What is a data hub? What is a data lake? What is a data warehouse?

Many organizations are looking for options to store and handle their critical enterprise data, with data hubs being the most commonly researched solution. According to Gartner, from 2018 to 2019, “client inquiries referring to data hubs increased by 20%.” However, based on organizations’ actual data storage and analysis requirements, they might actually need a data lake or data warehouse instead.

Technical terminology can be confusing, especially when similar words and phrases are used to identify related but different concepts. Many organizations confuse the terms data hub, data lake, and data warehouse, which have different applications.

A data hub is an organization’s main location for its core data. The hub centralizes the data that is essential for various applications, and enables different parts of the organization to seamlessly share the data. The data hub is the main source of trusted data for data governance, as it holds the “master data” used in all organizational processes and applications. It also connects the organization’s business applications to data lakes and data warehouses.

A data lake is a singular storage location for all structured and unstructured organizational data. It is the foundation for preparing, reporting, and visualizing the data, as well as the source for advanced analytics, machine learning, and data science. Data is stored in its native state and made available for analysis by anyone in the organization. The unstructured data in the data lake is not refined and there is limited quality assurance, so anyone who plans to use the data must manually process and discover value in the data.

A data warehouse contains integrated and structured data from several different sources. It is the key element in business intelligence, and is primarily used for data analysis and reporting. A data warehouse uses set, repeatable analytics patterns that are distributed to users across the organization. Its configuration is fixed and less agile than a data lake.

Data hubs, lakes, and warehouses are complementary but do not replace each other. Data warehouses and data lakes serve to collect the data for the purpose of analysis; which one you choose depends on the desired shape of the data, amount of data governance required, and the organization’s operational processes. Data hubs function as data sharing and mediation points, and support the application of data governance. While all three technologies can support data-driven activities, you should determine where your core needs lie before investing in a solution.

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