The short answer: Moving ingestion away from Fivetran is a smaller job than most teams expect, because Fivetran only owns one layer of your stack: the part that copies data from your sources into your warehouse. Your warehouse, your historical data, your dashboards and most of your dbt project stay where they are. You’re swapping the pipe, not the plumbing. The real work is matching Fivetran’s column names and, if you use them, its dbt packages.
What actually moves when you leave Fivetran?
When people picture leaving Fivetran, they picture rebuilding their whole data stack. Here’s what it really looks like:
| Layer | Does it move? |
| Your warehouse (Snowflake, BigQuery, Postgres, etc.) | Stays |
| Historical data already loaded | Stays |
| Your own dbt models | Stay, with source definitions updated |
| Fivetran’s dbt packages | Need a shim layer or a rewrite |
| Dashboards and BI | Stays |
| Ingestion connectors | Moves to Meltano |
| Schedules and alerting | Moves to Meltano |
Two rows out of seven move. One needs some rework. That’s the job.
Five reasons switching from Fivetran is easier than it looks
1. Your warehouse doesn’t move
Meltano loads into the same warehouse Fivetran does. No data migration, no new platform for your analysts to learn, no reports to rebuild. Heavy transformations keep running in the warehouse compute you already pay for.
2. Your own dbt models barely notice
If you wrote your own staging models, dbt barely notices. Your models describe your business logic, and that logic doesn’t care which tool loaded the raw tables. The change is a new schema name in your source definitions and a tweak to anything that references Fivetran’s metadata columns.
If you lean on Fivetran’s dbt packages, it’s more work. We cover that below, because it’s the one bit people get caught out by. Either way, Meltano can orchestrate your dbt project too, so everything runs in one place.
3. The connectors are already there
Meltano Hub covers 600+ sources and destinations. Adding one is a single command, and running it is another:
meltano add extractor tap-hubspot
meltano run tap-hubspot target-snowflake
If a connector is missing, or the existing one isn’t good enough, we build it. Our engineers use AI-assisted development with Claude Code to get new connectors and streams built quickly, with a human in charge of every change.
4. You don’t have to jump
With Mirror Mode, Meltano runs alongside Fivetran, loading into a separate schema while Fivetran keeps serving production. You compare the two, and only switch when you’re happy. Move one connector this week and ten next month if that’s what suits you.
5. You’re not doing it on your own
Every Meltano customer gets direct Slack access to the engineers who build the platform. When something doesn’t look right, you talk to someone who can fix it the same day.
So what does take effort?
It would be dishonest to say nothing does. Here’s where the real work sits:
- Custom or niche sources. If you rely on a connector that doesn’t exist yet, it needs building. We can do that for you.
- Fivetran’s dbt packages. Packages like fivetran/salesforce and fivetran/hubspot are built for Fivetran-loaded data. They expect Fivetran’s table shapes and _fivetran_* columns. Point them at Meltano tables and they can run without errors while quietly returning the wrong numbers. The fix is a thin shim layer, copying the models into your project, or replacing them with your own. We’ll tell you which fits in the audit, and Mirror Mode lets you prove the outputs match before you switch.
- Metadata columns and deletes. Fivetran uses columns like _fivetran_synced and _fivetran_deleted. Meltano uses _sdc_extracted_at and _sdc_deleted_at. Models that filter on these need a small update.
- Schedules and alerts. Your sync schedules and failure alerts need setting up in Meltano. It’s quick, but it’s worth doing deliberately.
- Getting sign-off. Sometimes the hardest part is internal. A parallel run with matching numbers makes that conversation much easier.
None of that is a nightmare. It’s a well-scoped project with a clear end.
What do you get on the other side?
A bill that doesn’t punish growth. Meltano charges for compute hours, not rows. For high-volume sources, that works out as much as 90% cheaper than Fivetran.
Faster pipelines. Meltano’s Arrow-based batch loading made database replication pipelines around 10x faster, and up to 24x faster for loads into Microsoft SQL Server. Here’s how we did it.
Proper change control. Fivetran can be managed as code through Terraform, but it’s optional and sits alongside a UI where changes still happen. In Meltano, your pipelines live in a Git repository you own, and that repo is the config. Changes are reviewed, tested and promoted like any other code, and you upgrade connectors when you choose.
No lock-in, ever again. This is the part people miss. Your pipelines are code, built on open-source foundations, in a repo you control. If you ever want to change tools again, you won’t be having this conversation. That’s the real reason switching to Meltano is easy: it’s the last time switching is hard.
Resident Advisor spent a year firefighting their ingestion setup across multiple vendors and custom scripts. After moving to Meltano, their engineers went back to building product, and delivery got 3x faster.
Frequently asked questions
Is managing Fivetran with Terraform the same as having pipelines in Git?
Not quite. Fivetran’s Terraform provider lets you manage connections as code, but it’s a separate layer with its own state, and the UI can still change things underneath it. In Meltano, the Git repo is the source of truth. There’s no second place for changes to happen.
Am I locked into Fivetran?
Less than it feels. Your data sits in your own warehouse, and your transformation logic sits in your dbt project. What Fivetran owns is the connectors and schedules, and those can be rebuilt and run in parallel before you switch anything off.
Do I have to move all my Fivetran connectors at once?
No. Most teams move connector by connector, starting with the highest-volume or most expensive sources. Mirror Mode means each one can be validated before it goes live.
What happens to my data in Fivetran after I switch?
Nothing. Data Fivetran loaded stays in your warehouse. You can keep those tables, archive them, or drop them once you’re confident in the new pipelines.
Can I keep using dbt if I move off Fivetran?
Yes. Meltano works with your existing dbt project and can orchestrate it for you. If you use Fivetran’s dbt packages, they’ll need a shim layer or a rewrite, because they expect Fivetran’s schema. You can also trigger existing SQL or stored procedures as pipeline steps if you’re not ready to move everything into dbt yet.
What’s the best Fivetran alternative for high-volume data?
For high-volume sources, pricing model matters more than anything else. Row-based pricing grows with every row you move. Meltano’s compute-based pricing grows with the work done, which is why the savings are largest where volumes are highest. See how the two compare on our Meltano vs Fivetran page.
How do I work out what I’d save?
Put your current volumes into the pricing calculator. It takes two minutes.
Find out what switching would save you
The easiest first step is seeing the number. Try our pricing calculator with your current Fivetran volumes.
If you’d rather talk it through, book a call and we’ll go through your connectors with you.
Ready to plan the move? Read How to migrate from Fivetran to Meltano for the step-by-step process.