As discussed in my blog and book “Deciphering Data Architectures: Choosing Between a Modern Data Warehouse, Data Fabric, Data Lakehouse, and Data Mesh” (Amazon), organizations are often challenged with choosing the right data architecture to meet their business goals—especially as AI and data-driven decision-making take center stage. To help clarify, here’s a quick review of the four core architectures, followed by guidance on when to use each. Each architecture includes five stages of data movement – ingest, store, transform, model, and visualize (described here).

Modern Data Warehouse (MDW)
A Modern Data Warehouse architecture combines a data lake for storing raw, unstructured, and semi-structured data, with a relational data warehouse for serving structured and curated data to business users. This hybrid architecture offers the best of both worlds: the flexibility and scalability of a data lake with the governance, performance, and usability of a traditional data warehouse​. (more info). How the MDW architecture looks like at a high level:

Data Fabric
Data Fabric is an evolved form of the Modern Data Warehouse, enriched with more technologies to support real-time processing, metadata catalogs, data virtualization, APIs, and governance tools. It creates a unified architecture that allows users to seamlessly access and manage distributed data across various platforms and formats, enhancing scalability, automation, and security​. (more info). In purple are the data fabric features:

Data Lakehouse
A Data Lakehouse attempts to merge the capabilities of data lakes and relational data warehouses into one platform, typically by using a transactional storage layer like Delta Lake, Apache Iceberg, or Apache Hudi on top of a data lake. It allows both raw data storage and structured querying in a single repository, enabling cost-efficient analytics and simplified architecture without a separate relational data warehouse​. (more info). This is how Microsoft Fabric operates – with data from lakehouses and warehouses all stored in a delta lake called OneLake – there is no relational storage. See Microsoft Fabric reference architecture. In the diagram below Delta Lake is added and the RDW is removed. Note that the model step is still included as you want something like a semantic model as the interface to the data instead of a list of folders and files.

Data Mesh
Unlike the Modern Data Warehouse, Data Fabric, and Data Lakehouse—which all rely on a centralized architecture where operational data is ingested into a central system—Data Mesh decentralizes data ownership, allowing each domain to retain its own operational data and take responsibility for building and managing its own analytical data. Rather than depending on a central IT team, domains treat data as a product and create their own analytics pipelines. Importantly, Data Mesh is a conceptual framework, not a technology, and each domain can choose to implement its analytical architecture using a Modern Data Warehouse, Data Fabric, or Data Lakehouse—whichever best fits their specific needs​. (more info)

This diagram shows on the left picture the centralized approaches used for the first three architectures, where operational data is copied to a central location owned by IT, who then creates the analytical data. In a data mesh, data is kept within several domains within a company, such as manufacturing, sales, and suppliers. Each domain has its own mini-IT team that takes its operational data, cleans it, and makes it available as analytical data that it owns, using its own compute and storage infrastructure.  This results in a decentralized architecture where data, people, and infrastructure are scaled out – the more domains you have, the more people and infrastructure you get. 

Here is a very high-level use case for each architecture (in ascending cost and complexity):

Modern Data Warehouse
Ideal for organizations handling relatively small volumes of data (typically <1TB), particularly those already familiar with relational data warehouses (RDWs). If your dataset is very small, you may even skip implementing a data lake. This architecture excels at structured reporting and business intelligence scenarios, offering well-established design patterns and a low barrier to adoption. However, its scalability for AI and real-time use cases is limited.

• ✅ Best for: Traditional BI, reporting, and small-scale analytics
• ✅ Advantages: Easier adoption, well-known patterns, minimal learning curve
• ⚠️ Considerations: Limited scalability for AI; less suitable for large, diverse datasets

Data Fabric
Suited for companies that must integrate and analyze a wide variety of data sources differing in size, speed, and format. It’s also appropriate when modernizing a legacy environment where a full rewrite (e.g., of many stored procedures) would be cost-prohibitive. Data Fabric supports real-time access, federated queries, and AI-driven use cases via a semantic layer. However, it demands strong data governance and integration discipline.

• ✅ Best for: Real-time integration, federated access, and complex data landscapes
• ✅ Advantages: Unified access layer, real-time support, AI-ready semantic modeling
• ⚠️ Considerations: Complex to implement; requires robust governance framework

Data Lakehouse
Best used as a flexible and cost-efficient solution for combining raw data storage and structured analytics in a single platform. It supports both AI and traditional BI, with transactional layers like Delta Lake, Apache Iceberg, or Apache Hudi providing additional functionality. A good rule of thumb: “Use it until you can’t.” When performance or governance needs outgrow the Lakehouse, offload specific datasets to an RDW as needed.

• ✅ Best for: Unified analytics platforms, scalable AI workloads, and mixed data types
• ✅ Advantages: Strong AI support, balance of structure and flexibility, lower cost
• ⚠️ Considerations: Moderate organizational change; governance needs to be layered in

Data Mesh
Designed for very large, domain-oriented enterprises experiencing major pain points with scalability and central IT bottlenecks. Data Mesh decentralizes control, giving each domain responsibility for managing its own data pipelines and analytics. Each domain can implement its analytical architecture using a Modern Data Warehouse, Data Fabric, or Data Lakehouse—whichever suits their needs. This approach fosters cross-domain AI scalability but requires a high degree of organizational maturity and a cultural shift toward treating data as a product.

• ✅ Best for: Large enterprises with mature data practices and strong domain ownership
• ✅ Advantages: Scales AI across domains, reduces bottlenecks, promotes autonomy
• ⚠️ Considerations: Long implementation timelines, requires cultural and process change

Most companies won’t adopt just one architecture. Instead, they’ll blend elements of several, depending on use cases, legacy systems, team capabilities, and AI goals. The key is aligning architecture with both your technical constraints and your organizational maturity.

Here is a table from my book that compares all the architectures, along with RDW and data lake:

I have done a video describing and comparing all four architectures that you can view here. If you want to learn more about these architectures and the concepts behind them, then check out my book.

Also, I will be discussing this topic on May 14th at 1:00pm ET for a webinar on “Navigating the Human Elements to Modernize your Data for AI Transformation” with myself and Christopher Samulski from Argano. Learn how to build a well-organized, informed, and empowered data team. Plus, we’re raffling off 5 copies of my book to attendees! Register here: https://bit.ly/4cyoM46.