This blog post is AI-Assisted Content: Written by humans with a helping hand.
Nothing hurts quite as bad as losing data. We recently helped a client recover from a production data loss in a Sigma app — not from a bad import or a broken formula, but from a version restore followed by a tag update. Sigma did exactly what it was told. The problem was a subtle but important misunderstanding of how version tagging, input tables and version restoration interact.
If your Sigma app uses input tables to capture data this one is worth understanding. Here’s what happened, why it’s an easy trap to fall into and three tactics that can protect your data going forward.
The Trap: Restoring a Version Rewinds Your Input Table Data
Version tagging in Sigma is a powerful feature. It lets you maintain separate published states of a workbook while continuing to iterate on the draft without affecting your end users. It’s Sigma’s answer to a proper software development lifecycle, and when used well, developers can iterate safely, while production apps do their thing.
However, there is niche interaction between three Sigma features: Tagged versions, Input tables and Version restore. Under the hood, Sigma stores input table data as a sequential write-ahead log in your warehouse — an append-only record of every change ever made to the table. When you restore a workbook to a previous version, Sigma effectively moves its read pointer back to the point in time of the version you are restoring to. Any data entered after that point is no longer part of the active view. When you then update the Production tag to reflect that restored version, that rewound state becomes the authoritative view, and all the newer data is orphaned. Even though you only restored the published view, the tagged versions’ input tables will rewind in sync.
There’s no warning prompt. No confirmation dialog. It’s documented behavior, but it’s easy to miss until it bites you. If your workbook has no input tables, a version restore is perfectly safe; but if you are collecting data in an AI-App, then this is a very important limitation to consider.
The Fix: Move Forward, Not Backward
The solution is not to abandon version tagging for AI-apps. The benefits are too great to ignore. Instead, we are adopting a simple philosophy: Treat the native Sigma version history like an audit log, not a rollback mechanism. You read from it, you don’t write back to it.
When something breaks, it’s easy to reach for the restore button for a quick fix. In many software development contexts, that’s a reasonable instinct. However, in a Sigma app with live input tables, the right move is to fix the issue in a new version. The added benefit here is that the main branch of your app development — the “Published” version according to Sigma — maintains a linear development path, providing clarity in the development lifecycle.
The move forward not backward philosophy sounds great, but the inability to use the native version restoration feature leaves a hole for clean app development and data security. Here are some ways to help fill that hole, keeping your data safe and your stakeholders smiling.
Three Tactics to Protect Your Input Table Data
- Validation Staging
A Staging tag creates a crucial buffer between development and end users. Before any update goes to Production, first push it to Staging long enough to confirm that existing data hasn’t been disrupted. A validation pass only takes a few minutes and can save you hours in data recovery. If there is an issue, you can fix it in the Published branch before pushing to Production.
A lightweight internal dashboard might not need a dedicated staging environment, but anything where users are entering data does.
- Back Up Your Warehouse Table
Before updating the Production tag, snapshot the current state of the input table’s underlying warehouse data. The specifics will vary by platform. Fortunately, most modern data warehouses make this straightforward. Whether that’s a table clone, a version checkpoint or a time-travel reference, the goal is to create a recovery path that doesn’t involve touching Sigma’s version history.
Work with your data engineering team to establish the right approach for your stack and make it part of the standard push process. The effort here should be proportional to how hard that data would be to recreate if it were lost.
- Keep a Version Log Inside the App
Sigma’s version tags don’t carry custom metadata, so you can’t attach a description or a note to a tag. While Sigma’s audit logs and version history API can tell you who pushed a tag and when, accessing those features requires admin access or external tooling. Additionally, neither source captures what matters most: What changed, why and where the backup lives.
An input table inside the workbook — or some other centralized location — solves this simply. Whoever pushes the tag adds a row capturing the date, a summary of what changed, who did it and a reference to the warehouse backup. It takes about 30 seconds, and it becomes the authoritative record of every production change the app has ever seen. You can also keep validation metrics here such as the number of rows or the latest known change in the data. This way when you push an update to the staging tag, the input table log becomes a canary in the coal mine. If something is wrong here, then your production data is likely in danger as well.
Why This Approach Works
None of these tactics are complicated. So, implementing them is achievable.
Together, they protect your data at every layer. Staging catches issues before they reach live data. The warehouse backup means recovery doesn’t require touching Sigma’s version history. And the version log means that months from now, when someone asks what changed and when, the answer lives right inside the app.
The underlying principle — move forward, never backward — scales well too. Whether you’re maintaining one Sigma app or a whole portfolio of them, treating version history as read-only keeps things predictable. And in production data systems, predictability is everything.
