Ultimate Checklist for Migration from Fivetran

If you’re reading this, chances are you’ve already made the mental decision to leave Fivetran. 

Maybe it was the pricing overhaul last year that pushed your invoices into uncomfortable territory. Maybe it’s the limited flexibility for transformation, or the fact that your team spends more time troubleshooting connector issues than doing analytics work.

Whatever brought you here, it’s justified.

A significant number of data teams have been re-evaluating their Fivetran commitment over the past year, and for good reason. But migration is a project that only feels straightforward until it isn’t. Your pipelines are deeply entangled with business-critical reporting and decision-making. One missed table you will find last year’s numbers on your dashboard.

That’s why you need a checklist that provides an actual phase-by-phase plan that accounts for the messy reality of moving production data infrastructure. This guide gives you that.

A word of caution before we begin: don’t rush this. A well-planned migration over six to eight weeks will save you far more time (and stress) than a hurried two-week sprint that leaves you patching things for months afterward.

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. And frankly, the cost of a second migration, in engineer hours, in risk, in sheer organizational exhaustion, means the platform you choose now will shape how reliably your data moves for years to come. Get this wrong, and you’ll find yourself back here in eighteen months reading a different migration checklist.

1. Evaluate Options

The first fork in the road: do you want to build or buy?

If you already have a development team and want full control over your data pipelines, open-source options like Airbyte give you that flexibility, but they come with a maintenance cost. You’re responsible for hosting, scaling, connector updates, and debugging when things break. But that trade-off can make sense for teams with dedicated platform engineers.

If you don’t have a large technical team, or you’d rather your engineers spend their time on analytics, a fully managed platform is the smarter path. 

This is where Hevo Data stands out as a strong Fivetran alternative, and it’s worth explaining why in some detail.

• Pricing that doesn’t require a spreadsheet to understand: This is probably the single biggest reason teams are leaving Fivetran right now. After Fivetran’s March 2025 pricing overhaul, which moved MAR billing from the account level to the connector level and eliminated bulk discounts, many teams saw cost increases of 50–70% overnight. Hevo takes a fundamentally different approach. Its event-based pricing starts at $239/month (Starter plan, 5M events) with predictable, tier-based scaling. There’s no per-connector pricing, no MAR-tier surprises. You know what you’ll pay before the bill arrives.
• Limited Support Responsiveness: Fivetran users often wait days for support to respond during incidents, leading to prolonged data outages, missed SLAs, and loss of trust in the platform. 
• Architecture built for throughput:  Hevo’s platform was rebuilt recently around a microservices-based architecture where ingestion, orchestration, and loading operate as isolated, independently scalable services. The result is up to 20x to 40x faster data replication compared to its earlier architecture, with fault isolation that prevents one misbehaving pipeline from cascading into others. 
• No Multi-Region Flexibility: Fivetran doesn’t offer a workspace model for managing multiple regions; you need separate accounts or destinations for each setup. Hevo, on the other hand, lets teams manage multiple region-specific workspaces under a single account, making organization and oversight much simpler.

Beyond Hevo, you should evaluate any platform against connector coverage for your specific sources, processing modes (batch vs. streaming), total cost of ownership and long-term scalability. A tool that looks cheaper today can become a burden if your data volumes double next year.

2. 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 the detailed Hevo vs Fivetran comparison for a thorough side-by-side breakdown.

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?

We covered Hevo’s strengths earlier in the platform selection phase, but let’s bring it all together, because choosing a migration target is arguably the most consequential decision in this entire process.

The core problem with Fivetran in 2026 isn’t that it’s a bad product. It’s that its pricing model has become difficult to predict, and unpredictable costs erode trust. When Fivetran moved to per-connector MAR pricing, it changed the economics for teams running multiple connectors, especially those with many small or evenly distributed data sources

Hevo addresses this with transparent and event-based pricing. All 150+ connectors are included in every paid plan. But pricing alone doesn’t justify a migration. Here’s what else Hevo brings to the table:

• Support from engineers: Hevo provides 24/7 support staffed by actual engineers who understand data infrastructure. For Business Critical customers, there’s an SLA for one-hour response times. 
• Reliability that’s engineered: Hevo’s 2025–2026 platform evolution rebuilt its core engine around a microservices architecture with isolated pipeline execution. Each pipeline’s ingestion, orchestration, and loading runs independently, so a failure in one pipeline cannot cascade into others.
• Billing transparency that eliminates MAR surprises: The single biggest drawback of Fivetran is its unpredictable Monthly Active Rows (MAR) pricing. It drastically inflates costs for teams running multiple data sources. Hevo solves this by providing a highly transparent, usage-based (event-based) pricing solution. You pay only for the exact volume of events you process.
• Observability that’s actually useful: Hevo provides column-level lineage so you can trace exactly where your data came from and how it was transformed. Pipeline-level monitoring surfaces issues proactively with alerts via email, Slack, or in-app notifications.

And the migration itself? If you’re currently locked into a Fivetran contract, Hevo offers a contract buyout program. It’s worth reaching out to check your eligibility; it can remove the biggest barrier to switching.

Book a demo or start a free 14-day trial.

FAQs