The Key Differences Between a Data Warehouse and a Database

Today’s businesses run on data, to the extent that it’s called the “new oil.” It’s data that makes sure that you can see changes in the markets as (or sometimes before) they happen; know what your customers want even when they’re having trouble articulating it; and track your competitors’ positioning and messaging. Without data, you risk being blind even to bottlenecks and inefficiencies within your own organization.

That said, data on its own is no magic bullet. You need to gather data, making sure that you aren’t missing vital data points and that your data is up to date and reliable. You need to process and clean it for duplications; analyze it and mine it for actionable insights; and then make sure that all your relevant teams can access those insights whenever they want. Otherwise, all you have is a lot of numbers taking up memory space.

This might sound like a tall order, but you can meet it when you deploy the right data management ecosystem. Business data has reached the point where there’s no single tool that can cover all your data gathering, processing, storing, and analyzing needs, but you can put together a suite of data tools that are more powerful than any single data platform. This includes both a database and a data warehouse.

Before you start comparing between different solutions, like Snowflake vs Databricks, here is an overview of the different natures and capabilities of databases and data warehouses, and how they can work together to support your business growth.

What is a database?

The term database can be deceptively familiar, because we’ve been using it ever since databases were typed on paper and stored in filing cabinets. A database is a way of storing real time, current information about just one aspect of your business.

One key differentiation between databases and data warehouses is that databases hold current information, not historical data. The data in a database is constantly updated, using a processing system called OnLine Transactional Processing (OLTP), which updates large datasets in the shortest possible space of time.

Why use a database?

You’d use a database to process daily transactions, like a sale, an item moving from your warehouse to the shelf in a store, or a visitor checking into a hotel.

You need data that is accurate, reliable, and always up to date, and databases help make that possible. Databases are optimized to capture every action, whether that’s a sale, a marketing conversion, or a visit, and update datasets as fast as possible, so that you never have gaps in your data.

Databases can also support thousands of concurrent users, making it possible for myriads of employees to all be checking records at the same time without dragging down system speeds.

Databases apply data normalization, deduplicating data and deleting any redundant fields. This makes it take up less memory space, but also ensures that data is non-repetitive and reduces room for errors. To this end, databases are also ACID (Atomic, Consistent, Isolated, and Durable) compliant, to maintain data integrity during data changes, and even in the case of a power failure, and database SLAs promise extremely high uptime.

Databases bring you the benefits of reliable, real time data that’s trustworthy and current, but the constant updates mean that it’s not well suited to applying analytics tools, tracking trends over time, or taking a deep dive into analytics insights.

What is a data warehouse?

Data warehouses store historical data, bringing it together from a number of different sources. While data warehouses are updated regularly, those updates don’t take place in real time, because the focus for data warehouses is preparing data and making it accessible for extensive analytics.

To that end, data warehouses typically offer built-in analytics and data visualization tools, as well as easy integrations with more powerful, standalone external analytics platforms. Unlike databases, data warehouses use a type of processing called OnLine Analytical Processing (OLAP), which swiftly analyzes massive amounts of data.

Because of the emphasis on analysis rather than data gathering, uptime is less of a concern for data warehouses. Many of them build in periodic downtime to allow for new data uploads, and fewer will be ACID compliant.

Why use a data warehouse?

You’d use a data warehouse to bring all your data together and support faster, more powerful analytics that produce deeper insights and more reliable business reporting. For example, predicting customer churn to refine marketing efforts, forecasting demand to focus on specific regions and products, or segmenting customers to serve them more relevant content.

Data warehouses have capabilities that make data insights both faster and more informative. They integrate data from multiple sources, verifying and enhancing datasets so the data is more rich and reliable.  While databases normalize data, data warehouses de-normalize it, which is query-efficient and supports swifter responses.

Data warehouses make data more accessible for built-in and external analytics tools, and are optimized to execute queries as fast as possible. They include intuitive dashboards and data visualizations, which allow people without a data science background to quickly take in and understand insights.

Because they are simple and quick to use, data warehouses make the entire data system resilient and scalable, removing the need for data scientists to act as gatekeepers. By enabling employees to run as many queries as they like, data warehouses help democratize access to data insights across your organization.

Databases and data warehouses work together

Both databases and data warehouses have a role to play in an integrated data management system, with one supplying real time data and the other making it accessible for analytics insights and non-expert users. When used correctly, they can work together to answer your queries, keep your finger on the pulse of changing business trends, and assist you to assess risk, spot opportunity, and make better data-driven business decisions.

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