A complete guide to data warehouses

Data processing has become essential for many business operations. You probably know that a data warehouse can help store and manage data, but it might be unclear what exactly it is or how it differs from other systems and tools.

In this article, we’ll explain why it’s called a warehouse, how it works, and why many companies rely on one to operate and make critical decisions. After reading it, you’ll be equipped to discuss the benefits with your team and decide if a data warehouse can help you achieve your business goals.

This post will cover:

• What a data warehouse is
• The architecture of data warehouses
• Data warehouse vs. data lake vs. data mart
• Data warehouse benefits

What is a data warehouse?

A data warehouse is a digital repository that pulls large amounts of data from databases and transactional systems. Its purpose is to process, manage, and store data so that businesses can identify trends, especially relating to customer behavior. Data warehouses produce business intelligence, which can help teams across the organization make better decisions.

Like an industrial warehouse, it serves as a large central location to receive materials — in this case, data — and then to organize them systematically so that the right pieces can be found, reassembled, and used elsewhere. Data from many different channels in different formats can be completely overwhelming, if not useless, without a processing center that can handle it, preserve it, and make it accessible.

Data warehouses are great for dealing with data of all kinds, including images and video, and for taking data in large quantities from various sources. Some examples of sources include transactions via point-of-sale (POS) systems, customer relationship management (CRM) software, a customer data platform (CDP), enterprise resource planning (ERP) software, social media, and devices via Internet of Things (IoT).

For a helpful description of a data warehouse as a single source of truth, check out this video:

https://www.youtube.com/watch?v=AHR_7jFCMeY

Types of data warehouses

The concept of data warehousing has been around for decades. Historically, the relevant hardware and digital equipment were housed and managed on-site. As time has passed, data quality and storage technology have improved, leading to better analytical capabilities. With cloud storage, smaller businesses can now find the same benefits previously available only to companies large enough to set up their own warehouse.

Let’s look closer at each of these two approaches:

• On-premises. This style was once the only option. Organizations have been doing it for years, and some still do. On-premises warehousing means hosting the data on your own servers and managing all the physical and technical components. It can offer greater security than the alternative cloud data warehouse, and it’s often necessary for government agencies and others to comply with specific regulations. However, on-premises operations can be difficult to scale and adapt to changing needs.
• In the cloud. Increasingly, data warehouses have been moving to the cloud. Cloud data warehouses offer certain benefits, such as management by outside parties. Companies don’t have to figure out how to store data on their servers, maintain that infrastructure, or scale their systems when needed. Cloud data warehouses offer greater flexibility at a lower cost, so many companies choose this route.

The future of data warehouse technology

The outlook of data warehousing will likely be cloud-based. Influencing this trend are some additional benefits keeping data in the cloud:

• Lower risk. Many organizations feel that there is less risk with keeping data in the cloud than there is with keeping it locally. And they benefit from avoiding the legal issues and regulatory requirements that arise with on-site data storage.
• Opportunities for small businesses. A data warehouse costs a lot to set up, requiring dozens of people to build it, keep it running, and optimize the data within it. But moving toward cloud-based storage opens a lot of opportunities for small businesses to be able to store larger amounts of data. The cost is significantly less to keep data in the cloud, freeing up huge chunks of the budget that would otherwise go to on-site servers. This low cost means that even smaller businesses will benefit from cloud-based data storage.
• Self-service. Keeping data in the cloud facilitates self-service. Self-service data warehousing allows business users to access and manipulate data independently, letting them make faster decisions in response to changing business needs. Self-service can also democratize access to data across the organization. It can foster a data-driven culture where insights are shared and utilized by a broader audience.
• Analytics capabilities. Another future aspect of data warehousing will be combining it with analytics in the cloud. Large companies are already employing huge analytical sets that work with data warehouses. A step further than this would be artificial intelligence components built into data warehouses to help you use machine learning for business decision-making. Artificial intelligence is becoming more capable, which eliminates the need to pay for professional data scientists.
  
  Whether on-premises or in the cloud, data moves through the warehouse in stages and steps. Let’s look at that structure to clarify how it works.

The architecture of data warehouses

Data warehouses are configured in terms of tiers. They typically follow a three-tier system where data arrives from multiple sources before being processed and made available through an interface that lets users perform queries and access the data in helpful formats:

• Bottom tier. Incoming data from multiple sources and interactions enter a repository. The data goes through extraction, loading, and transformation (ELT).
• Middle tier. As data moves through the middle tier, it is restructured for analysis. Just as an industrial warehouse has different shelves and sections to sort and store products, the data warehouse provides a system for ordering data and making it findable for various uses.
• Top tier. Finally, at the front end, clients can view and analyze data. They can perform queries for various purposes without disturbing the underlying tiers of data storage and ordering.
  
  From one tier to the next, a data warehouse receives, cleans, handles, stores, and packages information. This view of data warehouse architecture can help explain where other data processing tools and concepts fit in. We will look at some of those terms next.

Data warehouse vs. data lake vs. data mart

Several data terms are commonly used in conversations about data warehousing, and they can often be confused. Let’s define those terms and discuss how they differ:

• Data lake. A data lake is used to store data for later. You can throw whatever kind of data in any format into data lakes, and then at some point in the future, you can extract value from it. Like a database, a data lake is a holding area for data, but in a data lake it has not yet been filtered or organized. A lake might sound large to you, but it’s relatively small.
• Database. A database is typically used to capture raw data for real-time use. It is also a smaller repository, but the data types collected here are more specific. While it can hold all different types of data, both structured and unstructured, a database is focused on one area of the business, and the information is more likely to be used in real time rather than saved for later. It has more limited sources and uses than a warehouse does, and it does not offer the analytical capabilities that a data warehouse does.
• Data warehouse. The warehouse is higher on the pyramid of big data storage in terms of size and function. It is larger than a database or a data lake, and its function is more historical than immediate, although it can store real-time information too. It has a much larger scope and capacity to handle diverse kinds of data for diverse purposes. The best way to use a data warehouse is to connect data across channels.
• Data mart. Like a database, a data mart is useful for holding data related to one area of the business. Data marts differ from databases in that they hold data that has been processed in some way. A data mart is like a one-stop shop for certain users. It can include data taken from a data warehouse and serve as the final distribution center for that data. Multiple data marts can be established for different purposes.

The differences between these terms make more sense when you see how they are related. They are components in what is often a chronological process:

1. Data is imported into the system from a variety of inputs. That data is initially stored in a database or a data lake.
2. The data is processed and then moved into a data warehouse. From this point, teams can analyze their data.
3. Data can be taken a step further and moved into a data mart, which categorizes the data by department for easier and quicker analysis.

Data warehouse benefits

A data warehouse does more than store data. The main benefits of a data warehouse include:

• Informed decisions. You can make better business decisions with the extensive, high-quality intelligence and analysis found only in a warehouse.
• Consolidation. Data from many different sources can be gathered in one place to see the bigger picture, make faster connections, and access everything you need in one place.
• Standardization. Instead of storing data in different formats that can be difficult to interpret and use, a warehouse promotes good data hygiene and consistency.
• Speed. Having standardized, organized data makes querying faster because teams don’t have to sort through different reports from different departments. They can dedicate their time to analysis rather than wasting it on searching.

Get started with data warehousing today

Data warehouses can lead to better business decision-making because they help gather large amounts of historical data in one place, organize that data, and make decisions backed by greater business intelligence. A data warehouse can become a single source of truth that makes data available and useful for multiple analytical needs.

Generally, data warehouses are for larger businesses. But cloud-based data storage opens new opportunities for small- and medium-sized businesses to store larger amounts of data. With a cloud solution, you’ll be ready to scale as you grow and adapt to evolving analytical needs. A warehouse makes it possible to find and act on information that is less easily realized in smaller sets and systems.

If you’re ready to start a conversation with your team, share this article and discuss how your business could benefit from a data warehouse. It might be a good idea to make a brief list of data warehouse solutions that would support your work.

Adobe can help

A data warehouse can provide a rich underpinning for the powerful data processing you need to understand customers and make better business decisions. A data warehouse is one of the features included in Adobe Analytics, which brings together cross-channel data to provide real-time insights.

Explore the benefits of Analytics or request a demo to learn more.