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

eggs in tray on white surface data hub vs data lake

By Scott Moore, Director of Presales, Semarchy

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, 57% of data management leaders are investing in data warehouses, 46% are using data hubs, and 39% are applying data lake concepts.

Interestingly, the analyst firm noticed that this cohort of executives doesn’t necessarily understand the difference between the three. 

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.

Data hub vs data lake vs data warehouse explained

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To clear up confusion around these concepts, here are some definitions and purposes of each:

The 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. Data warehouses implement predefined and repeatable analytics patterns distributed to a large number of users in the enterprise.

The 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.

The Data Hub

The data hub is the go-to place for the core data within an enterprise, and 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.

Data Hub DW DL data hub vs data lake

They all look similar but they are different

In short, data warehouses and data lakes are endpoints for data collection that exist to support an enterprise’s analytics. In contrast, 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:

Data HubData WarehouseData Lake
Primary UsageOperational ProcessesAnalytics and reportingAnalytics, reporting and Machine Learning
Data ShapeStructuredStructuredStructured & Unstructured
Data GovernanceMain pillar for all data governance enforcement rulesAfter-the fact governance as it consumes existing operational data“Use at your own risk” data approach. Lightly governed.
Data QualityVery high qualityHigh qualityMedium / low quality
Integration with Enterprise AppsBi-directional real-time integration with existing business processes via APIs.Mono-directional ETL or ELT in batch mode. Transformed and cleansed data is refreshed at low frequency (hourly, daily or weekly)Mono-directional ETL or ELT in batch mode. Data is dumped without control into the lake assuming future cleansing by the consumer.
Business Users InteractionsCan be the primary source of authoring of key data elements such as master data and reference data. Exposes user-friendly interfaces for data authoring, data stewardship and search.Offers a read-only access to aggregated and reconciled data through reports, analytic dashboards or ad-hoc queries.Requires data cleansing / preparation before consumption. Access to business users is mainly offered via reports, dashboards or ad-hoc queries. Used to stage Machine Learning data sets.
Enterprise Operational ProcessesPrimary repository for reliable data exposed in business processes. 
Can be the primary conductor of enterprise business processes.
Mainly serves analytics processes.Mainly serves Machine Learning processes.

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.

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. However, using the three architectures in conjunction can effectively support increasingly complex, varied, and distributed workloads. 

Therefore, data management leaders should consider a combination of a data hub and data warehouse or data lake to meet their company’s current and projected requirements. The intent should be to develop an evolving dynamic capability to support a more diverse set of data and various analytics use cases.

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