Data Warehouse vs Database: What's the Difference?

Databases and data warehouses are both tools that organisations use to store, access, and analyse data. But, that's not to say they're exactly the same—or even used for the same purposes.

Explore the differences between data warehouses and databases, their use cases, and how they're each used to solve problems. Also, you can learn about flexible, cost-effective courses that can help you develop critical data skills today.

What's the difference between a data warehouse and a database?

Data warehouses and databases both act as data storage and management tools. However, it’s important to acknowledge a few key differences. First, data warehouses have analytical capabilities. They enable companies to make analytical queries that track and record certain variables for business intelligence. In contrast, a database is a simple collection of data in one place. The main purpose of a database is to store data securely and allow users to access it easily.

Organisations often need both databases and data warehouses to manage the massive amounts of data they produce daily. For example, a clothing company may use one database to store customer information and another to track website traffic. They can use a data warehouse to compare both databases on a historical scale to reveal insight into consumer trends.

What is a data warehouse?

A data warehouse is a large, central location that manages and stores data for analytical processing. The data comes from various sources and storage locations within an organisation. For example, two different departments likely manage inventory numbers and customer information. However, a data warehouse can collect and present both data types on the same dashboard. Then, data science professionals analyse the data for patterns and use their findings to help organisations make informed business decisions.

Data warehouse use cases

Data warehouses have many different business applications. Their use cases may depend on the industry. Below are two examples:

• Healthcare: A data warehouse may carry patient information that healthcare professionals can use to understand certain conditions or evaluate treatment methods. For example, a healthcare data scientist may analyse the information in a data warehouse to determine how often cancer patients over 25 receive chemotherapy rather than radiation treatment and why.

• Marketing: A marketing firm may use a data warehouse to track the success of a campaign or product launch. The firm can create and share dashboards and reports within the organisation to gauge performance, sales, and customer service interactions.

Data warehouse professionals

People who work with data warehouses in their careers are data science professionals. The following data warehouse careers exist, but the job titles can vary slightly from industry to industry.

• Data warehouse architect: A data warehouse architect oversees the design, deployment, and maintenance of data warehouse systems. Key work responsibilities include analysing firms’ data requirements and developing online analytical processing (OLAP) systems. Data warehouse architects in India earn an average base salary of ₹12,85,234 per year [1].

• Business intelligence (BI) analyst: A business intelligence analyst uses data warehouses to develop company-wide and department-wide business insights through data visualisation. They build reports, dashboards, and other visual aids using programming languages and data visualisation platforms such as QlikSense, SQL, and Tableau. Business analysts earn an average base salary of ₹8,80,000 per year [2].

Data visualisation is the visual representation of information. Charts and diagrams are examples of data visualisation methods.

• Data warehouse engineer: A data warehouse engineer builds and manages data warehouse strategies. Data warehouse engineers may also work toward automating the deployment of data pipelines. Data warehouse engineers earn an average base salary of ₹9,84,929 per year [3].

What is a database?

A database stores information from a single data source for one particular function of your business. Databases can process many simple queries (requests for data results) quickly. They often record real-time data such as e-commerce transactions or updates to a patient's health record. Databases can handle “big data” but can also be as small as an Excel spreadsheet. Big-data databases can convert structured and unstructured data into formats that analytics tools can use.

Relational database vs. non-relational database

Relational databases, also called SQL databases, store data in rows and columns like an Excel spreadsheet. Non-relational databases use one of the four storage models (document, key-value stores, graph, and column) for more flexible storage and complex queries.

Database use cases

Like data warehouses, databases have many different business applications across many industries. Databases can also be for personal use. Organisations may use the following types of databases to track important information:

• Electronic health record (EHR): In healthcare, providers can input patient information into an EHR during the first visit. Then, medical information updates during subsequent visits. This information stays secure and confidential on the platform. It updates the time and date of the appointment along with any other relevant symptoms and diagnoses. EHRs also enable clinicians to access the data at any time from any facility that shares access permission.

• Consumer recommendations: Online streaming services such as Netflix and Spotify use databases to track the TV shows and songs they offer, as well as your viewing and listening preferences. NoSQL databases store this information to recommend content you might like based on your user history.

Database professionals

Careers in data science often involve working with databases and include the following positions.

• Database administrator: Database administrators ensure that a database runs efficiently. They create and organise systems to store data such as financial information, product specifications, and customer orders. Database administrators also manage permissions so that this data is available to those authorised to access it. Database administrators in India earn an average base salary of ₹8,50,000 per year[4].

• Database architect: Database architects design and build databases. They create the standard for operating, programming, and securing a database.They collaborate closely with software designers and consultants to develop robust databases serving large user bases. Database architects earn an average annual salary of ₹22,25,000[5].

• Data analyst: Data analysts gather, clean, and study data sets to help solve an organisation’s problems. Additionally, they examine complex data sets to uncover trends, correlations, and patterns within the data. Data analysts earn an average base salary of ₹6,70,000 per year [6].

What is data cleaning?

The term "data cleaning" refers to removing or repairing corrupt, incomplete, duplicated, or otherwise incorrect data.

Learn more about data engineering on Coursera.

Data warehouses and databases are versatile tools, each tailored for specific aspects of data management. While databases cater to operational processes like querying, data warehouses help with data analysis and reporting. 

If you're ready to start a career in database engineering or learn how to use data-based tools in your organisation effectively, consider IBM’s Data Engineering Professional Certificate on Coursera. Also, the University of Colorado Boulder's Relational Database Design course offers step-by-step guidance to turn your raw ideas into a relational database. You’ll practise online with real-life cases and get comfortable building one in this 34-hour course.