Data Hubs are getting more attention as many enterprises are looking at the different solutions in the market to build their own, in order to handle their core critical enterprise data. However, this technology is still sometimes seen as an interchangeable alternative to Data Warehouses or Data Lakes.

[Learn more about the difference between a Data Hub, a Data Lake and a Data Warehouse in French.]

According to Gartner, “client inquiries referring to data hubs increased by 20% from 2018 through 2019.” Interestingly, the analyst firm noticed that “more than 25% of these inquiries were actually about data lake concepts(1).”

Have you ever been in a situation where you wonder whether you need to implement a data warehouse, a data lake or a data hub? Probably. There is still a lot of confusion when it comes to differentiating these three concepts as they sound similar. In reality, they have important differences that everyone should be aware of. To clear up confusion around these concepts, here are some definitions and purposes of each:

The Data Warehouse is a central repository of integrated and structured data from two or more disparate sources. This system is mainly used for reporting and data analysis, and is considered a core component of business intelligence. Data warehouses implement predefined and repeatable analytics patterns distributed to a large number of users in the enterprise.

The Data Lake is a single store of all structured and unstructured enterprise data. It hosts unrefined data with limited quality assurance and requires the consumer to process and manually add value to the data. Data Lakes are, in general, a good foundation for data preparation, reporting, visualization, advanced analytics, data science and machine learning.

The Data Hub is the go-to place for the core data within an enterprise, and according to a recent Semarchy article, the future of data management. It centralizes the enterprise’s data that is critical across applications, and it enables seamless data sharing between diverse endpoints, while being the main source of trusted data for the data governance initiative. Data hubs provide master data to enterprise applications and processes. They are also used to connect business applications to analytics structures such as data warehouses and data lakes.

In short, data warehouses and data lakes are endpoints for data collection that exist to support the analytics of an enterprise, while data hubs serve as points of mediation and data sharing. They are not focused solely on analytical uses of data.

In some cases, data warehouses and data lakes offer governance controls, but only in a reactive manner, whereas data hubs proactively apply governance to the data flowing across the infrastructure.

Data warehouses, data lakes, and data hubs are not interchangeable alternatives. Nevertheless, they are complementary, and together they can support data-driven initiatives and digital transformation. The table below summarizes their similarities and differences:

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(1) Gartner Article ID G00465401: Data Hubs, Data Lakes and Data Warehouses: How They Are Different and Why They Are Better Together. Published 13 February 2020 – By Analysts Ted Friedman and Nick Heudecker — Requires a Gartner account.