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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 is not hype , but a practical observation: the data is already there, in ERPs, CRMs, sensors, spreadsheets, operational histories, but remains disconnected from business intelligence.
While many companies still struggle with silos, duplication, low 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 a useful flow, 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 ceased to be a trend and has become critical infrastructure for anyone who wants data to truly work in favor of decision-making.
Shall we go?
2. Data lake : the concept behind analytical flexibility
Today, few companies suffer from a lack of data. The real challenge is to activate this information quickly and securely , and to make it flow 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 start.
According to 451 Research , 52% of companies have already migrated their unstructured data to data lakes , seeking greater flexibility and integration between systems and analyses. This shows that the adoption of the model is already part of the reality for those who need to respond quickly to business demands based on increasingly varied data, and in real time.
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 analyses. It is robust, reliable, and works very well in predictable scenarios, provided 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, arises from the need to deal with current complexity: multiple sources, varied formats, and constant changes in the questions the business needs to answer. It allows storing data 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 halting operations with lengthy restructuring processes.
2.2. When does it make sense to consider 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 speed . And this scenario is already a reality for a large number of companies.
If your organization deals with multiple sources (such as ERP systems, CRMs, sensors, spreadsheets, and APIs) and needs to cross-reference this information quickly, a data lake ceases to be a technical option and becomes a strategic necessity .
It is especially useful when:
- The data arrives in different formats, not always structured;
- Business areas require more autonomy and speed in analysis;
- AI, BI, or automation projects are on the radar, but the current model isn't up to the task.
In these situations, a data lake allows the company to move forward without having to remodel everything for each new use. It centralizes, organizes, and prepares data so that intelligence happens with less friction and more results.
As data ceases to follow a fixed pattern and begins to reflect the real complexity of the business, the data lake proves not only useful but inevitable. It organizes what was previously scattered, gives context to what was just 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 : the right environment is needed. And at this point, the choice of 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.
It's not enough to create a modern data repository if it's tied to an infrastructure that ages too quickly. The logic of a data lake is one of continuous growth, diverse sources, and real-time analysis, and this demands an environment that keeps pace with this dynamic .
Trying to sustain this model in data centers means stifling innovation within physical limits, unpredictable costs, and inflexible operations. In the cloud, however, the data lake finds the ideal scenario for frictionless , agile integration of new technologies , and ensuring security from the outset .
It is in this combination of freedom and control that the cloud excels. And not only as a technical environment, but as a facilitator of a new way of operating with data, as we will see below.
3.1. Real technical and operational benefits
Adopting a data lake doesn't just mean transferring files to another environment; it means rethinking how data is stored, processed, and accessed. It's a structural change that reduces technical bottlenecks and opens up space for faster, more business-aligned decisions.
In practice, this translates to:
- On-demand scalability : your infrastructure grows with the volume and complexity of 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 workload : operations become more fluid, and the technical team can focus on evolving the environment, not just maintaining the basics.
Not surprisingly, more than 60% of corporate data is already in the cloud , according to Dataversity . This strengthens the integration between data sources, data consistency, and data governance. And the data lake becomes a living infrastructure that evolves along with the business.
3.2. Ready for the AI, BI, and automation ecosystem
More than just offering space, the cloud provides ready-made service layers that facilitate the activation of data by artificial intelligence (AI) platforms, business intelligence (BI) , and automated system integration flows.
This drastically reduces the time and complexity required to get projects up and running. 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 is not the lack of tools, but the data architecture. If data doesn't circulate, intelligence doesn't happen.
In the cloud, the data lake ceases to be a sophisticated silo and becomes a platform for continuous activation , 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 larger than a repository: it transforms it into an hub for constantly moving data. But no potential is realized in isolation. To reap the benefits, it's necessary to structure this environment with solid criteria and a forward-looking vision .
That's what we explore next: how to build a data lake that not only works, but keeps pace with the speed of the questions your business needs to answer.
4. What to consider when structuring your data lake
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 and not an engine of intelligence.
Structuring a data lake in the cloud requires vision, yes, but also practical decisions: about sources, access, governance, and growth. Therefore, the secret lies less in following ready-made formulas and more in creating a foundation that evolves along with 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 data lake in the cloud is not an IT project: it's a strategic decision that requires 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 objectives : understanding not only where the data is located, but also 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 layers of access and security : creating policies that combine protection with user autonomy, and that are designed to scale from the start.
In other words, it's not just about moving data, but about preparing it to generate value from the very 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;
- Automating governance : with clear rules for classification, retention, and access 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 is this combination that transforms the data lake into a solid and sustainable foundation , ready to grow alongside the analytical ambitions of the business.
But you don't need to build everything from scratch, nor face this journey alone. Platforms already prepared to handle this complexity, as we will see below, can accelerate the process, avoid pitfalls, and ensure that the data lake delivers value from the start. Keep reading!
5. In practice: why Skyone accelerates this journey
Up until now, we've discussed concepts and ideal structuring for cloud-based data lakes how we put all of this into practice , and why choosing our platform can be the step that transforms theory into results from the very beginning.
At Skyone , we believe that value comes from action, not complexity. That's why our solutions, like Skyone Studio, have a single focus: activating old and new data in a ready-to-use 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 for data intelligence, with IT as the catalyst and business areas exploring results with greater autonomy, agility, and confidence.
In practice, the key 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 specialists 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 has ceased to be merely an input for analysis and has become a layer of intelligence present throughout the entire operation. What is expected for the coming years is not a linear growth in the volume of data, but rather a profound transformation in the way it flows, connects, and translates into decisions, in real time, with security and autonomy.
In this scenario, data lakes are consolidating themselves as a key point of modern analytical architecture. They are what allow us to deal with the variety, speed, and volatility of today's real-world data. But, more than that, they are what enable a new operating model , where data doesn't sit still waiting for someone to look for it, but circulates, learns, and proactively to business needs.
The companies that are advancing most in this direction are no longer debating whether or not to go to the cloud. They are discussing how to structure this transition intelligently, leveraging what already exists and creating a foundation for what is yet to come. In this sense, platforms like Skyone's show that, with the right choices, it is possible to accelerate this journey without sacrificing control, security, or context .
Therefore, if the future of data lies in the cloud, the next step is to ensure that 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. This is especially true because it's not just a technology project, but a decision that affects processes, people, and business strategy.
Below, we've compiled direct answers to the questions we hear most often from those on this journey or about to begin.
1) In what scenarios does a data lake become the best choice?
A data lake is the best choice when a company deals with data from multiple sources—structured, semi-structured, or raw—and needs to centralize everything flexibly. It's ideal for contexts where data grows rapidly, comes in diverse formats, and fuels initiatives like AI, BI, automation, or ad hoc . It also excels when business areas demand more autonomy in data exploration, without depending on IT for every new question.
2) Why deploy the data lake directly on a platform like Skyone's?
Because it eliminates the complexity of starting from scratch and accelerates the value delivered by data. With Skyone, you connect legacy systems to the cloud without needing to rewrite systems or interrupt operations, and you 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 considerations to ensure a scalable and reliable data lake
Three pillars support a data lake that is ready to grow:
- An elastic architecture (that 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 just storing data, the focus should be on preparing it to flow with context, quality, and speed.
