The Fivetran Migration Checklist: A Production-Safe Cutover Plan

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Ripping out a core piece of data infrastructure is terrifying. You know the current tool is too expensive or too slow, but it is a devil you know. The thought of migrating your entire ingestion layer feels like performing open-heart surgery on your data stack while the patient is awake.

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The reality is that a Fivetran migration does not have to be a multi-month ordeal.

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When data teams get bogged down in migrations, it is rarely because the new tool cannot connect to the old sources. It is because they try to cut over without a structured plan for reconciliation, or they realize too late that downstream dbt models expect a very specific schema structure.

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If you are planning a move away from Fivetran, you need a plan that protects production data while you switch the pipes. This checklist covers exactly how to map your dependencies, run a parallel test, and execute a safe cutover.

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Phase 1: Map the Blast Radius

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Before you touch a single pipeline, you have to know exactly what Fivetran is currently touching. This is the discovery phase, and skipping it is the easiest way to break a dashboard the CEO looks at every morning.

Audit Your Active Connectors and Sync Frequencies

Do not trust your memory here. Pull a complete list of every active Fivetran connectors. Note the source type, the sync frequency, and the volume of data moving through it. You will likely find connectors syncing data from SaaS tools that nobody at the company has logged into for a year. A migration is the perfect time to deprecate those pipelines entirely rather than moving them.

Document Your Downstream Dependencies

This is where migrations usually go wrong. Fivetran creates specific system columns in your warehouse, like the synced at timestamp or the deleted flag. Your dbt models almost certainly reference those columns. You need to map which models depend on which raw tables, and flag any logic that relies heavily on Fivetran-specific schema structures.

Establish Your Reconciliation Tolerance

You are going to run both tools side by side and compare the output. Before you do that, you need to decide what success looks like. For financial data, your tolerance for row count discrepancies is zero. For a marketing event log, a tiny variance might be acceptable if the overall trends match. Define those thresholds before you start testing so you are not making judgment calls on the fly.

Phase 2: Build the Parallel Environment

You never want to point a new ingestion tool directly at your production schema on day one. You want a sandbox where you can break things safely.

Set Up a Dedicated Staging Schema

Create a completely separate schema in your warehouse for the new tool. If Fivetran is writing to a raw schema, point your new platform to a staging schema. This ensures the two tools will never step on each other or cause locking issues in the warehouse.

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Run the Historical Backfill

Connect your new tool to your sources and trigger a full historical sync. This is your first real test of the new platform. You are watching to see how long the initial load takes, whether it hits any API rate limits on your source systems, and how the new tool handles the initial volume.

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Compare the Raw Schemas

Once the historical data lands, compare it to what Fivetran loaded. You are looking for structural differences. Did the new tool cast a JSON blob as a string when Fivetran cast it as an object? Did it handle timezone conversions differently? These schema differences are what will break your dbt models, so you have to catch them here and adjust your transformation logic to account for them.

Phase 3: The Parallel Run

This is the most critical part of the migration. You are going to let Fivetran and your new tool run simultaneously for at least a week, usually two.

Monitor the Incremental Syncs

The historical load tells you if the tool works. The incremental syncs tell you if the tool works at scale. Watch how the new platform handles high-churn tables. If you are testing a tool with streaming Change Data Capture, this is where you should see replication times drop significantly compared to Fivetran's batch polling.

Run Your dbt Models Against the Staging Schema

Point a test branch of your dbt project at the new staging schema and run your models. This is the moment of truth. If your models compile and run successfully, your schema mapping was correct. If they fail, you know exactly which transformations need to be updated before you cut over production.

Reconcile the Row Counts

At the end of the parallel run, compare the row counts between the Fivetran schema and the new staging schema. If the numbers match within the tolerance you set in Phase 1, you are ready to move forward. If there are massive discrepancies, stop and investigate. It usually means one tool is handling soft deletes or history mode differently than the other.

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Phase 4: Execute the Cutover

If the parallel run was successful, the actual cutover should be the most boring part of the entire project.

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Pause the Fivetran Syncs

Pick a low-traffic window and pause all Fivetran connectors. You do not want to delete them yet. Just pause them so they stop writing to the warehouse. This is your safety net. If something goes catastrophically wrong, you can turn Fivetran back on and recover.

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Update the Production References

Repoint your production dbt models to read from the new schema instead of the old Fivetran schema. If you built a translation layer to handle any schema differences during the parallel run, deploy that logic to production now.

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Monitor the First Production Run

Trigger a full dbt run and watch the logs. Once the models build successfully, check your core dashboards to ensure the data looks correct. Have a few key stakeholders verify the numbers they care about most.

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Deprecate the Old Infrastructure

Once the new pipelines have been running cleanly in production for a few weeks, you can finally delete the Fivetran connectors, drop the old raw schemas, and close the account. The migration is officially done.

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Why Migrating to Matia Is Different

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A lot of teams delay leaving Fivetran because they assume the migration will be a nightmare. They picture weeks of rewriting dbt models and manually reconciling row counts.

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Matia was built to make this specific transition painless. The platform is fully backwards compatible with Fivetran. That means your pipelines, connectors, and configs migrate without requiring a massive rebuild.

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Because Matia uses a unified DataOps platform, you are not just swapping an ingestion tool. You get ETL, Reverse ETL, Observability, and a Data Catalog in one place. And because Matia monitors data at the source, you get instant Slack alerts if a schema changes during or after the migration, preventing silent failures from reaching your warehouse.

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The results are consistently dramatic. Ramp reduced their sync time by more than 80% after switching. Lopay cut their data costs by 40%. Obligo increased productivity by 83%. Recharge cut Snowflake costs 32% after unifying ETL and Reverse ETL on Matia.

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Customers typically spend 30% less on Matia than Fivetran for ingestion, and report up to a 61% reduction in total data stack spend when consolidating their tools onto the platform.

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Book a demo and we will walk through your current Fivetran setup and build a custom migration plan for your team.

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