12 Things to Keep in Mind When You Migrate From Fivetran in 2026

Key takeaways

This guide covers 12 critical factors to consider for a smooth and future-proof Fivetran migration in 2026:

• Audit your data stack before migrating from Fivetran
• Watch for cost spikes, rigid schemas, and limited transformations
• Choose a platform aligned with your team, budget, and scale
• Use staged rollouts, backups, and parallel pipelines
• Document schemas and transformation changes clearly
• Validate data with row counts, checksums, and logic tests
• Plan cutover during low traffic and define rollback steps
• Review pricing models and future cost scaling
• Check key connectors like NetSuite, Shopify, and HubSpot
• Include security and compliance from the start
• Train your team and assign clear ownership

Monitor performance and retire Fivetran after stability

Many teams turn to Fivetran for its simplicity. It promises a quick setup, automated pipelines, and less engineering overhead. Initially, it all works well.

Then the cracks begin to show. According to a 2025 analysis, Reddit users and enterprise data teams reported cost spikes of 80–120% after Fivetran eliminated account-wide MAR aggregation in March 2025, applying discounts only at the individual connector level. One enterprise team saw its monthly bill jump from $14,000 to $31,000. 

Transformation options begin to feel restrictive, and teams that need real-time streaming or SQL-heavy workflows find themselves reaching for workarounds. That’s when migration becomes the conversation.

But migrating away from Fivetran is not as simple as swapping one tool for another. It is a strategic shift that affects budgets, operations, team workflows, and long-term data reliability. A rushed or under-planned move can mean downtime, data loss, and broken dashboards.

Before you begin, it’s worth reviewing common Fivetran pitfalls and Fivetran FAQs to understand where most migrations go wrong and how to avoid those traps.

This guide covers the 12 key things to keep in mind when you migrate away from Fivetran, so your transition is smooth, cost-effective, and future-proof.

Customer review:

What do you like best about Fivetran? Fivetran truly simplifies the process of connecting and syncing data across systems. Its out-of-the-box connectors are very robust, requiring minimal setup and almost no ongoing maintenance. I especially appreciate the automated schema management and the reliability of the pipelines — once configured, they just work without needing babysitting. What do you dislike about Fivetran? The pricing model can become expensive at scale, especially when dealing with large volumes of data or many connectors. Also, while the platform supports a wide variety of connectors, customization beyond standard transformations sometimes feels limited without moving into external tools like dbt.

Verified User

12 Key Considerations When Migrating Away From Fivetran

Data migration success depends on thorough planning. Here are the 12 key considerations to guide your move.

1. Assess your current data architecture and requirements

Before moving away from Fivetran, take a step back and map out your entire setup. Start with your data sources. List every SaaS tool, database, event stream, and file system that feeds into your pipelines. Identify who owns each source, the data volume you handle, and how you access it. This will tell you which integrations may require extra attention.

Don’t forget the destinations. Note whether you use a warehouse like Snowflake or BigQuery, a lake like S3, or a mix of both. Record sync frequencies, latency requirements, and the costs tied to storage or compute.

Finally, check your ETL or ELT processes. Document where transformations run, from dbt models to warehouse SQL. Include orchestration tools, compliance needs, and business-critical dashboards. If you are unsure what capabilities you’ve been relying on, a quick look at the Fivetran review can help you benchmark what you have before deciding what to replace it with.

2. Choose the right alternative platform

There can’t be one perfect data integration tool for all teams. The right Fivetran alternative tends to your specific requirements and struggles.

If you want open-source flexibility and cloud data pipeline automation in one tool, Airbyte is worth considering. It offers over 600 connectors with active community support. It is an ideal choice for teams that already have a development team since it requires more technical knowledge.

Similarly, Estuary is known for its real-time sync capabilities. It has more than 200 connectors and delivers sub-second change data capture with exactly-once guarantees. It is ideal for streaming or high-frequency updates.

If you want SQL-focused heavy transformations, Matillion is one of the strongest contenders. It integrates deeply with 150+ connectors. However, its consumption-based pricing can become expensive for heavy workloads.

Hevo combines the pricing predictability, simplicity, and transformation flexibility. It offers a no-code setup with over 150 connectors, real-time and batch sync, and transparent pricing. This balance of usability and cost control makes it compelling for growing companies.

Want to compare two top platforms side by side? Check out our detailed Hevo vs Fivetran comparison article.

3. Plan for data continuity and backup strategy

Data loss during migration can disrupt months of analysis. Ensure reliable backups across all pipelines and destinations. Cloud snapshots, warehouse exports, and versioned storage are a reliable safety net for unexpected pipeline issues.

Begin with less critical connectors to test how the new data migration tool handles syncs and transformations. This small-scale rollout will help you identify issues without risking business-critical data.

Once the initial phase runs without errors, expand gradually to higher-priority sources. Design an overlap period where both platforms run simultaneously, and document clear recovery steps in case of failure. If you encounter sync delays during this parallel period, prioritize troubleshooting Fivetran sync delays and resolve them before cutover to avoid carrying issues into the new environment. 

4. Handle schema mapping and transformation challenges

Fivetran applies its own conventions, such as prefixing Salesforce tables with ‘sf_’ and standardizing field names. These schema differences can cause major disruptions if left unchecked.

The platform also handles data types differently. For example, JSON structures might be flattened in Fivetran but preserved elsewhere. These shifts affect transformations, dbt models, and downstream reporting.

You can manage this by documenting the table and field mapping before cutover. Include business context, not just technical names. Back it up with automated tests for schema validation so inconsistencies surface before they affect production.

5. Develop a comprehensive testing strategy

While you run both pipelines in parallel, compare their outputs in a controlled environment. This prevents testing from interfering with production. Confirm accuracy by checking row counts, checksums, and business rules across the two platforms.

Test how the new setup handles stress. Simulate data spikes, concurrent sync jobs, and API rate limits to identify bottlenecks. Run load tests against your data warehouse to ensure it can handle the processing volume.

Automate reconciliation wherever possible. Add SQL-based checks for row parity, schema consistency, and null constraints. Schedule regular reports and set up alerts for a well-monitored cutove

6. Plan your cutover strategy carefully

A well-timed cutover can be the difference between a well-designed migration and a data disruption. Instead of cutting off all connectors at once, do it gradually in phases. Start small with less critical connectors like development databases or backup systems. Use these early wins to refine your process before moving to revenue-critical pipelines.

Schedule migrations during low-traffic windows to reduce the impact on business users.  Weekends or scheduled maintenance windows often give you extra recovery time. Establish rollback procedures if data quality issues arise. Monitor performance closely for the first 48 to 72 hours of migration and assign a team to handle urgent fixes.

7. Address cost optimization and pricing models

Understanding the Fivetran pricing model is essential before you can evaluate alternatives fairly. Fivetran’s costs can surge unpredictably, especially since Monthly Active Rows are now calculated per connection rather than across an account. This structural change has been the primary driver of unexpected billing for teams with 5+ connectors.

Compare connector-based billing and volume or event models to decide which fits your roadmap and workloads. Estimate how costs scale as your data volume grows. Include subscription fees, implementation costs, training, and running parallel pipelines during migration when both tools are live.

Negotiate terms that work within your budget. Consider asking the new vendor for usage caps, predictable escalation, or committed-use discounts on annual contracts. Look for opportunities to combine batch loading for historical data with real-time syncs only where business impact demands it.

8. Prepare for connector migration and validation

Critical connections, such as NetSuite, Shopify, HubSpot, and Klaviyo, require special focus since they directly affect revenue and operations. Confirm that the new tool fully supports these integrations and matches the functionality you rely on currently. Some tools may not cover every field, transformation, or webhook, so be sure to document the gaps.

Re-create authentication configurations with secure handling of API keys, OAuth tokens, and credentials. Test data completeness by comparing record counts, custom fields, and freshness across systems.

You might need to rewrite scripts for custom processes that previously depended on Fivetran’s API to maintain workflow integrity. Keep a reference guide of connector-specific differences to ensure data pipeline automation continues uninterrupted as your infrastructure scales.

9. Plan team training and change management

A migration is only successful if your team can work confidently with the new platform. Identify the skill gaps between your current setup and the new tool. If you are moving to a more complex ETL platform, focus training on areas where your team needs the most support.

Prioritize hands-on training with real company data instead of general examples. Create environments that mirror production so your team can practice adding connectors, resolving errors, and running checks without risk.

Assign owners to each task and set timelines to avoid confusion. Productivity may dip during the transition, so factor in learning curves in your timeline. Support the team with clear documentation, reference guides, and experts or vendor contacts who can step in when needed.

10. Implement post-migration monitoring and optimization

Finishing the migration is only the first step. The real value comes from how well you monitor and improve the new platform over time. Dashboards allow you to monitor pipeline reliability, sync performance, data freshness, and cost metrics.

Ensure data quality with automated checks and business rules, and compare outcomes against pre-migration benchmarks for consistency and accuracy. Set up alerting thresholds for critical issues and establish clear escalation procedures.

But don’t retire Fivetran just yet. Keep it running in parallel for at least two to three weeks until you have confidence in the new setup. Use monitoring insights to identify optimization opportunities, such as adjusting sync frequencies, consolidating similar connectors, or implementing efficient transformation logic for ongoing cost and performance improvements.

All these best practices will stabilize your migration and lay the foundation for a modern data architecture that scales with your business.

11. Build security and compliance into the migration plan

Security and compliance considerations are often treated as an afterthought during migrations, but they should be part of the plan from day one. If your organization operates under GDPR, HIPAA, SOC 2, or other regulatory frameworks, you need to verify that your new platform meets those standards before any data moves.

Audit the new platform’s data encryption practices both in transit and at rest. Confirm that audit trails and access controls are equivalent to or better than what you had with Fivetran. If your organization handles sensitive healthcare, financial, or personal data, ensure that the replacement platform carries the relevant certifications.

Review how the new tool handles credential storage, API token rotation, and role-based access controls. Define a clear policy for credential migration and ensure that no credentials are hard-coded or exposed during the switchover. If the Fivetran support team has helped you maintain compliance settings in the past, document those configurations before decommissioning.

12. Handle historical data backfills and CDC transitions carefully

One of the most technically demanding parts of any Fivetran migration is managing the transition from Fivetran’s managed sync state to your new platform’s ingestion model. Fivetran’s incremental sync checkpoints cannot be exported, which means your new platform will need to re-establish its own sync baseline.

For large historical tables, a full backfill can take hours or days. Plan for this by using partitioned extracts, incremental snapshots, or direct database exports where possible. If your new system uses Change Data Capture (CDC), determine where in the log to begin reading. Starting at the wrong offset can lead to missing events, duplicated records, or inconsistencies that are difficult to detect later.

High-churn tables, those with frequent updates, deletes, or soft delete patterns, require particular attention. Tables without primary keys or with composite key structures also need careful planning to avoid discrepancies during the handoff. Review how Fivetran manages sync state behind the scenes to anticipate which parts of your pipeline will need manual reconstruction.

All of these best practices will stabilize your migration and lay the foundation for a modern data architecture that scales with your business.

Case Study: How SalesRabbit Migrated from Fivetran to Hevo in Under a Month

SalesRabbit, a leading field sales enablement platform, switched from Fivetran after Fivetran’s pricing nearly doubled within a year, making it unsustainable for a lean team operating across multiple MySQL instances with over 2,000 schemas.

After evaluating alternatives, they chose Hevo for its multi-instance pipeline support, SOC 2 compliance, predictable pricing, and Schema Mapper feature, which allowed them to merge tables before loading to the destination, something Fivetran couldn’t offer.

“The Hevo support is pretty incredible. They help a lot with all kinds of problems and questions, and they seem to have huge value for my time. Dealing with questions or issues in Fivetran was very frustrating. In Hevo, the responses are lightning fast and very supportive.

Giovanna Damasceno, Data Engineer, SalesRabbit”

Case study

The migration was completed in under 30 days. Today, finance, customer success, and product teams at SalesRabbit independently access real-time dashboards, powered entirely by Hevo pipelines.

Why Consider Hevo Data as Your Fivetran Alternative?

Hevo Data is a fully managed, no-code ELT platform that makes data movement simple, reliable, and transparent. It connects over 150+ sources to leading data warehouses in minutes, with no engineering effort or ongoing maintenance required.

Where Fivetran surprises you with unpredictable MAR-based billing and rigid transformation options, Hevo gives you:

• Simple setup: A guided, no-code interface that anyone on your team can use from day one.
• Reliable pipelines: Auto-healing architecture, intelligent retries, and automatic schema handling keep data flowing even when sources fail.
• Full transparency:  Real-time dashboards, detailed logs, and data lineage views so you always know what’s happening across every pipeline.
• Predictable pricing:  Event-based billing starting at $239/month. No hidden fees, no surprise overages.
• Effortless scale: Hevo automatically handles growing data volumes and high-throughput workloads without downtime or manual tuning.

Hevo also supports Python scripting, dbt models, and GUI-based transformations for advanced workflows, backed by HIPAA compliance, SOC 2 certification, and dedicated support.

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

FAQs

Rajashree Bhat Associate Director, New Initiatives

Rajashree Bhat is Director of GTM & CS Ops at Hevo Data with over 8 years of experience at the company. She was one of the earliest architects of Hevo's content and marketing function, building it from the ground up before moving into growth strategy, global sales acceleration, and go-to-market operations. Her unique blend of technical understanding of data pipelines and integrations, combined with deep expertise in data-driven marketing and growth, gives her a rare dual perspective across both the product and business sides of the data industry.