What’s the difference between a data hub, a data warehouse and a data lake?

Posted by Trevor Legg on 12 May 2021

Data hubs, data warehouses, and data lakes are significant investment areas for data and analytics leaders and are vital to support increasingly complex, distributed, and varied data workloads.

Gartner finds that 57% of data and analytics leaders are investing in data warehouses, 46% are using data hubs, and 39% are using data lakes. However, they also found that these same data and analytics leaders don't necessarily understand the difference between the three...

To best support specific business requirements, it's vital to understand the difference and purpose of each type of structure, and the role it can play in modern data management infrastructure.

What is a data hub?

The critical difference between a data hub and a lake or warehouse is that a data hub cannot store detailed data for extended periods. Hubs are characterised by their ability to allow the seamless flow and governance of data between centrally managed and locally managed sources. Data hubs are also not storehouses in which analytic assignments are generally executed.

(You might also want to read "The Why's and How's of Automating your Data Governance")

Data hubs allow data sharing and governance controls to be applied to data flowing across various applications and processes. They enable data flow within a business by connecting producing systems and processes with consuming systems and processes. Data and analytics leaders can use data hubs to improve the delivery of data from business applications to a data warehouse or data lake for more long-term storage.

 

What is a data warehouse?

Data warehouses are used for long-term data storage, more of an endpoint than a point in which data passes through. Data warehouses provide support for the analytic needs of a business and store well-known and structured data.

Data warehouses support repeatable and predefined analytical needs that can be scaled across several users in a business. They are also more suited to complex queries, high levels of simultaneous access, and demanding performance requirements.

(We also recommend checking out "Do Tableau and Power BI replace the need for a Data Warehouse?")

 

What is a data lake?

A data lake is a central repository that holds a vast pool of raw data — ranging from unstructured to structured — from many sources. Raw data is data that hasn’t yet been processed for a specific purpose. Data in a data lake isn’t defined, and so it can be queried at will and used for a variety of different purposes. Because of its size and scope, a data lake is more difficult to maintain requiring governance processes and maintenance if you want your data lake to meet both your current and future needs.

 

How can data hubs, data lakes and data warehouses be used together?

It's essential to understand the differences and value to a business when using a data hub, data warehouse or a data lake, individually or together. It shouldn't be a case of choosing one over the other for data and analytics leaders. Instead, a combination of a data hub and data warehouse or data lake should be considered to meet a business's current and anticipated requirements.

To modernise data management infrastructure, the intent should be to become dynamic with the ability to evolve over time by enabling new connections and supporting diverse use cases. Businesses are increasingly applying a data hub architecture as a focal point for sharing and managing all their critical data across the company.

Understanding what type of analyses will be used for particular use cases is critical in using the system to its full potential. For example, using a combination of a data hub and data warehouse a business can reap the following benefits:

• Data quality and consistency: data comes from one place, providing a single source of truth which makes for faster decision-making.
• Enhanced business intelligence: by integrating data from multiple sources, leaders can get a complete view of their business from marketing conversions to product usage.
• Quality data for better decision-making: getting the right data to the right people at the right time ensures quality decisions can be made based on up to date, accurate and timely information.
• Easily identify areas for improvement: Access to both vertical and horizontal views of the organisation using cross departmental data means the interaction of business process within and across departments can be managed in greater detail.

With improved data analysis capabilities, businesses can refine their offering. And while analysing data is not a silver bullet to business success, it can become a competitive advantage by bringing insights to the surface and produce better business decisions that lead to company success.

About the Author:

Trevor Legg Trevor is a 20+ year seasoned professional who is passionate about delivering solutions that are based on analytics, data management, Business Intelligence, strategic planning, and coaching for individuals or organizations looking to optimize their performance. View my social profiles: LinkedIn | Twitter