What is a Data Warehouse?

A data warehouse is a centralized place to store data from different systems in your business. It is the backbone of modern data-driven strategies, transforming raw data from various sources into a goldmine of actionable information.

How can a data warehouse be used for analytics?

Data analytics brings great benefits to business leaders, such as visibility into operations and confident, objective decision-making. Getting data organized to use for reporting and analysis is like piecing together a giant data puzzle, and that's where a data warehouse comes into play. Picture this: a central hub where every bit of your company's data is neatly organized, ready to unlock powerful business insights.

Let's dive into the details of data warehouse concepts, stripping away the tech jargon, and discover how data warehouse analytics can be the centerpiece of your approach to data.

Understanding Data Warehouses

Data Warehouse vs Database

The analogy of a warehouse is a good one, but limited. Like a real-world warehouse, a data warehouse is a place to store data. A data warehouse is more than a database, although the underlying concepts are the same. For someone querying a database or data warehouse, the experience is exactly the same–connect to it, run a query, and see the results. The underlying infrastructure is specialized for different types of queries. Data warehouses are designed for analytical queries.

Unpacking the Term: What is a Data Warehouse?

The concept of a data warehouse is simple: get all your data into one place, organize it, and analyze it to give you a complete picture of your business.

In implementation, data analytics is composed of three things:

A data warehouse, a central place to store data for analysis
A method to extract data from operational systems and deposit it in the warehouse
A reporting tool to display data visualizations and deliver information to the people who need it

Differences from Application Databases

Application databases are designed to support applications, optimized for things like looking up a lot of details for a specific customer, product, etc. Data analytics, however, often involves queries that process a lot of data at once. Here is an example of typical queries from each:

Application Database Example: “What is the shipping address for customer XYZ?” In this query, the amount of data queried is small–one customer, one address.
Data Warehouse Example: “What are the numbers of shipping addresses in each region across all my customers?” In this query, the amount of data is much larger–every customer, and every address, then grouping them by region.

Although both technologies use the SQL language and store data, they are optimized for different scenarios

This distinction is critical because many organizations getting started with data analytics services will attempt to use their application database to perform reporting and analysis queries.

While application databases handle daily transactions, a data warehouse provides a panoramic view of your data, past and present, to inform strategic decisions.

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What is ETL in data warehouse?

Data warehouses can (and should) do more than store data. They are powerful tools for combining and modeling your data.

Combining data from multiple sources using ETL gives business leaders the 360-degree visibility we all want into our businesses. For example, the CRM may have information about customers and their businesses, the help desk system has information on how much effort is spent supporting customers, and the accounting system has information on billing. By combining these three data sources together into a single data model, you can answer questions about what affects customer profitability.

Data modeling is how the data is structured. A good outline helps someone who is reading a blog post to access and understand information quickly. Similarly, a good data model provides structure that helps people understand and use the data better, and it helps the machines process the queries faster.

Comparisons for Clarity: Data Warehouse vs Data Lake vs Data Mart

Many technical terms get thrown about when discussing data analytics and data analytics platforms. This can be confusing, especially when the terms are not used consistently. Here are some definitions you can use to increase clarity:

Data Warehouse - a database designed for data analysis
Data Lake - a less organized place to store data. Easier to get data in, but harder to do analysis. Can be used in conjunction with a Data Warehouse, but is not necessary. For example, Datateer’s platform includes a Data Lake to ease gathering data, and a process to move data from the Data Lake into the Data Warehouse.
Data Mart - a section of the Data Warehouse for a specific subject area, use case, or target audience. A Data Mart is simply a pattern and is not necessary. Data Marts become helpful for organizations when a Data Warehouse gets large. For example, if you have lots of datasets in the warehouse, having a section of only sales data can help people focus on sales questions without getting lost trying to navigate all the other datasets.

Learn more in our article Data Lake vs Data Warehouse vs Data Mart.

What is the primary purpose of a data warehouse? Benefits and Applications

So, why all the fuss about data warehouses? The answer lies in the benefits they bring. Data warehouse analytics combines data from across your business into a 360-degree view of the business, customers, and operations.

Data Warehouses Support Artificial Intelligence

Any artificial intelligence or predictive analytics efforts rely completely on the availability, readiness, cleanliness, and organization of data. In the end, no amount of technology, no matter how fantastic it appears, is valuable without clean, usable data.

[AI] model behavior is not determined by architecture, hyperparameters, or optimizer choices. It’s determined by your dataset, nothing else. - James Betker, Research Engineer at OpenAI

Data Monetization and Building Data Products

Data warehouses provide a place to gather and organize data for monetization purposes.

Building a data product: Data analytics products like customer-facing dashboards are based on data warehouses. These data products enable you to deliver analytics and insights to your customers. This increases retention, creates a differentiated customer experience, and increases sales.
Sharing datasets with customers via embedded analytics: Data warehouses provide a convenient way to share data sets with customers, deepening your integration and relationship with them, and providing a potential revenue stream.

Other Business Applications of Data Warehouse Analytics

The applications are as varied as the industries themselves. Retail companies can track inventory and customer preferences to tailor their marketing strategies. Healthcare organizations use data warehouses to improve patient care and manage operational costs. Even small businesses can leverage analytics data warehouse solutions to compete with larger rivals by making smarter, data-driven decisions.

What is the ultimate outcome of a data warehouse?

A common desire by business leaders is to analyze data by subject area, for example sales, marketing, customer service, or inventory. Many operational tools and products provide out-of-the-box reporting, but they don’t allow combining data from other tools or products. This limits reporting capabilities, because we don’t think in terms of tools, we think about our business by subject area. By organizing data all in one place, a data warehouse overcomes this limitation.

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Choosing a Data Warehouse

The landscape of data warehouse and data management solutions for analytics is varied, each with its unique features and benefits. Whether it's the scalability of a cloud data warehouse, the robustness of an enterprise data warehouse, or the modern architecture of solutions like the Snowflake data warehouse or Databricks, the key is to match the solution with your business needs and goals.

But how do you choose? It starts with understanding your business's data requirements and future goals. Are you leaning towards a data warehouse predictive analytics approach to forecast trends and behaviors? Or are you more focused on historical data analysis for informed decision-making? Perhaps the integration capabilities and analytics tools of a Snowflake data warehouse align with your vision of democratizing data across departments.

Remember, implementing a data warehouse is not just a technical decision; it's a strategic one. It involves considering your data's scale, diversity, and the speed at which you need to access insights. It's also about forecasting future needs – will your data warehouse architecture stand the test of time and scale with your business?

But don’t get caught up in analysis paralysis. Modern cloud data warehouses are evolving and constantly improving, even learning from each other and implementing competitor’s features. Any leading cloud data warehouse will provide a baseline of capabilities that will be sufficient for a growing business for years to come.

Which enterprise data warehouse to choose? Snowflake vs BigQuery vs Databricks vs ClickHouse

Feature	Snowflake	Databricks	BigQuery	ClickHouse
System Type	Cloud-native data warehouse	Unified analytics platform	Fully-managed data warehouse	Column-oriented DBMS
Core Strengths	Data sharing capabilitiesScalability and performanceRobust security features	Advanced analytics and MLCollaborative workspaceOptimized for Apache Spark	High-speed analyticsServerless, fully managedIntegration with Google Cloud	High performance on large datasetsReal-time query processingCost-effective
Ease of Use	User-friendly interfaceRequires technical management	Requires technical expertiseCollaborative for data teams	Easy to use in Google Cloud ecosystemAutomated management features	Efficient data storage and retrievalSteeper learning curve
Scalability	Highly scalable, separate compute and storage layers	Scalable compute options, integrates with cloud resources	Auto-scaling, high throughput	Highly scalable, efficient with large datasets
Cost	Pay-per-use can get expensiveCost-effective at scale	Can become costly with extensive usagePay-as-you-go pricing model	Potentially unpredictable costsPay-per-use pricing model	Generally more cost-effectiveOpen-source reduces software costs
Integration & Ecosystem	Good cloud platform integrationExtensive third-party tool support	Deep integration with Apache Spark and other data science toolsLess data warehouse focused	Strong integration within Google Cloud servicesLimited outside Google ecosystem	Open-source with community pluginsLess integrated with cloud services
Security & Compliance	Comprehensive security featuresRobust compliance certifications	Built-in security featuresCompliance depends on cloud provider	Google's security infrastructureCompliance features	Basic security featuresCommunity-driven enhancements
Special Features	Automatic scaling, data cloning, time travel	Machine Learning, real-time analytics, Delta Lake integration	Smart caching, machine learning, geo-analysis	ClickHouse's own SQL dialect, materialized views, and compression

Conclusion

A data warehouse is more than just a storage solution; it's the cornerstone of modern data management and analytics strategies. By centralizing, organizing, and analyzing your data, you can uncover invaluable insights that drive smarter business decisions, foster innovation, and maintain a competitive edge in today's fast-paced market. If you’re looking for help on where to get started with your data analytics consider exploring our managed analytics services.

As you move forward, consider not just the technology but the transformative potential it holds for your business. The journey to data-driven excellence may seem daunting, but with the right approach and solutions, your data warehouse can become the engine of your company's success.