Data Warehouse vs Database: What's the Difference?

Databases and data warehouses are both tools that organisations use to store, access, and analyse data. However, they have key differences and are used for specific purposes.

Uncover the distinctions between data warehouses and databases, what kind of problems each tackles, and how they serve different purposes in data analysis. Also, explore 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, but they also differ. 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. Databases’ main purpose 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.

Look further into both data warehouses and databases to learn more.

What is a data warehouse?

A data warehouse is a large, central location where data is managed and stored for analytical processing. The organisation accumulates data from various sources and storage locations. For example, inventory numbers and customer information are likely managed by two different departments. 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 they're used in. Consider these three examples:

• Health care: A data warehouse may carry patient information that health care professionals can use to understand certain conditions or evaluate treatment methods. For example, a health care 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 and media: A marketing firm may use a data warehouse to track the success of a campaign or product launch. Dashboards and reports can be created and shared within an organisation to gauge performance, sales, and customer service interactions. Media outlets, meanwhile, could use it to facilitate product promotions and inform decisions related to sales and distribution.

• Housing and the public sector: Data warehousing aids in intelligence gathering, with government departments relying on this method to ensure records' accuracy, including tax-related data.

Data warehouse professionals

Data science professionals work with data warehouses in their careers. The list below defines a few examples of careers in this field:

• Data warehouse analyst: A data warehouse analyst researches and evaluates data from a data warehouse. In this role, you use your insights to make recommendations for improving an organisation's data storage and reporting methods. You may also collect and visualise your findings to assist with other business processes. Data warehouse analysts in the UK earn an average base salary of £62,387 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. As a BI analyst, you build reports, dashboards, and other visual aids using programming languages and data visualisation platforms like Python, SQL, and Tableau. Business analysts in the UK earn an average base salary of £39,609 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. They might be responsible for setting project scopes, choosing the right software tools, and leading strategic solutions. Data warehouse engineers in the UK earn an average base salary of £48,757 per year [3].

What is a database?

A database stores information from a single data source for one particular function of your business. They can process many simple queries (requests for data results) quickly. Databases often record real-time data like 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.

Should you want to build your own database, 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 just 36 hours.

Database use cases

Like data warehouses, databases have many different business applications across many industries. Databases can also be for personal use. A few examples include:

• Electronic health record (EHR): In health care, patient information is input to an EHR during their first visit. Then, it's updated 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 them at any time from any facility that shares access permission.

• Consumer recommendations: Online streaming services such as Netflix and Spotify use databases to track TV shows and song offerings, as well as your viewing and listening preferences. This information is stored on NoSQL databases and used to recommend content you might like based on your user history.

Database professionals

People who work with databases in their careers are typically data science professionals. The following list defines a few examples of careers in this field. Remember, the job titles below can vary from industry to industry.

• Database administrator: A database administrator ensures that a database runs efficiently. In this role, you create and organise systems to store data like 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 the UK earn an average base salary of £37,506 [4].

• Database architect: Database architects design and build databases. As a database architect, you create the standard for operating, programming, and securing a database. Your primary goal is to make it easy and efficient for data analysts, data scientists, and engineers to access data. Database architects in the UK earn an average base salary of £65,525 [5].

• Data analyst: Data analysts gather, clean, and study data sets to help solve an organisation’s problems. Database analysts in the UK earn an average base salary of £36,489 [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 with Coursera

A data warehouse and a database are two different structures used to store and analyse data within the data science field. Learn from an industry leader online with one of these two Professional Certificates in data engineering: Firstly, consider this IBM Data Engineering Professional Certificate. You can prepare for a data engineering career by acquiring in-demand skills and hands-on experience in just a few months. If you’re a beginner, IBM also offers the Introduction to Data Engineering course.

Secondly, with the Meta Database Engineer Professional Certificate, you can develop job-ready skills for a sought-after career while earning a Meta Database Engineer Professional Certificate. No prior degree or experience required.