As organizations continue to generate massive volumes of data from diverse sources, the demand for repositories that can power real-time monitoring, fuel machine learning initiatives, and support SQL-based analytics—all while reducing complexity and cost– is consistently growing.

Many companies turn to storage solutions like Data Lakes and Data Warehouses to address this demand. However, using multiple platforms introduces complexity, requiring professionals to move and copy data between repositories, which can be time-consuming due to the unique characteristics of each system.

Fortunately, a third architecture has emerged to tackle this challenge: the Data Lakehouse. This next-generation architecture merges the best features of Data Lakes and Data Warehouses into a single, unified platform.

In this article, we’ll explore the evolution of these architectures, their key benefits, and how the Lakehouse is reshaping the data landscape for businesses striving to become more data-driven. You’ll finish this article with a deep understanding of the effect of each architecture on your team’s capabilities and know strategically when to use each one.

• Data Warehouse
• Data Lake
• Data Lakehouse
• Has the Data Lakehouse Replaced Data Lakes and Data Warehouses?
• About the Authors

I. Data Warehouse

The Data Warehouse was the first major step in helping businesses of all sizes make sense of their information. It consolidates structured data from transactional databases (OLTP) into a centralized analytical system (OLAP) optimized for reporting and decision-making.

Core Benefits of a Data Warehouse:

• Enables analysis of historical data
• Centralizes structured data from multiple systems
• Ensures high data quality and consistency

Data Warehouses are ideal for organizations that rely heavily on Business Intelligence (BI) dashboards and reports. They work well for predefined queries but are limited in their ability to process unstructured or real-time data. A Data Warehouse is typically used by companies that require an aggregated and consolidated view of their business. It primarily stores structured data with a predefined schema and is optimized for frequent access to aggregated and summarized data, commonly used by Business Intelligence (BI) analysts.

II. Data Lake

As data types grew more diverse with the rise of Big Data, traditional Data Warehouses struggled to accommodate unstructured and semi-structured data. The Data Lake brought a low-cost, high-capacity storage architecture built to ingest massive volumes of structured, semi-structured, and unstructured data.

The advent of a Data Lake-based architecture, known as the Modern Data Warehouse, introduced the advantage of using a Data Lake as a staging layer for structured data before processing and loading it into a Data Warehouse.

Core Benefits of a Data Lake:

• Compatibility with any data format
• Data availability at any time
• Allows concurrent access by many users
• Raw data delivery, enabling analysis across various business platforms
• High organizational capabilities
• Large data storage capacity

The Data Lake extends the capabilities of a Data Warehouse. In this model, the schema is applied upon reading the data. It is typically used in scenarios involving exponential data growth, diverse consumers, multiple access methods, and predictive analytics based
on detailed, raw, and processed data.

III. Data Lakehouse

The concept of centralizing structured, semi-structured, and unstructured data in a single repository emerged to address the core limitations of previous architectures.

This was made possible by the advancement of technologies that introduced transactional control to Data Lakes, such as Delta Lake– a new technology that enhances the reliability of data stored in a Data Lake. It features ACID transactions– previously exclusive to Data Warehouses– along with unified batch and streaming data processing and scalable metadata management.

Core Benefits of a Data Lakehouse:

• Data democratization – Provides a unified platform where technical and non-technical users can easily access and analyze data.
• Cost efficiency – Reduces the need for multiple, siloed storage solutions by consolidating infrastructure.
• Centralized architecture – Unifies structured, semi-structured, and unstructured data in one place, eliminating system duplication.
• Cross-functional usability – Supports a broad range of user profiles, from BI analysts running SQL queries to data scientists training machine learning models.
• Built-in governance – Enhances control and compliance by minimizing table redundancy and enabling consistent data definitions across environments.

A practical use case of a Data Lakehouse is storing user information within a company (e.g., video-based access control). Since these involve personal data, governance controls are necessary. A Data Lakehouse can automate compliance processes, ensuring data anonymization when required.

Has the Data Lakehouse Replaced Data Lakes and Data Warehouses?

Not necessarily.

Data Warehouses and Data Lakes still have their place. While the Data Lakehouse introduces a powerful, unified approach, it hasn’t rendered the previous architectures obsolete.

Choosing the right architecture depends on several factors: data volume, structure, access patterns, and governance requirements.

The Lakehouse stands out by combining the flexibility, scalability, and low-cost storage of Data Lakes with the data reliability, transactional integrity, and governance traditionally found in Data Warehouses. It’s a promising architecture for modern data needs, but to fully realize its value, organizations must invest in data literacy, cross-functional collaboration, and a strong data-driven culture.

Want to know which architecture is best for your business?

Schedule a consultation with us and let our data experts help you design a solution that fits your goals– today and in the future.

About the Authors

Alberto Mariano is a Data Architect with over a decade of experience in IT, specializing in .NET, SharePoint, and data platforms. He holds certifications in SharePoint and Data, and regularly creates educational content and training sessions. Outside of work, he enjoys cycling and playing guitar.

Benedito Póvoa is a Data Engineer at Programmers with a Systems Analysis and Development degree. He began his career in BI, working with SQL, DAX, and M, and later transitioned into Data Engineering. Curious by nature, he enjoys exploring new tools and technologies. In his free time, he’s into music, anime, and the occasional outdoor adventure.