Clearing Up the Data Hub vs Data Lake vs Data Warehouse Confusion

First published: March 9, 2020

Last updated: November 14, 2025

Key takeaways

• Data hubs, data lakes, and data warehouses serve distinct purposes: Data hubs enable operational processes and proactive governance, while data lakes and data warehouses primarily support analytics and reporting.
• Data governance approaches differ significantly: Data hubs proactively enforce governance rules across enterprise data flows, whereas data warehouses apply governance reactively and data lakes offer minimal governance controls.
• Maximum business value comes from using all three together: Combining data hubs with data lakes and warehouses creates a comprehensive architecture that supports operational excellence, analytics, and AI/ML initiatives.

Read the full blog below to learn more.

Data hub vs data lake vs data warehouse explained

Data hubs are getting more attention as enterprises explore solutions to handle their core critical enterprise data. However, this technology is still sometimes seen as an interchangeable alternative to data warehouses or data lakes.

With significant investments at stake, understanding the data hub vs data lake vs data warehouse debate – and the distinct purposes of each data architecture is essential. Your decision must align with your organization’s operational and analytical needs.

Additionally, according to 2020 Gartner research, 57% of data management leaders were investing in data warehouses, 46% were using data hubs, and 39% were applying data lake concepts. Interestingly, this group of executives doesn’t necessarily understand the difference between the three.

To clear up confusion around these concepts, here are the definitions and purposes of each.

What is a data hub?

The data hub is the go-to place for the core data within an enterprise and represents the future of modern 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 data governance initiatives.

Data hubs provide master data to enterprise applications and processes, while connecting business applications to analytics structures such as data warehouses and data lakes. They serve as the operational backbone for reliable, governed data across the enterprise.

Key differences between data hubs:

• and data warehouses: Data hubs distribute live, governed master data to operational systems, while data warehouses store historical data primarily for reporting and analysis.
• and data lakes: Data hubs curate and govern critical core data with high quality standards, while data lakes store all raw data without curation.

The trade-off: Data hubs prioritize data quality and governance for mission-critical data, but don’t attempt to store all enterprise data like lakes do.

What is a data lake?

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 generally a good foundation for data preparation, reporting, visualization, advanced analytics, data science, and machine learning.

Key differences between data lakes:

• and data warehouses: Data lakes store raw, unprocessed data in any format, while data warehouses contain only structured, processed data ready for analysis
• and data hubs: Data lakes hold all enterprise data without curation, while data hubs focus on governing and distributing trusted, core master data
• The trade-off: Data lakes offer maximum flexibility but require users to do more work to extract value.

What is a data warehouse?

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 (BI). Data warehouses implement predefined and repeatable analytics patterns distributed to many users in the enterprise.

Key differences between data warehouses:

• and data lakes: Data warehouses store only structured, processed data optimized for queries, while data lakes store raw data in any format that must be processed by users.
• and data hubs: Data warehouses focus on historical data for analytics and reporting, while data hubs manage live master data for operational use across applications.
• The trade-off: Data warehouses provide fast, reliable analytics on clean data, but require upfront effort to structure and model the data before storage.

In short, 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:

Using data hubs, data lakes, and data warehouses collectively

Data hubs, data warehouses, and data lakes each have a different primary purpose but can add more value to a business when used together. It shouldn’t be a case of selecting one over the other.

The data lake vs data warehouse decision often dominates architecture discussions, but the reality is more nuanced. Whereas data warehouses and data lakes exist primarily to support analytics and machine learning, data hubs enable data integration, sharing, and governance.

Accordingly, businesses are increasingly applying this architecture as a focal point of mediation and governance. Using the three architectures in conjunction effectively supports increasingly complex, varied, and distributed workloads while reducing costs and accelerating decision-making across the enterprise.

Therefore, data management leaders should consider one of the following options:

•       A combination of a data hub and data lake.
•       A combination of a data hub and data warehouse.
•       A combination of all three.

Choosing the between data hubs, data lakes and data warehouses: key considerations

You can use the table below to help pinpoint the ideal data architecture approach for your business.

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Frequently asked questions (FAQs) about the data hub vs data lake vs data warehouse debate

1. What is the main difference between a data hub and a data warehouse in short?

A data hub is designed for operational processes with real-time, bi-directional data sharing and proactive governance, while a data warehouse is optimized for analytics and reporting with structured historical data for business intelligence.

2. Can a data lake replace a data warehouse?

No, the data lake vs data warehouse debate misses the point – they serve complementary purposes. Data lakes store raw, unstructured data for exploratory analytics and machine learning, while data warehouses provide structured, high-quality data for consistent reporting and business intelligence.

3. Do I need both a data hub and a data lake?

Yes, for comprehensive data strategies. When evaluating data hub vs data lake options, understand that data hubs manage critical operational data with high quality and governance, while data lakes store vast amounts of raw data for advanced analytics – together they deliver both operational reliability and analytical flexibility.

4. What is a data hub used for?

As highlighted, a data hub serves as the central operational backbone for enterprise data, providing trusted master data to business applications in real-time, enforcing proactive governance, and connecting operational systems to analytical environments like data warehouses and data lakes.

5. Which should I implement first: a data hub, data lake, or data warehouse?

The data hub vs data lake vs data warehouse decision depends on your priorities. Start with a data hub for operational data consistency challenges, a data warehouse for reporting and business intelligence needs, or a data lake for advanced analytics and AI/ML initiatives – though most successful strategies eventually incorporate all three.