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Data Cube

What is a data cube?

A data cube is a multidimensional representation of data intended to facilitate effortless retrieval and structured analysis of the underlying data. When organized in a cube rather than a network of relational tables, it becomes easier for the user to establish relationships between data that could otherwise be challenging to figure out. This directly results in enhanced in-depth analysis and advanced drill down. Every face of the cube can be programmed to represent a particular category and users can pivot the cube to look at the same data from a unique perspective.

While data cubes can exist as a simple representation of data, without any extensive capabilities to analyze large volumes, OLAP data cubes are particularly valuable for complex data analysis, including business intelligence as they provide a comprehensive view of information across different dimensions, such as time, products, locations, or customer segments. For example, if you are looking at a sales data cube, different dimensions can show you data by year, product category, locations, customers, etc. So, whenever we mention a data cube from here on, we will be referring to an OLAP model.

What are data cube classifications?

OLAP emerged as a response to the limitations of relational databases for analytical and multidimensional data processing. OLAP databases are optimized for complex queries, multidimensional analysis, and fast retrieval of processed data. They allow users to interact with data from different angles and hierarchies. OLAP has developed into three major classifications:

  • ROLAP: Relational OLAP stores and processes data in a relational database. ROLAP servers typically query the relational database to generate reports and analyze data. Although scalable to large volumes of data, ROLAP can encounter scalability issues after a certain point due to its dependency on the underlying database. It integrates well with existing relational databases which makes it easier to implement and maintain.
  • MOLAP: Multidimensional OLAP stores and processes data in a multidimensional format conceptualized like a cube. This structure makes it easier to establish relationships between data points and optimize the data for complex analytical queries. MOLAP cubes are pre-calculated and stored in a separate database from the source data. With many operations done beforehand, MOLAP does not require as much processing power from the relational database server.
  • HOLAP: Hybrid OLAP combines the best features of ROLAP and MOLAP. HOLAP stores some data in a relational database and some data in the multidimension format. Therefore, HOLAP provides both the scalability of ROLAP and the performance of MOLAP for organizations with large and complex data sets.

What are the data cube operations?

Data cubes support various operations that allow users to examine and analyze data from different perspectives. Here is an overview of some key data cube operations:

  • Roll-up: This operation adds up all the data from a category and presents it as a singular record. It is like zooming out of the cube and looking at the data from a broader perspective.
  • Drill-down: While trying to access a transaction on the point-of-access, users need to descend into a dimension hierarchy. For instance, drilling down on the product dimension in the sales data cube would provide detailed sales figures for each product within each region.
  • Slicing: When users want to focus on a specific set of facts from a particular dimension, they can filter the data to focus on that subset. Slicing a sales data cube to focus on “Electronics” would restrict the data to sales of electronic products only.
  • Dicing: Breaking the data into multiple slices from a data cube can isolate a particular combination of factors for analysis. By selecting a subset of values from each dimension, the user can focus on the point where the two dimensions intersect each other. For example, dicing the product dimension to “Electronics” and the region dimension to “Asia” would restrict the data to sales of electronic products in the Asian region.
  • Pivoting: Pivoting means rotating the cube to view the data from a unique perspective or reorienting analysis to focus on a different aspect. Pivoting the sales data cube to swap the product and region dimensions would shift the focus from sales by product to sales by region.

What is an example of a data cube?

Banks collect and analyze data on customer interactions with their various products and services. This data-driven approach allows banks to offer personalized services and promotions, enhancing customer satisfaction and optimizing business performance. Here is an example of how banks collect and organize data:

Table 1: Banking Products

Product Type Description
Checking Accounts Everyday banking and payment transactions
Credit Cards Credit card offerings for various needs
Personal Loans Loans for personal expenses
Mortgage loans Home loan products for buying properties
Business Accounts Banking services for businesses

Table 2: Time Period

Time Period
January
February
March
April
May
June
July
August
September
October
November
December

Table 3: Customer Segments

Customer ID Customer Name Age Employment Status Income Level
001 Sarah Smith 28 Employed Moderate
001 Sarah Smith 28 Employed Moderate
002 John Johnson 42 Self-employed High
003 Emily Davis 60 Retired Low
004 David Brown 35 Employed Moderate
005 Susan Lee 48 Employed High
006 Mark Ross 30 Self-Employed Moderate
007 Kevin Grey 52 Employed High

Table 4: Customer Interactions

Time Period Product Type Customer ID Number of Transactions
January Checking Accounts 001 25
February Credit Cards 002 12
April Mortage Loans 003 3
August Business Account 006 17
October Personal Loans 004 8
November Investment Accounts 005 10
December Credit Cards 007 6

By tracking customer interactions with these products, the bank can gain insights into individual preferences and usage patterns. They can then use this data to create targeted marketing campaigns, offer personalized services, and adapt their strategies to seasonal trends, enhancing customer satisfaction and improving business performance in the real world.

Data Cube Representation

In a data cube, each cell contains the number of bookings for the intersection of a unique combination of dimensions mentioned in the table above. Here is a simplified representation of the data cube:

What are the elements of a data cube?

Data cubes are particularly well-suited for analyzing data that changes over time, as they allow users to easily see underlying trends and patterns. Here are the key elements of a data cube:

  • Dimensions: Different faces of a data cube represent different dimensions of the cube. Using dimensions, data can be categorized into separate groups. Product, customer, time, and location can be dimensions in a sales data cube.
  • Measures: If the dimensions of a cube are represented as tables, then those tables are conceptually divided into rows and columns. Measures are the homogenous groups, represented by these rows and columns, in which the data is clubbed together. In a sales data cube, measures might include sales amount, profit margin, and average order value.
  • Facts: Every data entry made by a user is treated as a fact. This collection of individual facts is then processed to identify trends and patterns in the data. Facts are stored on a table separate from dimensions and measures. Users need to drill down into a data cube to access individual facts.
  • Hierarchies: Hierarchies allow users to roll up or drill down on data. They represent the relationships between various levels of a dimension. In a time-based dimension, there may be a hierarchy that goes from year to quarter or month to days.

Advantages of a data cube

Programmed data cubes can accelerate data retrieval, support multi-dimensional analysis and enable easy processing to help businesses make informed decisions based on data-driven insights. Data cubes offer several advantages over traditional data analysis methods, including:

  • Fast: Data cubes are programmed before appending the semantic layer onto it, which means most of the required calculations reside in the cache memory. These calculations expedite query response times, which helps users retrieve and analyze large datasets quickly.
  • Efficient: The multidimensional approach enables users to identify patterns, trends and relationships that might go unnoticed in a traditional two-dimensional table. By enabling users to slice, dice, format and pivot the data along every available axis in its multi-dimensional structure, data cubes allow a versatile range of operations without impacting performance.
  • Scalable: Data cubes can be scaled to accommodate billions of rows of data to adjust to evolving business requirements. At the introduction of any new data lakes/warehouses and dimensions, data cubes are built to remain flexible and adapt to the new order.
  • Convenient: By processing data in advance, these cubes ensure that operations remain smooth irrespective of data volume. As the data grows, some level of abstraction can creep in from the cracks, but the robust structure and pre-calculated relationships can still conveniently handle user queries.
  • Reliable: The underlying data is processed and vetted before preparing the multi-dimensional data structure. By performing operations on a single source of truth, data cubes can be trusted to produce accurate, consistent and complete insights from the data, notwithstanding the dimension from which the cube is accessed. This confidence allows organizations to make better decisions about their operations, marketing strategies, and resource allocation.
  • Accessible: Data cubes are platform agnostic for users to access and analyze their data from anywhere. This allows business operations to become location agnostic, allowing a rapid response to changes in the business environment. Additionally, visual presentation of the data using charts, graphs and dashboards can speed up insights.

Difference between data warehouse and data cube

On a prima facie basis, data warehouses are centralized repositories for storing data from various sources. No processing operations are done on the spreadsheet-like data which is presented to the user as soon as it is asked. However, while programming a data cube, the data is processed first and then shaped into a multi-dimensional structure. Users can then run queries on the data, helping them draw insights from the available facts.

Here are a few pertinent differences between the two:

Aspect Data Warehouse Data Cube
Purpose Centralized data storage Multidimensional data model
Data Structure Relational database Facts, dimensions, and measures for multiple viewpoints
Data Sources Most suitable for data storage and retrieval speed Most suitable for analytical operations and query speed
Query Complexity Suitable for standard SQL queries and reporting Supports complex OLAP queries and analytical operations
Schema Design Star schema, snowflake schema etc. Dimensions, hierarchies, facts and measures
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