Data Cube: Definition and Examples

An essential part of maintaining data is keeping it organized. One way to do that is with a data cube.

This post will examine what a data cube is, including its structure and components, as well as the different types of data cubes available to you. Then, we'll look at some challenges with data cubes before taking a closer look at data cubes at work in real-life scenarios.

What Is a Data Cube?

A data cube is a type of online analytical processing (OLAP) system. Used as part of the greater observability infrastructure, these multidimensional data structures help us visualize and organize data into an easily digestible format. Data cubes can organize various types of data, but they're most often used for organizing business data. They display business values (items, sales numbers, etc.) over time (year one, year two, etc.).

Data cubes make it simple to organize and find otherwise complex data, which in turn makes data cubes valuable tools for business analytics. Structuring data across multiple dimensions makes it easier to analyze relationships and trends from different perspectives (e.g., time, geography, product), and analysts can quickly compare metrics across these dimensions.

Structure and Components of Data Cubes

Data cubes are not solid objects. The name "data cube" is derived from the fact that they're usually made up of spreadsheets (containing sales values, for example), each representing a different time period stacked on each other.

YouTuber Matthew Love created a great video explaining the basics of data cubes in simple terms. In the example below, his spreadsheets show the sales of different items across four days, which is why the cube has four layers.

Data cubes may have fewer or more layers than the one above and, in rare cases, they may not be visualizable at all.

Every data cube contains dimensions and measures. Dimensions represent the perspectives or attributes by which data is categorized. In the diagram above, the dimensions are "products," "branches," and "dates." As Love explains, dimensions will have members, i.e., the rows and columns containing the product and branch names. And each member will have an attribute, which is the actual descriptive text within that row or column, e.g., "sweets," "crisps," "Sheffield," etc.).

Measures, on the other hand, represent the values within the cells. They're the numeric datasets that the cube organizes for us. 

Each dimension organizes a set of measures, and data cubes show these values over time, allowing analysts to track changes over time.

Types of Data Cubes

There are various types of data cubes that are used for various analytical purposes. These types of data cubes differ in how they store, process, and retrieve data, offering flexibility for a range of applications.

Relational data cubes are built on traditional databases, dynamically aggregating data through SQL queries. This means they operate in real time. And while that may have its advantages, it means that relational data cubes that hold large amounts of data may load slowly.

Multidimensional cubes, on the other hand, pre-aggregate data across dimensions. This makes them quite a bit faster, but they require more storage space.

For environments with limited resources, virtual cubes avoid physical storage by retrieving data on demand, though this results in slower query speeds. Similarly, sparse cubes address scenarios where many data combinations lack values, optimizing storage by compressing empty cells. Dense cubes, on the other hand, assume all data combinations are populated, offering simplicity at the cost of increased storage.

Advanced types like hybrid cubes balance pre-aggregation and dynamic computation, combining the strengths of relational and multidimensional models. Distributed cubes handle massive datasets by distributing data across multiple nodes, providing scalability in big-data environments. Incremental cubes allow efficient updates by recalculating only new data, reducing computational overhead.

Rolled-up cubes aggregate data to higher granularity, such as annual sales, whereas drill-down cubes provide detailed views like daily transactions. In dynamic contexts, real-time cubes continuously update to support immediate insights, which are essential in fields like IoT and stock trading.

These diverse types enable data cubes to cater to various scenarios, from real-time analysis to large-scale distributed systems. Their adaptability ensures they remain integral to modern data analysis, facilitating both high-level summaries and detailed insights.

Operations on Data Cubes

Data cubes allow users to analyze data through various operations, giving analysts a variety of ways to explore their data and making them powerful tools for gaining insights. 

Drill Up and Drill Down

A drill-down operation moves users from an overview of a dataset to a more detailed view. For example, yearly sales values can be drilled down to monthly or quarterly sales. Conversely, drilling up, or rolling up, aggregates detailed data and summarizes it in a high-level overview, like consolidating monthly sales into yearly sales values.

Slice

Slicing narrows down to a single dimension and highlights data from only that dimension. For example, if year, state, and product are dimensions in your cube, a slice operation would allow you to see only data that corresponds to a specific year or from a specific state.

Dice

Dicing a cube creates a smaller cube by selecting specific values across multiple dimensions. For example, you can create a sub-cube of data corresponding to a specific year, state, and product for deeper analysis.

Pivot

Pivoting, or rotating, reorients the data cube to change its perspective. Pivoting rotates the dimensional axes, swapping rows and columns to allow viewers to see the cube from a new angle. For example, if the product dimension is on the y-axis and the state dimension is on the x-axis, a pivot would move products to the x-axis and states to the y-axis.

Applications of Data Cubes

Data cubes are used across a wide variety of industries to gain data-driven insights into consumer behavior and financial performance, and their insights may even be used to guide companies' strategies so that they know how best to navigate their businesses.

Financial institutions use data cubes for budget tracking, profitability analysis, and forecasting. Similarly, businesses use data cubes to analyze key metrics including sales and expenses across different times and locations. This allows them to monitor performance in real-time or over long periods of time.

Data cubes are particularly popular in the healthcare, education, and retail industries. Hospitals are able to study patient trends to maximize efficiency, and schools can do the same for their students. Retail companies are able to analyze sales patterns and allocate resources to their highest-performing products. Data cubes also allow companies to more efficiently inventory their stock and analyze their logistics.

That's not to say, however, that data cubes are not used in other industries. Data cubes are widely used.

Challenges and Limitations

Although data cubes can be powerful analytical tools, they come with unique disadvantages, two of which are their limited flexibility and complex nature.

OLAP tools in general are complex to set up, and data cubes are no exception. Creating schemas, managing hierarchies, and ensuring data accuracy requires expertise. As a result, setting them up can be time-consuming and expensive.

Furthermore, data cubes offer limited flexibility. Large datasets can slow down the cube, and adding new dimensions can prove cumbersome. Most traditional data cubes also rely on pre-aggregated data, making them unable to deliver real-time updates. This can also lead to storage issues, especially for large datasets, since pre-aggregated data would need to be stored locally.

Conclusion

In this post, we discussed data cubes, the operations they can perform, their use, their limitations, and the various types. However, data insights don't end there.

OLAP systems are useful tools for organization and analytical purposes, but they are just part of a company's observability infrastructure. Platforms like Acceldata can boost this infrastructure, providing comprehensive insights into your data to help your organization gain the maximum value out of data cost-efficiently. By monitoring, optimizing, and troubleshooting OLAP workloads in real time, Acceldata ensures peak performance, scalability, and seamless integration with broader business objectives.

This post was written by Jo Efobi. Jo is a Software Engineer with a degree in Neuroscience. She has worked with the MERN stack, Vue,js, Python, and Golang. She loves contributing to open source and exploring the intersection of medicine, healthcare, and technology. Jo is also a founder and senior editor at Writehandle Media- an online writing solution agency.