The dispersion of data calls for different approaches, ranging from the efficiency of the lakehouse to the versatility of the data fabric or the decentralization of the data mesh.

More than half of European companies’ infrastructure is already in the cloud. In practice, this means that the traditional concept of a centralized repository is no longer relevant. Information is no longer stored under one roof, but is distributed across multiple platforms and network environments. And that has consequences.

When management requests an analysis that requires cross-referencing data from various departments, it is necessary to establish complex connections that are time-consuming and increase network transfer costs (known as “egress fees”). The result is a delay in locating and validating the information, which ultimately affects the entire company.

Centralizing all information in a single, massive repository is no longer a realistic option. Companies at the forefront of the cloud approach have accepted this reality: data dispersion is here to stay. The question is no longer how to eliminate it, but how to navigate it. That is why the adoption of approaches such as the lakehouse, the data fabric, and the data mesh—which are used to solve different types of problems—is on the rise.

 

Eliminate storage redundancies with a lakehouse

It is becoming increasingly common for two parallel infrastructures to coexist within a company: a data lake for storing raw data intended for Big Data applications and a data warehouse for queries by business analysts. The problem is that this duplication leads to inconsistencies in reporting, forces engineering teams to constantly replicate data, and ultimately drives up costs.

The lakehouse model helps address this redundancy by unifying both capabilities into a single platform. Its design applies transactional and data integrity capabilities (using the ACID standard) directly to low-cost object storage systems. This allows analysts to run high-speed SQL queries on the same database that engineers use to train machine learning models. This streamlines operations and reduces costs.

In addition, the unification of the data infrastructure makes it easier to take the final step from accumulated data to strategic decision-making, transforming repositories that were once static into dynamic assets that respond at the pace required by the market.

 

Access data without moving it with the data fabric

There are situations in which moving data is not only complicated, but downright impossible. For example, the General Data Protection Regulation restricts the transfer of personal data outside the European Economic Area without additional safeguards. Other times, the problem is the volume of data or the cost of migrating legacy systems. This forces organizations to design manual integrations for each new analytical need in these distributed environments, which overwhelms IT resources.

The data fabric architecture provides the flexibility needed in these scenarios by adding a virtualized abstraction layer on top of existing databases. Using active metadata, this architecture continuously identifies and catalogs the information that exists throughout the enterprise. This makes it possible to connect data without moving it or altering its structure.

When an analyst runs a query, the data fabric locates the records in real time, processes the request in a distributed manner, and returns the result in a unified interface without the files ever leaving the server. This eliminates the need to create a separate integration for each query, which would also quickly become obsolete.

 

Empowering Each Department's Data with a Data Mesh

According to Eurostat, 78.8% of large European companies already perform data analysis internally within their respective business areas. But this can lead to organizational problems. When a department needs an analytical dashboard, the request usually goes to an IT team that is proficient in infrastructure but sometimes lacks in-depth domain knowledge, so the result falls short of expectations.

Data mesh is based on a different approach: instead of centralizing, it distributes responsibility. It divides data into domains and has each department take charge of cleaning, structuring, and making its own datasets available to the rest of the company as ready-to-use products accessible via standard APIs.

To ensure that this autonomy does not lead to chaos or compliance issues, the data mesh model must be supported by a federated governance structure that coordinates security standards across different areas. This aspect takes on particular importance given the role that data governance plays in the development of artificial intelligence.

 

Find solutions to your data challenges at Big Data & AI World 2026

Understanding how the lakehouse, data fabric, and data mesh can solve different types of business problems is the first step toward addressing data fragmentation. The next step is to learn how these architectures are implemented in real-world environments and to design a strategy tailored to the business. The upcoming edition of Big Data & AI World Madrid on November 4 and 5 is the ideal event to explore these topics.

As part of Tech Show Madrid, Spain’s leading technology event for businesses, this year’s Big Data & AI World brings together more than 40 leading exhibitors and 70 international speakers to explore industry trends: from business intelligence and predictive analytics to machine learning and generative AI.

With more than 4,000 attendees expected (37% of whom are C-level executives such as CEOs, CDOs, and AI directors), Big Data & AI World 2026 is the perfect setting to get a head start on what’s to come, engage in high-quality networking, and find the strategic partners who can help take your business’s data architecture to the next level.