Businesses today are overflowing with data, yet often struggle to turn it into actionable insights. Around 402 million terabytes are created daily, and we’re heading toward 181 zettabytes a year in 2025. This flood of information brings opportunities, yet it also worsens a persistent organizational problem: the enterprise data silo. These isolated data pockets hinder collaboration, hide vital insights, and reduce the agility businesses need to succeed.
The main question now is about how to design data management to break down these silos and use its full potential. Two main architectural approaches, the data lake and the data mesh, offer different solutions. Both aim to make data more accessible, but they have different philosophies and outcomes. Picking the right approach is key to changing your data environment from a point of friction to a source of innovation.
A data lake is a centralized place intended to hold enormous volumes of data in its native, raw form, structured, semi-structured, or unstructured.
The central concept is “ingest now, structure and analyze later” (schema-on-read), which gives great freedom. This lets companies support different analytics, save everything, and get reasonably cheap storage.
Though it helps to consolidate raw data for exploration, this centralized approach has certain drawbacks as well. Major, continuous work is needed in managing access, guaranteeing data quality across several sources, and keeping the lake from turning into a disorderly “data swamp.”
As Matthias Patzak from AWS points out in his blog, swamps cause discoverability and usability problems, as well as a bottleneck for the central crew tending the lake.
A typical data lake setup: Raw data is ingested from operations, then refined through pipelines for analytics and ML.
Source: Starburst
As organizations faced the limits of fully centralized models, the data mesh philosophy, started by Zhamak Dehghani, offered a new option. Instead of one huge, centrally managed lake, the data mesh pictures a distributed network of “data products” owned by different business areas.
Imagine it less like one big reservoir and more like many connected, well-kept local water sources. Each source is managed by the group (business domain) that best understands its contents. The main ideas, detailed in the ArXiv paper “Towards Avoiding the Data Mess,” focus on:
Source: Starburst
This approach aims to match how data is managed with how businesses already operate in an agile way.
Deciding between a data lake and a data mesh is a key choice that must fit your organization’s specific situation, maturity, and goals. There’s no single “better” option, only what works best for you.
A data lake often proves its worth when your primary needs revolve around centralized raw data storage and foundational analytics. It can be a strong asset if your organization has a robust central data team capable of managing the associated infrastructure and governance.
The data lake model also fits well if the immediate priority is cost-effective storage for diverse raw data, taking precedence over intricate, real-time data sharing across different business domains. For smaller organizations, or those where a shift to fully independent domain data ownership represents a significant cultural or operational leap, it serves as a practical first step.
However, if your enterprise is grappling with the limitations of centralization, a data mesh becomes an attractive option. If central data teams are causing delays, slowing innovation, and hindering the release of data-driven features, this is a key signal.
A data mesh also supports scenarios where deep, domain-specific knowledge is vital for extracting the real value, quality, and context from your data, empowering these experts directly.
If widespread data silos are blocking cross-functional teamwork and a complete business overview, or if establishing clear accountability for data quality across different business areas is crucial, the data mesh principles of domain ownership and “data as a product” offer a clear way forward.
Data mesh architecture is particularly well-suited for large, complex organizations already employing distributed, agile teams. These teams could significantly gain from more data independence and the ability to quickly update their own data products.
As this ArXiv paper highlights, a certain degree of domain focus and a dedication to improving data governance and platform abilities are necessary for a successful data mesh adoption.
Building a data system isn’t always an either/or choice. More and more, companies see that data lake and data mesh ideas can work together and even improve each other:
Secoda’s blog also points out that a data lake can serve as the raw data repository. Data is then distributed to domains within a mesh to be developed into products.
Zalando, Europe’s top online fashion platform, shows a real example of how a data lake works. Around 2015, Zalando moved from an old, single system to a cloud-based data lake on Amazon S3. This happened because they needed to handle growing data complexity while supporting analytics in their changing microservices setup.
Important parts of their first data lake setup included:
Source: AWS
This gave them a single database. However, as Zalando’s data lake grew to petabytes in size, they ran into usual problems: managing data sharing across hundreds of teams, making sure backups and recovery were robust (S3 versioning was key here), and lowering big storage costs. They solved these by:
Zalando’s experience shows that while a data lake effectively centralizes data, running it at a large scale adds challenges in management, costs, and ease of use. These are problems that the data mesh approach tackles through decentralization and ownership by different business areas.
Our expertise in modern cloud data solutions, along with our understanding of effective DevOps strategies and platform engineering, allows us to help your enterprise in several ways:
Talk to our experts to find out how Kanda can help you design a data strategy that breaks down silos, and set up a solution that truly helps your company use all your data’s power.
The choice between a data lake and a data mesh is a strategic one, closely linked to your organization’s unique situation, maturity, and goals.
The key is to select a system design that not only solves your current data silo challenges but also helps your company use data as a key asset for ongoing growth and new ideas. Stay ahead of the curve with Kanda.
