What is Data Warehouse: Characteristics, Applications, & More

American computer scientist Bill Inmon is considered the father of the data warehouse concept. He defined the term in 1990. Knowledge about it along with business intelligence is a sought-after skill for data architects, codes, analysts, and database administrators. This blog will discuss what is data warehouse, its characteristics, related concepts, benefits, and applications to enhance your knowledge of the subject. 

Data Warehouse Overview

The data warehouse is a data management system that stores information about businesses and performs queries on this data to analyze it. The analysis helps businesses get insights and make informed decisions. It stores current and historical data from multiple sources, including internal and external databases and online transaction processing applications. Data warehouse meaning is a centralized data repository with tools and technologies that enable the employees to make efficient business-related decisions. Note that it is highly compatible with SQL systems and prioritizes SQL databases. 

The basic components of a data warehouse are as follows:

• Database Server
• Analytics Engine
• BI and Reporting Tools

There are also additional components such as a staging area, and data marts. To learn more about data warehouse consider taking an online SQL course. 

Data Warehouse History

IBM researchers Barry Devlin and Paul Murphy coined the term “business data warehouse” in the 1980s.  However, it was in 1990 that Bill Inmon created the data warehouse definition and further developed the concept. In 1995, The Data Warehousing Institute was set up for research purposes. The technology became widespread in the 2000s and further evolved with the introduction of a cloud-based data warehouse in 2012 by AWS and Oracle autonomous data warehouse in 2018. 

Characteristics of Data Warehouse

Here are the major characteristics: 

• Non-Volatile: A data warehouse is non-volatile. Data stored in it cannot be modified, altered, or updated. When new data is inserted, the old data remains. It allows the maintenance of historical data. The two types of operations available are data loading and data access. Hence, functionalities such as transaction processing, recovery, and concurrency capabilities. 
• Subject-Oriented: In a data warehouse, the focus is on modeling and analysis of data for decision-making. It provides a concise and straightforward view of a particular subject instead of focusing on the current operations of an organization. The warehouse will include all the data related to and necessary to understand the subject and exclude the data not concerning the subject. 

Examples of a subject are customer, product, sales, account, policy, claim, etc. 

• Time Variant: Historical data is stored in different time intervals such as weekly, monthly, annually, etc. It has a wide-ranging time limit, and we can predict data with the help of a specific time interval. For example, we can retrieve the data from six months, two years, or even from before that. 
• Integrated: A data warehouse integrates the data from multiple heterogeneous sources such as mainframe and relational databases, flat files, and online transaction records. To integrate data from these sources, a shared entity is found and the data is scaled accordingly. Integration is necessary to ensure reliability and consistency in naming conventions, encoding structure, attribute types, column scaling, etc. 

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Data Warehouse Concepts

Some of the key concepts are as follows:

• Data Warehouse Architecture: It is a framework that defines the data warehouse design and highlights how the various components integrate to work together. There are three common types of architecture. They are one, two, and three-tier architecture. In the basic architecture, we can directly access data derived through many sources. Other architectures include cleaning and processing data before storing them in the warehouse and customizing the architectural design for various groups within the organization. 
• Cloud Data Warehouse: In traditional ones, data was stored on servers present in the physical location of the organization. The cloud-based version uses cloud storage to store and analyze data. 
• Data Warehouse Modeling: It is the process of designing the schemas of the large volumes of data in the warehouse. Various kinds of modeling types are dimension data models, conceptual data models, logical data models, and physical data models. 
• OLAP (Online Analytical Processing): It is a data processing system designed for complex business data analysis and reporting. Data collected from different sources is combined and grouped by OLAP into categories to get insights for strategic planning and quick decision-making. 
• Data Mining: It uses large volumes of data to create models that can provide significant and valuable insights. It predicts customer behavior, identifies frauds, helps with risk management, discovers hidden patterns in data, etc. 

Benefits of Data Warehouse

Integral for a wide range of organizations, the benefits of this warehouse include the following:

• Data Consolidation: It consolidates data from multiple sources such as websites, internal databases, external databases, applications, etc. With this, the decision-makers of an organization get comprehensive insights into the operations, performance, and trends of the business. Because of its consolidation capabilities, it becomes the single source of truth for the entire organization, where all the teams work with the same data. 
• Data Analysis: It improves data quality and consistency for efficient analysis results and enhanced accuracy of the BI applications. Also, it can perform analysis on large amounts of data. 
• Informed Decisions: It helps in making informed and better forecasting decisions. Such business decisions lead to improved productivity, an increase in revenue, and a competitive edge compared to the competitors. 
• Accessible: The structure of the warehouse is such that it is accessible to the end-users who find it easy to navigate through it. 
• Easy Queries: We can build and maintain queries in the warehouse easily since it is not as complex as normalized databases.
• Handles Historical Data: It can store and analyze large volumes of historical data that help understand business issues and market trends over the years. 

Data Warehouse Applications

Here are some of the common applications of data warehousing technologies:

• Banking and Finance: In this industry, data warehouses ensure standard security compliances, get updates on customer deposits and loans, and understand and compare the performances of different branches. They are also used to offer customers better strategies to manage their expenses based on their records. 
• Agritech: They help optimize agricultural practices. Data analysis related to crop inventory, yields, pesticides, etc., can help agribusinesses to identify and solve problems of soil quality, damage due to excessive pesticide, etc.
• Healthcare: Data warehousing in healthcare helps with personalized healthcare services such as diagnostics, prescription, follow-up, etc., provided through a single platform. 
• E-commerce: They help e-commerce platforms optimize their performance and operations by tracking and visualizing key performance indicators such as conversion rates, sales, storage, demand, etc.  
• Manufacturing and Supply Chain: Here, it manages the inventory, logistics data, supplier and vendor data and improves the supply chain by eliminating human error.
• Sustainability and Climate Action: It allows organizations to collect raw data from various sources to make predictions and decisions about climate change. 

Conclusion

Organizations today need a data warehouse to implement analysis on their large sets of data and make improved decisions for their business in less time. Due to its benefits, it is a better choice for industries such as banking and finance, healthcare, climate and sustainability, and manufacturing.

Are you looking for a job in the field of data warehouse, then check out these top data warehouse interview questions to ace your next interview.