Data Lakes, Data Warehouses and Data Hubs: What's the Difference?

Understanding data infrastructures and why every modern data-driven company should have the right types.

Data Lakes, Data Warehouses and Data Hubs: What's the Difference?
Lepakshi Poonamallee

You may have heard of the term data warehouse thrown about in a conversation with a colleague working in the IT department. Maybe you heard the term on the television where a renowned data scientist rambled about the monumental significance of Big Data for the future of the economy. It seems simple: Data Warehouses is where you ‘house’ or store your data. Unfortunately for data newbies that are looking to explore the world of Big Data, data storage and management is a lot more complicated than that. There are a wide variety of data management structures that offer varying advantages and have varying capabilities for users of different skills. A few data architectures available to businesses today include: data lakes, data hubs and of course, data warehouses. This article hopes to bring some clarity to readers who seek to understand the differences.  


Let’s start with Data Warehouses. Data Warehouses, or DWs, are where companies store their valuable data. This can range from employee data, sales data and revenue data to customer data. A DW is often considered the single source of data truth for an organization. There are 4 major features of data warehouses: they are subject oriented, integrated, time variant and nonvolatile.  

  • Subject oriented: Data warehouses provide information around a subject. A subject can be a business’s products, their customers, or even their streams of revenue.
  • Integrated: A data warehouse integrates data from multiple data sources and these multiple sources may have different conventions of labeling the same set of data. Therein lies the benefit of DWs: Data warehouses have a single, standardized way of identifying data. Standardization facilitates data analysis and transformation. 
  • Time variant: The data collected in a data warehouse is identified with a particular time period, hence referred to as historical data. In comparison, a transactions system only stores the most recent data. For example, a transactions system would hold the latest address of a customer. To find all the addresses the customer has lived previously, one would need to search through the data warehouse. 
  • Non volatile
  • Previous data that has been entered into a data warehouse is not erased nor altered when new data is added to it.

Businesses use DWs to store structured data. As the name suggests, structured data has a tabular car format with rows and columns. This type of data is easy to work with, apply standard analytical models, and use business intelligence tools on. 

However, a large amount of a business’s data has no structure. Take for example, a company’s phone call transcriptions, text data, social media comments, or even audio and video recordings. Unstructured data is much more difficult to manage and store. Data warehouses CAN handle unstructured data - but they don’t do so in the most efficient manner.


This is where Data Lakes come into play. Data Lakes (DLs), which rose in popularity in the early 2000s, are used to store unstructured data in a cost-effective manner. It should be noted that Data Lakes, like Data Warehouses, can also manage structured data. So how do DLs differ from DWs aside from the additional management of unstructured data?  

Data Lakes preserve all data. For DW’s, data engineers spend a lot of time analyzing data sources and profiling data to produce a highly structured data model that is optimized for data reporting. In this process, data that isn't used to answer specific questions or pertain to a subject is excluded from the warehouse. This helps simplify the data model, conserve space, and reduce disk storage costs. Data Lakes on the other hand, retains all data, even data that the business expects to never use. This presents two advantages: it preserves the business data should new technologies allow them to analyze the data for new insights and it allows the business to go back in time to any time period to do analysis on historical data.

The second major difference is the ability of Data Lakes to adapt to changes faster. DW’s rigid, tabular format is costly and time consuming to alter to accommodate business developments. Since Data Lakes store raw data, users can always explore and expand on the data to answer new business questions. 

Both Data Lakes and Data Warehouses allow their users to store and manage data at a high level. Recognizing this, many businesses have begun to combine these two architectures to maximize their data management capabilities. Others have begun to prefer Data lakes. In Ventana's 2020 Report “Data Warehouses Meet Data Lakes”, researchers found that 73% of organizations are combining their data warehouse and data lakes in some way, while 23% of organizations are completely replacing their DW’s with DL’s. This brings us to the last data architecture of our discussion: Data Hubs 


A data hub is a data-centric storage architecture that leverages a combination of different technologies including Data Warehouse and Data lakes. This management tool focuses on streamlining data sharing.  Data flows into and out of the hub through endpoints and the hub allows businesses to see these data flows in real time. Data hubs allow for huge quantities of data, unstructured and structured, to be processed quickly and standardized. As mentioned earlier, data standardization is important as it allows for the transformation of data to reveal insights. Unsurprisingly, Data Hubs are growing quickly in popularity, and businesses that are looking to invest in a more flexible, comprehensive data system that maximizes data analytics need to consider investing in them. The article A Modern Take on Data Management: The Data Hub will provide readers more information on Data Hubs and how Adaptive Pulse is helping B2B SaaS businesses invest in the Data Hub architecture.

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Lepakshi Poonamallee

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