Ultimate Checklist for Migration from Fivetran

Data migration is often more than adopting a new tool. You have to learn the new platform while also protecting data integrity, minimizing downtime, and ensuring your existing workflows remain intact.

The stakes are even higher when moving away from Fivetran, as your pipelines are deeply tied to business-critical reporting and decision-making. That’s why you need a structured checklist to make it easier.

It can help you break the migration down into manageable steps, anticipate risks, and transition smoothly without disrupting operations.

In this guide, we share the ultimate checklist to make your Fivetran migration easy and secure.

Phase 1: Pre-Migration Planning

A Fivetran migration starts well before any data is moved. First, plan your goals based on your current data workflow.

Define Goals and Scope

Start by clarifying why you want to move away from Fivetran. Is it the rising costs, limited transformation options, or performance issues? Linking the migration to specific business objectives keeps the project aligned with real needs.

Next, set a realistic timeline. A smaller setup with fewer than ten connectors may take a month, while complex environments with custom logic and multiple dependencies can take several months. Factor in the budget for both the new platform and the internal hours required.

Finally, establish success metrics and risk tolerance early. Success metrics often include zero data loss, a short cutover window, and data accuracy of 99.9% or higher. Risk tolerance refers to the disruption your organization can handle during migration. Some teams can accept short reporting delays, while others require uninterrupted real-time data flows.

Establishing these benchmarks upfront ensures everyone on the team knows what a successful migration looks like.

Inventory and assessment

With the goals in place, the next step is to understand what your Fivetran setup supports and lacks. List every connector you use, along with the source systems, sync frequencies, and data volumes. This helps you spot potential gaps that could complicate migration.

Document existing ETL processes in detail. Include custom transformations, quality checks, and workarounds that may have been added over time. Pay close attention to connectors that handle sensitive or regulated data, since these often require additional control.

Map all downstream dependencies to identify which dashboards, reports, or applications rely on each data stream. Some may need real-time updates, while others can run on daily batches. This ensures you avoid surprises and select a platform that meets both current and future needs.

Phase 2: Platform Selection

No business wants to switch tools every six months, right? The platform you choose will shape how reliably your data moves in the future.

Evaluate options

If you already have a development team and want full control over your data pipelines, consider open-source options like Airbyte. However, if you don’t have a technical team, fully-managed cloud solutions like Hevo are your best bet.

Once you know your route, evaluate tools by their connector coverage. Your chosen data migration tool should support all your current sources. If you work with niche systems, verify that the connectors are reliable or that substitutes won’t create manual work later.

Processing modes are just as critical. Batch syncs may suffice for periodic reporting, while real-time streaming becomes necessary in operational dashboards or alerting systems.

Costs can differ significantly across platforms. Compare whether pricing is volume-based, event-based, or connector-based, and project how costs will scale with your data growth. Consider not just subscription costs, but the total cost of ownership (TCO), including data volume charges, transformation compute, additional user seats, and support.

Don’t forget to weigh this decision against your long-term goals. A tool that looks cheaper or faster today can become a burden if your business needs shift later.

Architecture design

While connector coverage and pricing provide an overview of the tool, there’s more to consider when choosing a Fivetran alternative. Evaluate how the tool supports your architecture needs.

Start with source-to-destination mapping. The tool should help you clearly track how data flows from source to target and handle enrichment, filtering, or aggregation without excessive custom work.

Some tools offer built-in error handling and monitoring with alerts for failed syncs or performance issues. Assess the visibility it provides and whether it matches your operational standards.

Check if the platform supports staging and production environments. Without this, testing becomes risky, and every change might feel like a gamble. The right tool makes this separation straightforward and reliable.

Want to see how Hevo performs against Fivetran in all these aspects? Check out our detailed Hevo vs Fivetran article for a thorough comparison of both tools.

Phase 3: Data Preparation and Backup

This is where you start prepping your data for migration. The aim is to protect your data and avoid any potential mistakes.

Data quality

Perform thorough data profiling on each of your source systems. This uncovers gaps, inconsistencies, and format variations that could otherwise stall new pipelines. Once the gaps are visible, resolve the critical ones so you aren’t forced into fixes during cutover. 

Bring consistency across formats by defining clear mapping rules for dates and numeric precision. Test them thoroughly in a staging setup to confirm accuracy.

Ensure that your schemas are mapped properly. Document transformations already handled by Fivetran and decide what to replicate in the new platform. However, it’s important to keep schema evolution in mind, as business requirements rarely stay static for long.

Backup and recovery

Before you begin data migration, export all your Fivetran configurations. This includes connector settings, transformation logic, and schedules, for a complete reference if something breaks later. Keep these backups in a version-controlled repository where they can be tracked and restored.

However, don’t assume backups are reliable without testing them first. Test the restores in a staging environment and create clear runbooks for how to roll back if critical issues surface during cutover.

Phase 4: Testing and Validation

Testing is where your migration planning meets reality to validate whether your choice of tool works as expected under real-world conditions.

Environment setup

Set up a staging environment that mirrors your exact production system in Fivetran. All connectors, transformations, and destination settings should match to ensure that what works in staging will work in production. Minor differences can cause unexpected failures during cutover.

Use both Fivetran and the new platform on the same datasets simultaneously. This parallel processing will reveal discrepancies in outputs and highlight any performance gaps before a complete switch.

Test types

As you transfer connector data, check integrity by comparing row counts, data types, and sample records between your current system and the new platform. Automated reconciliation scripts also help you catch discrepancies quickly.

Simulate realistic workloads to test performance. Process peak volumes and concurrent jobs to see how the platform handles production-level stress.

Additionally, remember to conduct end-to-end integration tests with all downstream systems. This will confirm whether the dashboards, reports, and applications work correctly and the business workflows remain unaffected.

Phase 5: Execution and Cutover

Now that you are sure the new tool meets your expectations, shift your focus from testing to safely moving data and switching systems with minimal disruption.

Pre-cutover

Communicate clearly with all stakeholders at least a week in advance. Share cutover schedules, expected data downtime, and points of contact for real-time updates.

Prepare a rollback plan with well-defined criteria that specify conditions that would trigger reverting to Fivetran. This could be data loss above a set threshold or downtime beyond an acceptable window.

It’s important to understand the risks of tool malfunction and take precautions accordingly. Assign decision authority to a responsible team member to avoid hesitation during critical moments. Lastly, schedule the cutover during low-impact periods to minimize disruption.

Cutover and monitoring

You are now ready to carry out the final data migration. However, don’t transfer all your data at once. Do it one connector at a time, starting with less critical connectors. This prevents failure and allows a quick response.

Pay close attention to the connectors during the first 48 to 72 hours. Monitor progress in real-time using automated checks to ensure accuracy. Data pipeline automation helps you reduce manual oversight. Take an extra precautionary step by setting up alerts to notify your team instantly of errors.

Most importantly, maintain open communication throughout the cutover and record any deviations from the plan for post-migration review and continuous improvement.

Phase 6: Post-migration Optimization

Your job isn’t done yet. After the migration, you need to ensure performance and long-term reliability to keep your data flowing efficiently.

Validation

Once you’ve completed the migration, verify that all data has transferred accurately and systems perform as expected. Since you still have Fivetran running in parallel, compare outputs with results on the new platform and focus on business-critical reports and metrics. Retire Fivetran completely only when you are 100% sure of the new pipeline. 

It’s a good idea to have business users validate dashboards and analytics workflows through user acceptance testing (UAT). Their feedback can bring out perspectives and issues that technical checks might have overlooked. Schedule these sessions with enough time to address any problems before considering the migration complete.

Now, it’s time to go back to your pre-migration benchmarks. Measure processing times, resource usage, and cost metrics to determine the success of the migration. If you find any gaps remaining, brainstorm ways to bridge them. These minor improvements can significantly impact your pipeline health.

Ongoing monitoring

The final step to your Fivetran migration journey is consistent monitoring. This is what turns a modern data architecture from a concept into a high-performing system.

Keep a close eye on your post-migration environment with KPI dashboards that show data freshness, sync success rates, and processing costs. These dashboards give your team a clear view of performance and opportunities for further workflow optimization.

Set up alerts for quality or performance issues and define thresholds that reflect business priorities. Like any other business strategy, periodic reviews help you refine these processes, improve efficiency, and build a stable data ecosystem.

Why Consider Hevo Data as Your Fivetran Alternative?

If you are considering moving away from Fivetran, Hevo is one of the most dependable choices. Its event-based prices start at $239 per month, so you don’t have to worry about billing surprises often tied to Fivetran’s pricing. This makes budgeting far simpler and more reliable.

Hevo is flexible, it supports over 150 pre-built connectors, and allows schema management that simplifies complex environments. You can merge tables across sources, enforce naming conventions, and apply transformation logic without heavy coding.

Where Fivetran might lag with large data volumes, Hevo’s streaming-first architecture built on Kafka handles both streaming and batch workloads seamlessly. You also get exactly-once processing and column-level lineage for complete accuracy.

Transformations are also equally adaptable. Hevo supports Python, SQL, dbt, and drag-and-drop workflows, with options for both pre- and post-load processing. Add enterprise-grade compliance, strong SLAs, and 24/7 support, and Hevo gives you a clear path to efficient and scalable data integration.

Want to see it in action?

Build scalable, no-code data pipelines with Hevo → Book a demo now!

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