Data warehouse, data lake, or data lakehouse?

A comprehensive guide to your modern data analytics solution.

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What is the difference?

Let's explore the differences between data warehouses, data lakes, and data lakehouses, and see how they can be used to best meet the needs of your organization.

Data warehouses, data lakes, and data lakehouse are all used to store and manage large amounts of data, but they are very different tools with different purposes.

A data warehouse is used to store structured data from multiple sources and can be used to analyze and report on data. A data lake, on the other hand, is used to store large amounts of both structured and unstructured data, making it more suitable for data mining, machine learning, and other analytics-related tasks. While a data lakehouse is a hybrid approach that combines the distributed data storage of a data lake with the unified interface of a data warehouse, making it easy to retrieve and analyze the open format data.


Data warehouse, data lake, or data lakehouse?

No matter which modern analytics solution your organization chooses to use for your valuable data, smart data automation can help with the process.

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Data warehouse

Data warehouses are specifically designed for analyzing data that has been collected, contextualized, and transformed. If your organization requires advanced data analysis or analysis that relies on historical data from multiple sources across your organization, a data warehouse may be your right choice.

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Data lake

Data lakes store a wide variety of unfiltered data that is later used for a specific purpose. If your organization needs cost-effective storage for raw, unstructured data from multiple sources that you plan to use for specific purposes in the future, a data lake may be your right choice.

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Data lakehouse

Data lakehouses use best practices from both data warehouses and data lakes. They can handle both structured and unstructured data, which is stored in open formats allowing different engines to run simultaneously. Data lakehouse is a flexible and scalable solution for you.

A summary

When to use a data warehouse


A data warehouse is usually optimized for time-series data and relational data, and can be integrated with other systems to feed data in and out.


A data warehouse is optimized for analytical reporting, so it is best for storing and managing structured data.


A data warehouse can be either on-premises or in the cloud, has a long-term retention policy, and is designed to be highly scalable.

A summary

When to use a data lake


A data lake is a scalable, bulk storage repository that serves as a catch-all for all data types, formats, and origins.


A data lake is optimized for analytics, so it is best for storing and managing unstructured data.


A data lake is usually deployed in the cloud, and does not have a long-term retention policy.

A summary

When to use a data lakehouse


A data lakehouse is designed for the modern data landscape: the reliability of a data warehouse and the scalability of a data lake.


A data lakehouse allows organizations to make sense of both structured and unstructured data more efficiently.


A data lakehouse can provide better data quality and governance than the data lakes architecture.


Smart data automation for data warehouse, data lake, and data lakehouse

How biGENIUS helps you automate your development tasks no matter which approach you choose.

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Model & meta-data

Smart data automation provides you with standardization and best practice blueprints to help automating the otherwise repetitive and monotonous development tasks that you would have to spend your valuable resources on.

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The tried-and-proven biGENIUS generators help you to further optimize your development cycle. With advanced modifications feasible, unparallel flexibility means you can meet your business needs without compromise.

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Your choice of architecture

Whether your choose data warehouse, data lake, or data lakehouse, biGENIUS will help you save time and costs substantially by providing standardization and drastically reducing testing efforts.

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What our customers are saying

See what industry leaders say about biGENIUS and their experience of working with us.

“Thanks to biGENIUS, we were able to lay the foundation for combining data from different sources into one report or dashboard.”

Evi Verschueren
Business Intelligence Team Lead, Smurfit Kappa

“The efficient DWH generator enabled us to achieve the required transparency with regard to marketplace performance within a very short time, in high quality and in compliance with BI best practices.”

Andreas von Ballmoos
Business Intelligence Lead, Scout24

“The biGENIUS team is knowledgeable about the inner workings of the application - stuff that you can't work out yourself as a customer.”

Tobias Rist
Data & Analytics Architect, Swica

“For us, it's really a central tool that should do a lot in helping people have a standardized approach for the whole firm.”

Sébastien Brennion
Business Intelligence & Analytics Engineer, Valiant Bank

“Since we have been using biGENIUS, we manage the development 100% internally. We did not only save a lot of money but, having built everything ourselves, we gained in efficiency as we are able to adapt any user requests right away.”

Marc Buthey
IT & Project Manager, Tirus International SA

Future-proof your data with biGENIUS-X today.

Accelerate and automate your analytical data workflow with comprehensive features that biGENIUS-X offers.