Question

How is the concept of a relational view related to data warehouse and data marts? In what way are they different?

Short answer

Relational views, data warehouses, and data marts are all related through their ability to manage and present data. Relational views aim to simplify the user interface and do not physically store data. Meanwhile, data warehouses and data marts physically store data and are used for reporting and analysis. While relational views contribute to user-friendliness in both systems, their main purposes vary.

Step-by-step solution

Understand Relational View

First, understand the concept of 'relational view'. It is an aspect of the database management system which presents the data in a format that the end user finds easy to interpret. A relational view does not store data physically but provides a virtual table created from a database function.

Understand Data Warehouse and Data Marts

A 'Data Warehouse' is a system used for reporting and analysing data. It is a central repository of data integrated from one or more sources. 'Data Marts', on the other hand, are smaller, focused data warehouses designed to serve a particular business line or team.

Relational View and its relation to Data Warehouse and Data Marts

Relational views are used in both data warehouses and data mart systems. For example, in a data warehouse or data mart, relational views can be used to present the data in a desirable user-readable format. This allows end users to access and analyse data effectively without getting involved in complex queries or worrying about where the data is stored.

Differences between Relational View and Data Warehouse and Data Marts

While both relational view and data warehouses or data marts are used for managing data, they have quite a few differences. A relational view operates on virtual tables and doesn't store data physically, whereas data warehouses and data marts physically store data. The major goal of relational views is to simplify the user interface, while data warehouses and data marts serve for reporting and analysing purposes.

Key concepts

Data Warehouse

Imagine a massive, secure storage facility that only contains valuable data instead of physical goods. That's what a data warehouse is like in the digital realm. It's a centralized repository for data collected from various sources, often different parts of an organization. Rather than just storing data, a data warehouse is designed to consolidate, manage, and analyze large volumes of information, optimizing decision-making processes.

The effectiveness of a data warehouse relies on its architecture and technologies that support complex queries, fast data retrieval, and advanced data analysis. Thanks to its approach to historical data retention and non-volatile nature, data warehouses allow businesses to track trends, perform intricate analyses, and generate comprehensive reports.
Users commonly access a data warehouse through business intelligence tools, which help depict the data in a more digestible form through charts, graphs, and dashboards. These tools often use a relational view—essentially a perspective of the data—that makes it easier for non-technical users to interact with and understand this complex information.

Data Marts

Think of data marts as small, boutique stores you might find in a neighborhood, specializing in a specific type of good. Data marts are subsets of data warehouses tailored for the unique needs of a particular business unit or department within an organization.

By focusing on a limited scope, data marts provide quicker access to relevant data and more targeted insights. They allow users to dive deeply into a particular business segment with increased performance and without overwhelming them with the vastness of data stored in the larger data warehouse.
For instance, a marketing data mart may solely focus on customer information, campaign performance, and sales data, enabling the marketing team to perform specialized analysis and generate insightful reports quickly. The use of a relational view in data marts empowers users by offering them a user-friendly format that mirrors the simplicity and efficiency of a boutique shop in the context of data management.

Database Management Systems

Now, picture the backbone of digital data storage and retrieval—a database management system (DBMS). This complex software acts as a facilitator between users and databases, ensuring that data is consistently organized and remains accessible.

A DBMS allows for the creation, definition, and management of databases, providing a systematic way to handle data. It supports various operations such as data insertion, query processing, update, and administration of databases. DBMSs ensure data integrity, security, and concurrency through a set of rules and structures.
One essential part of a DBMS is the concept of a relational view. This feature enables users to see a tailored presentation of database data, akin to a custom viewfinder that highlights certain aspects but does not physically store them. The relational view simplifies interaction with the data, offering a more intuitive and understandable way to work with complex databases, which is especially useful for non-technical end-users.