are not physical database objects, nor are they stored in the database — an OLAP server creates them during its processing.

Dimension Example

OLAP servers use dimensions to create calculated members and hierarchies (cubes) such as [Product].[Product Subcategory].[Price Discount].

What Are Measures In OLAP Cube?

A measure is a value calculated from the data in your cube, which provides specific information about the business.

The measures in your cube can be grouped into several categories: numeric measures, string or text measures, and date-time periods.

Measure Example

Features Of OLAP Cube In SSAS.

The OLAP is the fastest way to access and analyze your data.

This datastore methodology is a multidimensional database for capturing and storing large amounts of data. It is a virtual multidimensional data set containing aggregated data from multiple sources.

The cube is preconfigured and optimized for multi-user queries, allowing users to slice and dice data from many perspectives.

A hybrid OLAP cube contains a variety of measures and facts and provides interactive analysis for the user.

Types Of OLAP Cube In SSAS.

There are three main types of OLAP cubes in SSAS:

• Tabular,
• Multi-dimensional
• and Hybrid.

Tabular Cubes

Tabular Cubes offer better performance when working with smaller data sets and dealing with massive amounts of values since relationships can be denormalized more efficiently.

Multi-Dimensional Cubes

On the other hand, multi-dimensional cubes remain applied when complex relationships must be maintained between various data sets and warehouse-type environments.

Tabular Cubes or Multi-Dimensional Cubes?

1. When choosing between a Tabular or Multi-dimensional cube, the first thing to consider is whether your business requires a large set of transactional or historical data.
2.  Second, we consider how well the data are distributed across multiple related tables.

Why Use OLAP Cubes?

OLAP cubes are more flexible than relational databases.

OLAP cubes can handle complex computation, summarize data in different ways, and enable powerful analytics. These features make them the most powerful tool for data analysis tasks.

The main advantage of building your reports and dashboards on OLAP cubes is that there are performance gains without having to rebuild the cube. As new data is added, the existing reports will not need to be rebuilt because everything is refreshed periodically as needed.

Benefits Of OLAP Cube.

1. OLAP cube is the most crucial stage in an organization’s decision-making process.
2. This datastore methodology allows users (business professionals and other experts involved in business) to access information through queries based on their needs and requirements and by creating reports that reflect their interests.
3. It enables the interactive exploration of data using a multidimensional query language.
4. This datastore methodology also provides materialized views that are prebuilt structures to support interactive analysis. These capabilities help users quickly access and manipulate big data.
5. This concept is multidimensional, meaning it uses a combination of data (facts) and dimensions to portray the dataset.
6. OLAP allows users to examine a slice of data from the top down or the bottom up.
7.  OLAP is advantageous because it can represent analytical requirements that are otherwise impossible or inefficient to define in relational systems.
8. You’re getting better insight into your data by eliminating transaction blind spots, getting more accurate results, and making smarter decisions faster.

Are OLAP Cubes Still Used?

Yes and No.

OLAP cubes have been replaced in many cases. In some industries, however, these cubes are still used by IT teams and business users who are more comfortable with this type of product rather than using newer offerings that exist for business intelligence and data analytics.

A global regulator might use an OLAP cube provided by a data warehouse because the tech staff is familiar with its capabilities over some other type of solution offered from one of the popular vendors today, such as Tableau or Power BI.

They are no longer used as much as they were in the past, with many organizations moving to a more agile approach. However, there is still a place for them in today’s data architecture.  They are still used in government, military, and educational institutions. These organizations look to OLAP cubes for their security and high-performance requirements.

NoSQL and data warehouses have replaced OLAP cubes, but they still have their uses.

This methodology is designed to help users make better decisions. They provide a multidimensional, tabular view of the data that is analyzed and analyzed in real-time. In other words, cubes facilitate decision-making by analyzing data from multiple perspectives. The problem with these cubes is that they tend to be overly complicated for many organizations and require high levels of skill and knowledge to maintain.

OLAP Cube Vs Data Warehouse.

OLAP is one of the concepts in data warehousing. online analytic processing is a data storage methodology that allows a user to analyze, visualize, and create reports from large amounts of data viewed differently. In general terms, an OLAP cube is a multidimensional database that can store and manipulate data in an aggregated format within the context of business questions and queries.

In contrast, a data warehouse system uses a relational database model with a schema to store transactional data.

How To Create An OLAP Cube In SSAS?

The first thing you need to do is to collect the data that you want to use in your analysis. You can do this by either creating a SQL view over your existing tables or using SSIS packages.

Once your data is collected, you need to define relationships between those tables and create dimensions with their key values, and measure groups with their measures.

After that, it’s time to define how those measures are related to each other using hierarchy relationships.

Finally, the cube needs to be processed using the Data Flow task and deployed into Management Studio as a new object (table).

Conclusion

An OLAP Cube can be a valuable tool for working with data if you have the right environment, time, and resources to manage it.