Guide to Data Warehouses

Data warehouses bring your structured and semi-structured data sources together into one centralized location. The goal of buying a warehouse is to have an excellent place to store data — and later use it to analyze patterns and find insights to guide your company. While structured data only makes up about 20% of your company’s information, it can be very powerful to show you customer behavior trends and help you gain quantitative insights. This guide will go into detail about what data warehouses are, what their benefits are, and some best practices for creating one.

A data warehouse is a centralized repository that stores structured data from multiple sources, designed specifically for querying and analysis. Unlike transactional databases, data warehouses are optimized for analytics and help organizations to gain actionable insights from large datasets and many data sources. They serve as a foundation for business intelligence tools and reporting.

Many organizations gather information such as point-of-sale transactions, marketing automation, customer relationship management (CRM), and more, and a data warehouse is a place where it can all be stored and analyzed together. While you could pull up data sources separately, a data warehouse lets you analyze that information all in one place for deeper insights.

One of the main purposes of having data warehouses is to create a place where analysts can retrieve information for business intelligence. That data can be turned from rows and columns of numbers into knowledge that can help your organization make informed decisions.

While a data warehouse is similar to a database, they do differ in a few key ways:

• Purpose: A database is primarily used for day-to-day operations such as storing and managing real-time transactional data while a data warehouse can be used for analytical processing and long-term storage of data.
• Scope: Databases typically gather current data while a warehouse is primarily for storing large amounts of data, including both operational data and historical data.
• Use: A database is particularly useful for real-time business operations, such as processing sales transactions or managing customer records, while a data warehouse is more useful for strategic planning, trend analysis, and business intelligence reporting.

In general, most data warehouses will use a layered structure:

The highest layer includes user-facing tools such as dashboards , BI tools, and data visualization platforms. It exists to allow users to interact with and query the data. If you’re not a data warehouse engineer, it’s likely the only layer of the warehouse that you’ll see, while the lower two work behind the scenes to aggregate data and respond to user queries.

The middle layer processes queries and enables analytics. Most commonly, this level is built around an analytics engine called the online analytical processing (OLAP) model, which might look like these three subtypes:

• ROLAP: Uses relational databases to perform multidimensional analysis with SQL queries, offering scalability for large datasets but often with slower performance compared to other OLAP types.
• MOLAP: Stores data in multidimensional cubes, which supports fast query performance and precomputed aggregates, making it ideal for scenarios requiring rapid response times.
• HOLAP: Combines the strengths of ROLAP and MOLAP by storing some data in relational databases and some in multidimensional cubes, balancing scalability and performance for complex analytical needs.

This layer isn’t typically visible to the average database user, but it does power much of the queries you might use.

The lowest level is where the data flows from all of its sources to the warehouse server to be stored. It’s the central repository where data is aggregated, cleaned, and stored through processes called extract, transform, load (ETL) or extract, load, transform (ELT). You might choose ETL to prioritize safety because you can encrypt data before putting it into your warehouse while ELT allows you more control over your data since you load it first. Once the data is added to this layer, it can then be analyzed.

Data warehouses let organizations analyze large amounts of information that you can use to make decisions. These are three unique benefits of data warehouses:

One major advantage of a data warehouse is that it combines data from disparate sources into one centralized place for comprehensive insights. You’re able to bring together information from areas such as customer transactions with user activity and to get the big picture of what customers are doing. Data warehouses also support ad hoc queries and predictive analytics to make business intelligence as intuitive as possible.

Warehouses can reduce the need for manual data aggregation and cleansing, something that could be taking your teams’ time and is prone to human error.

Automating a warehouse can handle data cleansing by identifying inconsistencies, duplicates, and errors, and that helps ensure that the data is accurate and ready for analysis. This step reduces the reliance on manual efforts, freeing up your team to focus on strategic tasks rather than repetitive maintenance. With improved data processing, you’re able to accelerate your analytics, find helpful insights faster, and stay competitive in dynamic markets.

Data is particularly useful if it can help you determine the path for your business and improve your customers’ experiences. To do this, you’ll want to make sure you have access to reliable and consistent data, and a data warehouse is a way to do that. It helps you bring all of your data sources into one centralized system, eliminating discrepancies and ensuring everyone works with the same accurate information. This clarity helps your teams feel more confident in decisions and minimizes the risk of miscommunication across teams.

A cloud-based data warehouse is a centralized system for structured and semi-structured data with loading, cleansing, integration, processing, and data storage, all within a public cloud environment.

While a cloud-based data warehouse and traditional data warehouse share a lot of similarities, there are a few distinctions between the two:

• Scalability: Cloud-based systems grow with your data needs while a traditional warehouse may need new hardware to expand with your company.
• Cost efficiency: Cloud tools often come with pay-as-you-go pricing models to reduce upfront costs, whether you’re starting small or already have established processes. A traditional data warehouse requires upfront costs.

Any type of data warehouse can be beneficial to your company and allow you to extract insights. However, there are a few perks that come from using a cloud-based data warehouse specifically:

• Easy integration with other cloud services, so you can have all of your systems work together and in one place.
• Simplified management with provider-handled infrastructure, so your team isn’t building everything from scratch.
• Doesn’t require your team to purchase, maintain, or upgrade hardware.
• A cloud-based warehouse solution provides flexibility and scalability without requiring significant upfront infrastructure investments — in addition to payment plans that let you pay for what you need.

There are a few related topics that are often confused with a data warehouse, but each of them has a few distinctions that make them unique from a traditional data warehouse.

A data warehouse is specifically designed for structured data analytics. The goal is to centralize your information and accumulate historical records of all of your data sources.

A data lake is also a centralized repository of data; however, your data is stored in its raw form in the data lake–structured or unstructured–while it’s transformed when it’s put into a data warehouse. This means a data lake is a way to centralize all your raw data from a wide variety of data sources.

A data lakehouse combines features of data lakes and warehouses for flexibility and performance. Data lakehouses have an open system design that lets you run analytics from a centralized repository on structured, unstructured, and semi-structured data. Having features of a data warehouse and a data lake combined has many advantages–among them, letting your employees work from one system instead of having to switch between the two.

A data mart is a subset of a data warehouse that’s designed specifically for a particular unit, department, or line of business within your organization. For example, you may create data marts for your finance, sales, and marketing teams. It’s specifically used for structured data that’s been transformed, just like a traditional data warehouse. Companies use data marts to create highly specific insights more quickly than with a generalized warehouse.

Developing a data warehouse that truly supports your organization’s needs requires a deliberate approach and the right tools. Below are some strategies that can help you determine which data warehouses are the right fit for your business.

Take the time to clearly define your organization’s overall objectives, your data strategy, and how your warehouse could help. What questions do you want your data to answer? Which metrics are most critical for decision-making? Having these ideas in mind will help you create a data warehouse that’s designed to meet your organization’s unique goals.

Consider collaborating with stakeholders across departments to understand their unique data needs as well, so you can make sure your warehouse supports lots of aspects of your company such as marketing, finance, and operations. Establishing this clear roadmap helps you prioritize relevant data and also prevents the inclusion of unnecessary complexities that could slow down warehouse performance.

A data warehouse is only as good as the data it holds. To choose the right data warehouse, you’ll want to focus on storing high-quality data by adding processes for cleansing and standardizing incoming data flows. You might use automated tools to identify duplicates, fill in missing values, and validate data accuracy.

In addition, you’ll want to set up data governance policies to define how data is stored, accessed, and shared. This includes creating user roles, managing permissions, and implementing security protocols to protect sensitive information at all levels. On top of your governance strategies, regular audits and compliance checks can help you check that you’re adhering to industry standards and regulatory requirements throughout the years. By focusing on collecting great data and protecting it, you’ll be able to rely on the insights you gather from the data and build trust with your customers.

If you build a data warehouse now, it might not meet your needs in the years to come — especially if you expand. As your organization grows, so will your data needs. That’s why it’s important to design a data warehouse that can scale effortlessly to accommodate larger volumes of data and more complex queries.

Planning for scalability from the start helps you create efficient warehouses and prevents costly disruptions and reconfigurations down the line — allowing your business to easily adapt to constantly evolving data needs.

A data warehouse is a powerful way to organize your structured data. With your data in one centralized location, teams all across your company can run reports and glean valuable business insights.

However, if you are looking to unify and manage structured and unstructured data or fuel AI and AI autonomous agents, take a look at product.data—a hyperscale engine native to the Salesforce platform. product.data can ingest data from data lakes and warehouses, normalize and harmonize it, and form the foundation for agentic AI–the latest and most promising innovation in AI.