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Introduction
Global investment in data lakes has more than doubled in less than two years, jumping from US$13.7 billion in 2024 to over US$25 billion in 2025 , according to a survey by Scoop Market Research . The reason behind this accelerated growth isn't hype , but a practical observation: the data is already there, in ERPs, CRMs, sensors, spreadsheets, and operational logs, but it remains disconnected from business intelligence.
While many companies still struggle with silos, duplication, poor quality, and wasted time gathering information, others are building a unified, flexible, and scalable environment : the data lake . And it's not about storing more, but about accessing it better; about transforming a raw volume into useful data, and of course, doing so securely, quickly, and with native integration with the tools that drive the business.
In this article, we show why the data lake has gone from being a trend to becoming critical infrastructure for those who truly want data to work for decision-making.
Let's go?
2. Data lake : the concept behind analytical flexibility
Today, few companies suffer from a lack of data. The real challenge is activating this information quickly and securely , and ensuring it flows to where it generates value. This is the role of the data lake : an environment that centralizes raw data from different sources and formats, keeping it accessible for analysis, integration, and automation, without requiring a rigid structure from the outset.
According to 451 Research , 52% of companies have already migrated their unstructured data to data lakes , seeking greater flexibility and integration between systems and analytics. This shows that adopting this model is already part of the reality for those who need to respond quickly to business demands based on increasingly diverse, real-time data.
But what exactly differentiates a data lake from other traditional structures? And when does it cease to be a technical possibility and become a strategic path?
2.1. What changes in relation to the data warehouse
The data warehouse emerged with a clear purpose: to centralize structured data for repetitive and historical analysis. It's robust, reliable, and works well in predictable scenarios, as long as the data is clean, standardized, and organized before entering the system . This approach is called schema-on-write .
The data lake , on the other hand, arose from the need to deal with today's complexity: multiple sources, varied formats, and constantly changing business questions. It allows data to be stored in its raw format, structuring it only when necessary, an approach known as schema-on-read .
This logic makes the data lake more suitable for exploring new correlations, testing hypotheses, and integrating technologies such as AI, automation, and real-time analytics, all without hindering operations through lengthy restructuring processes.
2.2. When does it make sense to think about a data lake?
The comparison with a data warehouse makes it clear: a data lake is ideal for contexts where data constantly grows in volume, variety, and velocity . And this scenario is already a reality for many companies.
If your organization deals with multiple sources (such as ERP systems, CRMs, sensors, spreadsheets, and APIs) and needs to quickly cross-reference this information, a data lake ceases to be a technical option and becomes a strategic necessity .
It is especially useful when:
- Data arrives in different formats, not always structured;
- Business areas demand more autonomy and speed in analysis;
- AI, BI, and automation projects are on the radar, but the current model isn't enough.
In these situations, the data lake allows the company to move forward without having to reshape everything for each new use. It centralizes, organizes, and prepares data so that intelligence occurs with less friction and more results.
As data ceases to follow a fixed pattern and begins to reflect the true complexity of the business, the data lake proves not only useful, but inevitable. It organizes what was previously scattered, gives context to what was merely volume, and transforms variety into value.
But this architecture alone is not enough. For the data lake to deliver its potential with scalability, performance, and security, it is necessary to go beyond the structure : it requires the right environment. And at this point, choosing the cloud ceases to be a matter of convenience and becomes a strategy. Let's understand why?
3. Why the cloud is the ideal environment for your data lake
Creating a modern data repository isn't enough if it's tied to an infrastructure that's aging too quickly. The logic of a data lake is one of continuous growth, source diversity, and real-time analysis, and this requires an environment that can keep up with these dynamics .
Trying to sustain this model in data centers means hampering innovation with physical constraints, unpredictable costs, and inflexible operations. In the cloud, the data lake finds the ideal environment for frictionless , agile integration of new technologies , and ensuring security from the source .
It's in this combination of freedom and control that the cloud excels. And not only as a technical environment, but also as a facilitator of a new way of operating with data, as we'll see below.
3.1. Actual technical and operational benefits
Adopting a cloud data lake rethinking how data is stored, processed, and accessed. It's a structural change that reduces technical bottlenecks and opens the door to faster, more business-oriented decisions.
In practice, this translates into:
- On-demand scalability : your infrastructure grows with the volume and complexity of your data, without the need for constant reconfiguration or heavy investments in local servers;
- Resilience and continuity : with automatic replication, fault tolerance and backups , the risks of downtime and data loss are minimized;
- Reduced IT burden : operations become more fluid, and the technical team can focus on evolving the environment, not on basic maintenance.
It's no surprise that over 60% of corporate data is already in the cloud , according to Dataversity . This strengthens cross-source integration, consistency, and data governance. And the data lake becomes a living infrastructure that evolves alongside the business.
3.2. Ready for the AI, BI, and Automation Ecosystem
More than just offering space, the cloud offers ready-made service layers that facilitate data activation through artificial intelligence (AI) platforms, business intelligence (BI) , and automated system integration flows.
This drastically reduces the time and complexity of implementing projects. And it's no coincidence: Qlik survey , 94% of companies increased their investments in AI , but only 21% managed to successfully operationalize these initiatives. This highlights a critical point : the bottleneck isn't the lack of tools, but the data architecture. If data doesn't flow, intelligence doesn't happen.
In the cloud, the data lake ceases to be a sophisticated silo and becomes a continuous activation platform , where AI, BI, and automation no longer depend on IT to function and begin to respond directly to business demands.
By combining technical elasticity with intelligent connections , the cloud transforms the data lake into something much more than a repository: it transforms it into a hub of activation for data in constant motion. But no potential is realized alone. To reap the benefits, you need to structure this environment with solid criteria and a vision for the future .
That's what we explore next: how to build a data lake that not only works but also keeps up with the speed of the questions your business needs to answer.
4. What to consider when structuring your data lake
Far beyond technology, building a data lake begins with a simple question: what does your company want to do with the data? Without this clarity, the risk is building just another repository, not an intelligence engine.
Structuring a data lake in the cloud requires vision, yes, but also practical decisions: about sources, access, governance, and growth. Therefore, the key is less about following ready-made formulas and more about creating a foundation that evolves alongside the business.
So, let's talk about what really matters to transform the project into value from the start.
4.1. Fundamental implementation steps
Implementing a cloud data lake well-defined foundations . It all starts with mapping the sources and types of data, structured or unstructured, and clearly defining how this data will be extracted, organized, and made available for use.
The most critical steps in this process include:
- Data inventory and usage goals : understand not only where the data is, but how it will be used, by whom, and how often;
- Building ingestion and cataloging pipelines : ensuring that data enters the data lake smoothly, with well-defined metadata, versioning, and context;
- Structuring access and security layers : create policies that combine protection with user autonomy, and that are already designed to scale.
In other words, it's not just about moving data, but preparing it to generate value from the first insight .
4.2. How to ensure scalability and control
Growing with data is inevitable, but growing with control is a choice. Without planning , even the best data lake can become a new bottleneck, with excess data and little value delivered. Ensuring scalability and governance relies on three fundamentals:
- Elastic and distributed architecture : that accommodates different types and volumes of data without constant re-engineering;
- Governance automation : with clear classification, retention, and access rules based on profiles and projects, not silos;
- Native visibility and traceability : knowing, in real time, what is being accessed, by whom and with what operational impact.
It's this combination that transforms the data lake into a solid and sustainable foundation , ready to grow alongside the business's analytical ambitions.
But you don't have to build everything from scratch, nor face this journey alone. Platforms already prepared to handle this complexity, as we'll see below, can speed up the process, avoid pitfalls, and ensure the data lake delivers value from the start. Stay tuned!
5. In practice: why Skyone accelerates this journey
So far, we've discussed concepts and ideal structuring for cloud data lakes how we put all this into practice and why choosing our platform can be the step that transforms theory into results from the start.
At Skyone , we believe that value comes from action, not complexity. That's why our solutions, like Skyone Studio, have a single focus: activating legacy and new data in a ready-made analytical environment capable of scaling without losing control and security.
5.1. Data lake with embedded intelligence: Skyone Studio
Static storage is no longer enough. That's why Skyone Studio transforms the data lake into a living platform , with automated
pipelines This is how we enable a new pace of data intelligence, with IT as the catalyst and business areas exploring results with greater autonomy, agility, and confidence.
In practice, the difference lies in how it all connects Skyone 's support , you don't just build a data lake : you create an intelligent, agile, and secure environment, ready to scale with your business, from legacy data to future AI projects.
Want to see this difference in your company? Talk to one of our experts and learn how to transform scattered data into faster, more assertive, and strategic decisions!
6. Conclusion: What to expect from cloud data in the coming years?
Data is no longer just an input for analysis; it has become a layer of intelligence present throughout operations. What's expected in the coming years isn't linear growth in data volume, but rather a profound transformation in how it flows, connects, and translates into decisions—in real time, securely, and autonomously.
In this scenario, data lakes are consolidating themselves as a key element of modern analytical architecture. They enable the variety, velocity, and volatility of today's real-world data. But more than that, they enable a new operating model , where data doesn't sit idle waiting for someone to search for it, but circulates, learns, and proactively to business needs.
Companies that advance most in this direction no longer debate whether or not to move to the cloud. They discuss how to structure this transition intelligently, leveraging what already exists and laying the foundation for what's yet to come. In this sense, platforms like Skyone show that, with the right choices, it is possible to accelerate this journey without giving up control, security or context .
Therefore, if the future of data lies in the cloud, the next step is to ensure this move is strategic. To continue exploring possible paths, also check out this other article on our blog , "Enterprise Cloud Storage: The Practical Guide You Needed .
FAQ: Frequently Asked Questions about Cloud Data Lakes
Between the interest in transforming data into value and the practice of structuring a data lake in the cloud, many questions arise. Especially since this isn't just a technology project, but rather a decision that impacts processes, people, and business strategy.
Below, we've gathered straightforward answers to the questions we hear most often from those on this journey or about to begin.
1) In what scenarios is data lake
A data lake is the best choice when a company handles data from multiple sources—structured, semi-structured, or raw—and needs to centralize it all flexibly. It's ideal for contexts where data grows rapidly, comes in diverse formats, and feeds initiatives such as AI, BI, automation, or ad hoc . It also excels when business areas demand greater autonomy in data exploration, without relying on IT for each new question.
2) Why deploy the data lake directly on a platform like Skyone?
Because it eliminates the complexity of starting from scratch and accelerates the value delivered by data. With Skyone, you can connect legacy systems to the cloud without having to rewrite systems or disrupt operations, and structure your data lake with Skyone Studio, ready to scale with governance, automation, and embedded intelligence. The result is an environment that integrates, protects, and activates your data with much less friction.
3) What are the main technical precautions to ensure a scalable and reliable data lake
Three pillars support a data lake that is ready for growth:
- An elastic architecture (which adapts to the volume and diversity of data);
- Automated governance (with clear rules from ingestion to use); and
- Real-time visibility (to understand how data flows and ensure security).
More than storage, the focus should be on preparing data to flow with context, quality, and agility.
