Introduction
A data lake offers a central repository for storing vast volumes of information in its original format, ready to be processed and made available to those who need it. However, that very flexibility—without a clear structure—often produces the opposite effect. Data lakes rarely start out in disarray. They begin with good intentions—such as quickly connecting a single source to generate an urgent report—but grow through accumulation until they turn into a "data swamp" where no one can explain the origin of any given figure.
At Rootstack, we have participated in the design and remediation of platforms like these. We have learned that the difference between a successful data lake and a failed one rarely lies in the chosen technology. Instead, it almost always comes down to a set of architectural decisions made right from the start.
In this whitepaper, we share that practical experience:
- How to recognize a data lake gone wrong: common symptoms, such as a single metric yielding three different results or changes in a source system breaking unexpected processes.
- What a well-structured architecture looks like: the "medallion" model—featuring Bronze, Silver, and Gold layers—and the specific role (and limitations) of each.
- Files vs. APIs: when to use each mechanism to feed the lake, and why it is important to keep operational and analytical layers separate.
- Conventions and organization for scalability: naming standards, domain-based workspaces, and distinct environments for development, testing, and production. How to migrate existing assets: inventorying reports and automations, mapping equivalencies, and executing a wave-based migration.
- Platform health indicators: how to determine if your data lake is on the right track without needing a formal audit.
If your organization is considering building a data lake, or already has one that isn't delivering the expected results, this guide will help you make the right decisions before data starts flowing in.
