Data Warehouse vs. Database: Differences Explained

If you're new to working with data, you might have heard of databases and data warehouses. But do you know what sets them apart?

Knowing the differences between data warehouses and databases can clear up a lot of confusion for many people, especially with the volume of data we have these days.

In this blog post, I'll discuss the differences between these two types of data systems. I'll also provide some examples to help illustrate the points made. 

Data warehouse vs. database, quickly

If you just need a quick answer, here’s the TLDR:

• A data warehouse is a data system that stores data from various data sources for data analysis and reporting. Data warehouses are often used for data analytics and business intelligence tasks like market segmentation and forecasting.
• A database is a data storage system for recording information collected from applications in an organized format.

Now let’s look at each in detail.

What is a data warehouse?

Data warehouses are used for data analysis and reporting. With data warehouses, data can be analyzed over time to gain insights into trends and patterns.

Having a data warehouse in your organization can bring about many benefits, including:

• Having a central data repository with data cleansing, data normalization and data transformation capabilities
• Enhancing data security and data privacy
• Enabling real-time data analytics
• Collaborating on data

Data warehouses also provide a central data source that can be easily accessed from multiple applications. It contains data from multiple sources, usually from transactional systems such as point-of-sale or customer relationship management (CRM) software.

In my experience as a data analyst, data warehouses are typically built up as part of data pipelines to ensure good reporting. All data ingested by data warehouses are cleaned and segmented according to the purposes and needs of business intelligence reporting.

Here are some examples of common data warehouses:

• Amazon Redshift
• Microsoft Azure Data Warehouse
• Google BigQuery
• Snowflake

I first experienced working with a data warehouse when I pursued my Google Data Analytics Certification. I had opportunities to work with Google BigQuery to load CSV files and create a data warehouse for my project.

Through this experience, I realized that having a cloud data warehouse was extremely helpful in structuring and storing data for analysis — as compared to a database. Although the learning curve to learning how to set one up can be hard, it is an essential skill in the data analytics field.

In recent years, there has been a rise in some data warehouses that fit the modern data stack, such as Snowflake. Snowflake provides a modern data warehouse as a cloud service, with features that provide scalability and security.

In addition, cloud data warehouses have become increasingly popular as organizations move towards cloud-based solutions. This has also opened up opportunities for smaller businesses to access data warehousing capabilities that were previously only available to larger enterprises.

(Remember: data warehouses differ from data lakes.)

What is a database?

A database is a data system that stores data in an organized format, typically on a server. It is used to store data collected from applications and other sources.

Databases are commonly used for data management tasks such as data validation, data manipulation and data retrieval. They are also often used for online transaction processing (OLTP) systems, such as customer and sales data.

Databases can come in different forms based on their structure. Some common forms of databases include:

• SQL relational databases
• NoSQL databases
• Cloud databases

Some common databases include MySQL, Oracle Database, and Microsoft SQL Server.

Relational database vs. non-relational database

A relational database is a type of database that organizes data into tables, with columns and rows. It stores relationships between different data points in order to make it easier to query and retrieve specific information.

On the other hand, non-relational databases do not follow a strict structure like relational databases. Instead, they use key-value pairs or document-based models to store data.

Non-relational databases, also known as NoSQL databases, can be used when dealing with large amounts of unstructured data, such as user-generated content on social media platforms or IoT sensor data.

(Related reading: SQL vs NoSQL databases.)

Data warehouses & databases: 4 key differences

With the core concepts out of the way, let’s clarify the differences. Some major differences between a data warehouse and a database include the following – and we’ll talk about each one below:

• Data modeling
• Data storage
• Data type
• Optimization

Data modeling

Data warehouses use dimensional data models to organize data into meaningful categories for analysis. Conversely, databases typically use relational data models, which are better suited for data management tasks such as data manipulation and data retrieval.

This means that data warehouses are designed to be more focused on downstream analytics!

Data storage

Data warehouses tend to store large amounts of data from multiple sources, typically for long-term storage and reporting. Data warehouses tend to have more extensive storage due to the need to retrieve historical data for analysis.

For example, if you're going to do a year-on-year analysis, you've got to have data from at least a few years back.

Databases, on the other hand, store data in tables and are used for short-term data storage or data manipulation tasks. A database is mainly used for:

• Data manipulation
• Data retrieval tasks

It stores data in tables, and data is typically stored only for a short time. However, some databases can also store quite a large amount of data as well, depending on the use case and design.

Data type

Data warehouses typically store data that's been cleaned through some ETL processes and are either segmented or summarized in a suitable format for analysis. Databases usually have data that's less clean but have a higher level of detail.

In my experience, databases will be where more of your raw data will come from. A data analyst will typically take these highly detailed real-time data from databases and clean them. Moreover, data may be summarized in a lower level of detail.

For example, data taken from databases can be summed by month. This will be useful for monthly reporting on website users, customers, and company financials. Subsequently, the data will be loaded into data warehouses, where detailed data will no longer be stored.

Optimization

Having a data system that's optimized is crucial in maximizing value from your data. That's why both the data warehouse and the database have unique optimizations to suit their specific needs. Let's have a look at their differences in optimization:

• Data warehouses use OnLine Analytical Processing (OLAP) as a layer to better optimize for processing large volumes of data at high speeds.
• Databases use OnLine Transaction Processing (OLTP) to process data more efficiently for data management tasks.

Best practices for data warehousing & managing databases

When it comes to managing data warehouses and databases, there are some important best practices you should be aware of:

• Keep data up to date. Make sure data is as current as possible by using data pipelines or automated data refresh processes that update data in real-time or close to it.
• Use a suitable data model. Make sure data is organized and structured in the right data model that best fits your data needs and usage.
• Optimize data. Ensure data is optimized to the fullest extent so that query performance can be improved. Use data compression techniques or other optimization methods such as OLAP or OLTP to improve data processing speed.
• Adhere to security best practices. Establish data security policies and ensure data is encrypted whenever possible. Also, ensure data access permissions are set to the right levels and data privacy laws are followed.
• Monitor data usage. Track data usage metrics and understand how data is being used better to optimize data pipelines and processes for data analysis.

Following these best practices will help ensure data is managed, stored, and used in the most effective way possible.

When should you choose a data warehouse?

Data warehouses are best suited for long-term storage and analysis of historical data, making them ideal for businesses that rely heavily on analytics and reporting. This includes industries such as retail, finance, and healthcare, where analyzing large volumes of data is crucial for decision-making.

When should you choose a database?

Databases are essential for short-term data storage and manipulation tasks. They are ideal for businesses that require quick access to real-time data for transactional processes or operational tasks. Industries that often use databases include e-commerce, banking, and customer service.

Using both data warehouses and databases

While there are distinct differences between data warehouses and databases, they can also be used together to maximize the value of your data. With a combination of both data systems, you can have a reliable source for historical data analysis while also having real-time access to operational data.

This is especially useful for businesses that need both long-term analytics capabilities and quick access to real-time data.

For example, when storing data for data governance and regulation purposes, a data warehouse can be used for long-term storage, while a database can be used to quickly retrieve real-time data for data analytics and business analysis.

Choosing data storage

To summarize, a data warehouse and a database each have their own unique data storage and data processing functions, as well as capabilities that can be beneficial to different organizations. They can be used in isolation, but most companies use a combination of both for a more comprehensive data stack.