What is a Data Warehouse?

Before understanding what a data warehouse is, let us first get the definition of a warehouse. In the traditional sense, a warehouse is a building where a company or an enterprise stores its raw materials or finished goods for distribution. For example, many of us have ordered items from Amazon or eBay. Those items are delivered to you from a nearby warehouse. Now that you have understood what a warehouse is, let’s talk about a data warehouse, its components, its structure, and how it differs from a database.

Data Warehouse definition

A data warehouse is a centralized location where your enterprise data from database and file systems are stored for further analysis and interpretation. The analysis results are used to make informed and better business decisions. Normally, data scientists, data engineers, business analysts, and decision-makers often interpret output from the data warehouse using SQL clients, business intelligence (BI) tools, and analytics apps.

By combining all the data in a central hub, companies can better understand their customers. With time, the data warehouse builds an invaluable historical record of all information that has been used by the organization.

Data Warehouse Architecture

Data warehouse architecture is the process of designing a data warehouse. Typically, there are three tiers of architectural designs:

• Single-tier architecture: This type of architecture is hardly used to build real-time data warehouse systems. Instead, they are commonly used for batch and real-time operational data processing. A single-tier design comprises one layer of hardware to limit data space.
   
• Two-tier architecture: Here, the analytical and business process are separated from each other. This is to augment the level of efficiency and control.
   
• Three-tier architecture: This architectural design is separated into a top, middle, and bottom tier called the source layer, reconciled layer, and data warehouse layer, respectively. Three-tier architecture designs are suitable for systems that have long life cycles. An additional layer of analysis and review is done when changes are made to the data. This is to ensure the absence of errors.

How a Data Warehouse Works

Information from different systems is collected in the data warehouse, which can be divided into four stages or tiers – Collect, Store, Analyze, and Consume.

At the Collect stage, the data is fetched from the relational databases in raw form and stored in a staging layer. The fetching is done through the extraction, transformation, and loading (ETL) process. This data must be cleaned and processed.

Now that the data has been collected and stored, it is time to analyze. At this stage, you decide what kind of insights you want from the data. Different transformation processes occur to shape the data in a form that can be used to get better business insights.

At the Consume stage, the transformed data is presented in a readable and visually pleasing format using graphs, objects, and diagrams.

Basic Characteristics of a Data Warehouse

Now, let us quickly understand the basic characteristics of a data warehouse:

1. Integrated: The data warehouse enables you to integrate the data coming from multiple systems in a centralized hub.
    
2. Time Variant: This means that whatever data is stored in the data warehouse varies with time. It helps experts to do analysis based on time, such as measuring employee performance.
    
3. Non-Volatile (Static): Whatever data come into a data warehouse remains unchanged. Whenever a change occurs, there is a separate mechanism to store the new information.
    
4. Subject-Oriented: Data warehouses are built for specific purposes. If you want to analyze data related to a specific area of interest, you have to design your data warehouse considering that subject.

Data Warehouse Vs. Database

At first glance, many people may think that a data warehouse and database are synonyms. However, intrinsically, they differ.

A database is a transactional system that constantly updates and monitors data in real-time to ensure that only the most recent data is kept in the system. On the other hand, a data warehouse stores everything, both old and new data. This is to ensure historical data, which may still be needed for business processes are available.

Benefits of Data Warehouses

One of the main purposes of a data warehouse is to give the business a competitive advantage. It provides a source where vital information can be stored, analyzed, and tracked over time. This helps companies to be able to make better and more informed decisions.

Data warehouses provide a platform where BI tools can be used to show reports, dashboards, and other interfaces. A data warehouse that is well-designed ensures queries are fast with much data delivery. It also ensures that users can break down data into sections for detailed examination and analysis.

Companies can use the information stored in a data warehouse to analyze past performance and make changes to improve.

Steps To Implement a Data Warehouse

There are some business risks associated with implementing a data warehouse. Employing a three-prong strategy can help to mitigate these risks.

• Enterprise Strategy: First, you need to identify useful tools and current architecture. Also, it is important to get the dimensions, facts, and attributes of the data warehouse.
   
• Phased Delivery: The implementation of a data warehouse should be done based on a specific subject. Entities that are related should be implemented first and then integrated together.
   
• Iterative Prototyping: The data warehouse should only be implemented in stages. It should be developed over time and tested iteratively.

Conclusion

Data warehouses are a vital part of every company or organization and need to be implemented to keep records and measure performance. This article provides an overall picture of a data warehouse. However, with the advent of new technologies and different types of data, new data warehouse designs have started cropping up. There are technologies like data vault, data lake, and data mart, which can also be used to provide solutions to a specific business problem.