How Meta Uses AI Agents for Data Warehouse Access and Security

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Disclaimer: The details in this post have been derived from the details shared online by the Meta Engineering Team. All credit for the technical details goes to the Meta Engineering Team. The links to the original articles and sources are present in the references section at the end of the post. We’ve attempted to analyze the details and provide our input about them. If you find any inaccuracies or omissions, please leave a comment, and we will do our best to fix them.

Meta has one of the largest data warehouses in the world, supporting analytics, machine learning, and AI workloads across many teams. Every business decision, experiment, and product improvement relies on quick, secure access to this data.

To organize such a vast system, Meta built its data warehouse as a hierarchy. At the top are teams and organizations, followed by datasets, tables, and finally dashboards that visualize insights. Each level connects to the next, forming a structure where every piece of data can be traced back to its origin.

Access to these data assets has traditionally been managed through role-based access control (RBAC). This means access permissions are granted based on job roles. A marketing analyst, for example, can view marketing performance data, while an infrastructure engineer can view server performance logs. When someone needed additional data, they would manually request it from the data owner, who would approve or deny access based on company policies.

This manual process worked well in the early stages. However, as Meta’s operations and AI systems expanded, this model began to strain under its own weight. Managing who could access what became a complex and time-consuming