3 posts tagged with "Software Design"

If you build enterprise SaaS with any kind of permissions model, you've heard this feature request before: "Our admins need to see what a user sees." The motivation is always the same — a user reports they can't access something, or they're seeing the wrong data, and the admin has no good way to verify what's going on from the user's perspective.

The workarounds your customers resort to are painful. They ask their users for screenshots, or schedule screen shares, but these are high-friction interactions. They try to reproduce the issue with test accounts, but test accounts never have the same permissions, the same data, or the same organizational context as the real user.

So sooner or later, the feature request lands on your backlog: let admins log into the app as another user — see the same dashboards, the same data, the same permissions. Even before users complain about missing access.

But the moment you say "admin impersonation" in an enterprise context, alarm bells go off. Can the admin modify the user's data? Can they access private information? Is there an audit trail? How do you prevent this from becoming a backdoor?

These are fair concerns. At Pigment, we built a user-facing impersonation mode that lets admins review what users see safely. It's been in production for a while now, and our customers love it. Here's how we designed it.

At Pigment, we provide a central platform for organizations to model and plan using their most valuable data. As part of that mission, enabling predictive insights is key. Our first implementation of predictions worked well for smaller datasets, but as more users began applying the feature to larger and more complex data models, we quickly hit the limits of a single-machine setup in terms of memory and compute.

To address this, we redesigned our infrastructure around a distributed Dask cluster, allowing us to scale horizontally and support much larger workloads. In this post, we’ll share the architecture of our new solution and key takeaways from adopting Dask in production.

As our infrastructure scales, our Terraform codebase grows. To remain productive, we need to keep our code flexible and extendable. A design pattern we find useful is the pure Terraform module. Let's dive into what it is and how it helps us write maintainable Terraform code.