No-Code Data Migration: Why It's Much Harder Than You Think

On paper, moving from one no-code platform to another — or from a no-code platform to custom software — sounds straightforward. Export your data as CSV. Import it into the new system. Rebuild your automations. Done by Friday.

In practice, it’s one of the most underestimated challenges in business technology. Not because the data itself is complex (though it often is), but because no-code platforms store your data in proprietary formats, with proprietary relationships, governed by proprietary logic that doesn’t translate cleanly to anything else.

Here’s what actually happens when you try to migrate, and why it takes three times longer than everyone expects.

Your Data Is Not Just Rows and Columns

The first surprise is that your “data” is much more than the records in your tables. In a mature no-code setup, data includes:

• The records themselves — your customers, orders, projects, tasks, inventory items
• Relationships between records — which customer placed which order, which project contains which tasks, which inventory item belongs to which warehouse
• Calculated fields — formulas, rollups, lookups, and aggregations that derive new information from existing data
• Views and filters — the saved perspectives that different team members use to see the data they need
• Automations — the workflows triggered by data changes (when a status changes, send an email; when a record is created, assign it to a team member)
• Permissions — who can see what, who can edit what, who gets notified about what
• Attachments and files — documents, images, and files linked to records
• History and audit data — who changed what, when, and why

When you export a CSV from Airtable, you get the first item on that list. The other seven don’t come with it. And rebuilding them in a new system is where the real work lives.

Relationships Break on Export

This is the single biggest migration headache, and it catches almost everyone by surprise.

In Airtable, you link records between tables. A “Projects” record links to multiple “Tasks” records. An “Orders” record links to a “Customer” record and multiple “Line Items” records. These links are stored as internal Airtable record IDs — strings like “rec4kF8mN9qZ2pL7.”

When you export to CSV, those links export as the display value of the linked record (usually the primary field), not as a stable identifier. So your “Orders” CSV shows the customer’s name, not a unique customer ID. If two customers share the same name, or if the display field has been changed since the records were linked, the relationship data is corrupted on export.

Rebuilding these relationships in a new system requires:

1. Exporting all tables with their Airtable record IDs (which requires the API, not the CSV export)
2. Creating a mapping between old IDs and new IDs in the destination system
3. Re-establishing every link using the new IDs
4. Verifying that every relationship survived the transfer

For a simple setup with three linked tables, this is manageable. For a mature setup with twelve tables and dozens of relationships, it’s a multi-day project that’s easy to get wrong.

Calculated Fields Don’t Transfer

Every no-code platform has its own formula language. Airtable formulas look different from Notion formulas, which look different from Monday.com formulas, which look different from SQL. None of them are compatible with each other.

A formula in Airtable like:

IF({Status} = "Overdue" AND DATETIME_DIFF(NOW(), {Due Date}, 'days') > 7, "Critical", IF({Status} = "Overdue", "Warning", "OK"))

This doesn’t export. It’s not in the CSV. The only thing that exports is the calculated result at the moment of export — a static snapshot that becomes stale immediately.

In the new system, you need to:

• Identify every calculated field (there are often more than you think)
• Understand the logic behind each one (which might mean reverse-engineering formulas written by someone who’s no longer around)
• Rewrite each formula in the destination platform’s syntax
• Test that the new formula produces the same results as the old one

For a system with 30-50 formula fields — not unusual in a mature Airtable base — this alone can take days.

Automations Must Be Rebuilt From Scratch

No-code automations don’t migrate. Period.

Your Airtable automations, your Zapier Zaps, your Make scenarios, your Monday.com automations — none of these can be exported and imported into another system. They exist only within the platform that created them.

This means every automation needs to be documented, understood (not just what it does but why), redesigned for the new platform’s capabilities, rebuilt from scratch, and tested with real data.

The documentation step is where most migrations go sideways. Automations are built incrementally over months or years by different people. Many are undocumented. Some are duplicates. Some conflict. Some were built to work around a bug that’s since been fixed. Untangling this is detective work.

Format Differences Are Everywhere

Even when data does export and import cleanly, format differences between platforms create subtle problems:

• Date formats — Airtable exports dates in ISO format. Google Sheets might expect DD/MM/YYYY. A date that imports as “12/03/2025” could be March 12th or December 3rd depending on locale settings.
• Multi-select and tags — Airtable exports multi-select fields as comma-separated strings. If any tag values contain commas, the import splits them incorrectly.
• Rich text — Notion’s rich text includes formatting, mentions, and embeds. Exporting to plain text loses it all. Exporting to a different rich text format (Markdown vs HTML) means conversion work.
• Attachments — File attachments are typically stored as URLs pointing to the platform’s storage. Those URLs may expire when you leave. You need to download every attachment before migrating.
• Currency and numbers — “$1,234.56” might import as a number, a string, or cause an error. Every number field needs format verification.

What Doesn’t Transfer at All

Some things simply can’t be migrated, and knowing this upfront saves disappointment: user activity history (who created or modified each record), automation run logs, saved view configurations, notification settings, permissions and access controls, and comments attached to records. All platform-specific. All non-transferable.

These losses aren’t catastrophic, but they add friction to adoption. Your team has muscle memory built around their current views, filters, and notification patterns. In the new system, they rebuild those habits from scratch.

The Timeline Is Always Longer Than Expected

Based on migrations we’ve seen and handled, here are realistic timelines:

Complexity	What’s Involved	Expected Duration
Simple	1-3 tables, no automations, under 5,000 records	1-2 weeks
Moderate	5-10 tables with relationships, basic automations, under 50,000 records	3-6 weeks
Complex	10+ tables, deep relationships, many automations, 50,000+ records	6-12 weeks
Enterprise	Multiple bases/workspaces, complex permissions, hundreds of automations	3-6 months

These timelines include planning, data extraction, mapping, migration, automation rebuilding, testing, parallel running, and team training. Most businesses budget for the “Simple” timeline regardless of their actual complexity, which is why migrations so often feel like they’ve gone wrong.

How to Approach It Right

Accept the real timeline. Whatever you think the migration will take, multiply by three. Budget for it. Staff for it. Set expectations with your team. A rushed migration creates data quality problems that haunt you for months.

Run systems in parallel. Don’t switch off the old system until the new one has been running alongside it for at least two weeks with real users doing real work. This catches issues that testing alone won’t find.

Migrate data in stages, not all at once. Start with your most critical table. Migrate it, verify it, get your team using it. Then do the next table. This limits the blast radius of any problems and gives your team time to adapt.

Treat it as an improvement opportunity. Migration is painful, but it’s also a chance to fix the problems that accumulated in your old system. Those workaround automations? Don’t recreate them — fix the underlying issue. Those redundant fields nobody uses? Leave them behind. Those inconsistent naming conventions? Standardise them now.

Get help if the stakes are high. If your business depends on this data — if losing records or breaking relationships would cause real problems — a migration is not the place to learn by doing. The cost of getting it wrong (lost data, broken processes, frustrated team, extended downtime) almost always exceeds the cost of getting experienced help.

The irony of no-code platforms is that they make it incredibly easy to get started and incredibly hard to leave. That’s not a conspiracy — it’s a natural consequence of proprietary formats, platform-specific logic, and the accumulation of complexity over time. The best time to plan your migration strategy is before you need one. The second best time is now.