Scaling a data warehouse involves more than just managing storage capacity or optimizing query latency. As the volume of data grows, the complexity of managing access and monitoring resource consumption increases proportionally. Without strict controls, a high-performance system can quickly become a security liability or a financial drain.

This chapter focuses on the operational frameworks required to maintain a secure and observable environment. We examine how to structure Role-Based Access Control (RBAC) to manage permissions efficiently, preventing the administrative overhead often associated with manual user management. You will learn to implement fine-grained security measures, specifically Dynamic Data Masking for protecting sensitive fields and Row-Level Security (RLS). For instance, we will define policies where a query returns result set $R$ only if the user's attribute $A$ matches the data's security tag $T$, ensuring users only access data relevant to their role.

We also address the financial aspect of running Massively Parallel Processing (MPP) systems. The content covers methods for observability, including tracking credit usage and configuring automated resource monitors. You will implement quotas and auto-suspend policies to prevent budget overruns common in consumption-based cloud models. By the end of this section, you will be able to apply automated governance rules that scale alongside your data infrastructure.