How is a Data Hub different from a Data Lake?

Data hubs, data warehouses, and data lakes are significant investment areas for data and analytics leaders. And they are vital for supporting increasingly complex, distributed, and varied data processes.

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

In this short video, we explain the difference between a data warehouse and a data lake.

In summary: A data warehouse (DWH) is a centralized data repository for structured, filtered and for a specific purpose, processed data. It is primarily used for data analysis and reporting, particularly of historical data. It could be considered a cornerstone of business intelligence.

A data lake (DL) is also a single or centralized data repository, but it is used for storing vast amounts of un-processed data in its natural or raw format. How the data will eventually be used can be decided at a later date.

It was the rise of big data and the Internet-of-the-Things, that led to the evolution of concepts around data lakes. More and more utilities started initiatives to create value from their data and defined a data lake as a key requirement in their future IT architecture and digitalization strategies.

As a side note, a poorly managed data lake that is not creating value or is not accessible for the intended users / use cases might be referred to as a data swamp.

When comparing a data warehouse and data lake, we’re often asked about data hubs.

So what is a data hub and how does it differ from a data lake?

The main difference is its intended purpose.

A data hub is mainly designed to exchange or share data. It will typically store semistructured and harmonized data and make pre-processed and curated data available in various formats to simplify data exchange and sharing.

A data hub architecture operates as a hub with spokes and as a result, acts as a "source system" to serve other applications or services with quality managed data. Business and data and analytics leaders can use data hubs to improve the delivery of data from utility applications to a data warehouse or data lake for more long-term storage.

A data lake, as previously described, is designed as a single repository for processing raw data utilized by data scientists for advanced analytics, visualizations, AI use cases and machine learning to create value.

In summary, a data hub is about sharing and exchanging curated and managed data between systems, services, or parties. A data lake is about creating a vast pool of data in many different formats which can feed analytics, AI or data science services to create value.

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