Before you begin using a Data cube in your project, it’s important to know what is data cube and what it can do. Basically, it is a data aggregation tool that stores and summarizes a large amount of data in a simple way. In this article, we’ll look at how to use this tool and what you should keep in mind when using it.
What is Data Cube
Data cubes are often used to represent complex data. They are basically tables with one or more dimensions. They are best suited to presenting analytical data. For example, in online analytical processing, the dimensions can be company structure, products, time, and more. Another example is the satellite image time series, where each pixel represents a particular space and time.
Data cubes can be one, two, or three-dimensional. They are divided into cells for each dimension. Each cell represents a measure of interest. Some cubes contain only a few values, while others store all of the cube’s coordinates. This type of data is considered dense.
When using a data cube, you need to determine how to use it to summarize your data. Usually, this is done using a concept hierarchy, which maps lower-level concepts to higher-level ones. This makes it easier to find the relationships between values.
Can you imagine data in multidimensional form? Well, there is a data cube which is an arrangement that is there for detailed queries. They have metrics which one or more generally approachable dimensions. The cube represents the data with the measurement. The cube can be two-dimensional three dimensional, or multidimensional as well. Every dimension f the cube represents some characteristics of the dataset. Every cee represents the measure of interest. A data cube represents the particular data extracted from a complex dataset. Some of the features of data cube –
- It can be multidimensional, which is higher than the second and third dimension
- Analyzing the data improvises the business strategies.
- The current market scenario is found through a data cube by creating trends and performance analysis.
- A data cube is a vital bridge between a data warehouse and reporting tool.
Data Cube Aggregation
Data cube aggregation is one of the parts of data cube reduction where the data is reduced. Like every week, the data is compiled, and the data is reduced like the volume of the data is reduced. Data cube aggregation is to aggregate data into a more straightforward form. Data cube aggregation is a multidimensional technique it uses the method f data reduction. Data cube aggregation represents the data in precise form. For example, you work in a real estate company, making the data yearly. The company is from 2010. Now you want the data for this year only, so by using data cube aggregation, the data would be much less and the particular data aggregated.
Data Cube Examples
A data cube is sued in real life, so here are some life data cube examples
For Measuring Performance
Example 1
You won five grocery stores in 5 different locations. It would help if you found which store generates the most profit out of the five. Firstly you have to make a data cube of profit for all the five stores. Now you can make a report based on the data cube. So data cube makes the work much easier to measure performance.
To Find out Opportunities
Example 2
You work in the cosmetics category and must find the top-performing brand. While making the data cube, you have to make a data cube on the brand and the product sales data. The data cube is the data source here. Now you have to make the data cube resemble the brand and spot the possible opportunities. Data cube helps separate the specific data from a complex dataset.
Data Cube Warehouse
There always has been a debate about data cube warehouses and data warehouses. A Data warehouse is like a place where data is kept and stored. A data cube is a small part of the data warehouse. a Data warehouse is an outdated concept now. Data warehouse has a lot of limitations over it. The data cube warehouse was a revolutionary concept To separate specific data from complex data.
Data Cube Technology
Now that we have understood what is data cube technology. We will find out what data cube technology is used for.
The data was aggregated from different viewpoints in a data cube. It is in the form of a cube because the data could be shown in a summarised way to inform facts and variables. For a normal data set, the data cube represents the data using its dimensions. Each dimension shows some particular database such as – profit or revenue,
There are two types of data cube technology –
- Multidimensional Data Cube – this type of data cube is used in businesses as the businesses have a large set of data. The extensive data is handled by multiple dimensional data cubes easily. The data is more precise and in a separate way. If a particular data is needed urgently, the multidimensional data cube makes the work simpler.
- Relational data cube – the relational data cube uses the relational database model. If we compare it to the multidimensional data cube, it is double it. It is like a cuboid that has more space. More data sets and requirements are mentioned in the data cube.
Data Cube Operation
An organization has a lot of piled-up data and wants to come up with insightful data out of the abundant data. Data cube summaries that data using the data cube operation and convert the compiled data into different data.
- Slicing
Slicing works as it takes an individual for the whole but only two dimensions of the data cube. The slice of the data cube has only two dimensions left; the other has the data, which is shown as a table.
2. Dicing
Dicing is done with a multidimensional data cube. It chooses a subset of the by picking two or more values of multiple dimensions of the data cube. The data is spread among multiple dimensions, but the data is comparatively tiny than the original data.
3. Roll up
Roll-up summarizes the data in a single dimension. The summarized data is calculated like we calculate profit, i.e., profit = sales – expenses. The Series of data is also collected and summarised and then put into the data cube using roll-up.
4. Pivoting
Get another perspective of the data. The pivoting data cube operation is used. For example, the data was shown as rows and columns by pivoting the data. The rows become the columns, and the columns become the rows.
5. Drill down is the opposite of the data cube operations. It maximizes the data. It starts to hierarchy from a higher level. For example, if we want the total number f employees, the drill-down methods will show the total number of employees to the male and female employees separately.
Advantages and Disadvantages of Data Cube
The data cube is the best explanation for solving the problems of untangling complex data. But everything has its advantages and disadvantages. Here are some of the advantages and disadvantages of the data cube
Advantages
- Fast – the data cube is faster as it untangles the complex data. It speeds up the process of the company, as the complex data into converted into separate categories.
- Simple UI – the data cube has a simple user interface compared to the data warehouse. The data is interpreted easily in such an interface.
Disadvantages
- New data cube – data cube works on the particular if a new data comes you then a separate data cube is required for the existing has to go through changes.
- Time-consuming – as mentioned above, a new data cube is required if new data comes up. It is time-consuming because a new data cube is required every time new data comes up.
- Summarised information- data cube requires summarised information details left out by the data cube. If you need detailed information, you need the read the whole dataset.
Conclusion
The data cube presents the data in the form of dimensions and facts. In today’s time, the company needs the data to be more specific and categorize this in s where will advance the data cube steps in. the data cube technology in the coming future as the researcher is working on this.
