G2

Vishal K.

Data Engineer

Mid-Market (51-1000 emp.)

9/17/2026

"A Powerful Platform for Seamless Data Integration"

4/5

What do you like best about Fivetran?

What I like most about Fivetran is how it has simplified our overall data integration workflow. We initially chose Fivetran because we wanted to avoid building and maintaining custom ingestion pipelines for every data source. As our requirements grew, we started using Fivetran across multiple sources, and it has allowed us to manage these integrations through a much more centralized and automated approach.

From a UI/UX perspective, the platform is clean and straightforward to work with. Setting up connectors, selecting schemas and tables, checking sync status, and monitoring the overall health of pipelines is relatively simple. It gives our data engineering team good visibility without requiring us to build separate monitoring solutions.

Integrations are definitely one of the biggest strengths for us. We are working with multiple sources such as ShipHero, Shopify, Gorgias, and other systems, and having pre-built connectors has saved us a significant amount of development and maintenance effort. Instead of developing and maintaining individual API ingestion workflows, we can rely on Fivetran to handle the extraction and synchronization.

In terms of performance and reliability, the automated incremental synchronization and scheduling have been very useful. The alerts around pipeline failures, sync issues, and rescheduling also help us identify problems quickly without having to constantly monitor every pipeline manually.

Another major positive has been the support and onboarding experience. We have faced some fairly complex connector and data-related issues, particularly with ShipHero, including schema changes, historical synchronization, permissions, missing fields, and data reconciliation. What impressed me was that the support team didn't simply provide generic troubleshooting steps. They took the time to understand our business requirements, investigate the issues, and coordinate with Engineering and Product where necessary. Some of the issues we raised resulted in connector improvements and fixes, which has been very valuable for us.

From a pricing and ROI perspective, Fivetran has helped reduce the engineering effort required to build and maintain integrations. The fact that historical syncs are not charged has also been particularly useful when initially loading and backfilling data. For us, the value comes not only from the data movement itself, but also from the engineering time saved on maintaining custom integrations.

For AI and intelligence, this isn't the primary reason we currently use Fivetran, but I see the broader potential of having reliable, centralized, and continuously synchronized data as an important foundation for downstream analytics and AI-driven data products.

Overall, Fivetran has helped us reduce custom engineering work, improve pipeline visibility, and make our data integration process much easier to manage. Combined with the responsive support team and their willingness to work through complex requirements with us, it has been a valuable part of our data engineering workflow. Review collected by and hosted on G2.com.

What do you dislike about Fivetran?

Fivetran has been very useful for us, but there are a few areas where I think the experience could be improved.

From a UI/UX perspective, the platform is generally easy to use, but when dealing with complex connector issues, it can sometimes be difficult to understand exactly what is happening behind the scenes—for example, why a sync is delayed, what is happening with a historical sync, or when a connector-level change will actually reach the destination.

For integrations, connector completeness is probably the biggest area for improvement for us. We have encountered situations where certain source fields or tables were not initially available, or where permissions and schema selections differed across connections. With our ShipHero integration in particular, we have had to raise several feature requests for additional fields and deal with schema changes. Fivetran Support has been very helpful in addressing these, but ideally these connector capabilities would be available earlier so that we don't have to build workarounds or wait for feature requests.

Performance and reliability have generally been good, but when a source API has throttling or a connector falls behind, it can sometimes be difficult to get a clear ETA because the synchronization depends on the source system as well. Better visibility into sync progress, expected completion time, and the reason for delays would make troubleshooting easier.

Pricing is another area where I think there could be more predictability. The usage-based MAR model is logical, and Fivetran provides usage monitoring, but for teams with rapidly changing data volumes, it can sometimes be difficult to predict exactly how changes in source data or additional connections will affect the final cost.

Support has actually been one of our strongest experiences, so my criticism here is more about the product process than the support team. The Support team has been very responsive, but for some complex feature requests it can take time to get a confirmed status or ETA. Having clearer visibility into feature-request status and planned release timelines would be helpful.

Finally, AI/Intelligence isn't currently a major part of our Fivetran experience. Our primary use case is data movement and integration, so I would like to see more capabilities that help identify data-quality issues, explain sync problems, detect anomalies, or provide more intelligent recommendations around pipelines.

Overall, these are areas where I think Fivetran could improve further. The core product is valuable for us, and the biggest opportunity would be making connectors more complete out of the box, improving visibility into sync and schema changes, and providing more predictable timelines and costs. Review collected by and hosted on G2.com.

What problems is Fivetran solving and how is that benefiting you?

Fivetran is primarily solving the data integration and pipeline maintenance problem for our team. We work with multiple data sources such as ShipHero, Shopify, Gorgias, and other platforms, and building separate custom ingestion pipelines for each source would require significant development and ongoing maintenance.

With Fivetran, we can set up connectors and automate the movement of data into our warehouse, which has reduced the amount of custom API and ingestion code our team needs to build and maintain.

It also helps us with incremental synchronization, scheduling, monitoring, and pipeline alerts. When a pipeline fails or gets rescheduled, we have visibility into it and can take action instead of having to continuously monitor every integration manually.

Another important problem it helps us solve is scaling integrations as our data requirements grow. Rather than creating a completely new ingestion framework whenever we add another source, we can use Fivetran's existing connectors and focus our engineering effort on transformations, data quality, reporting, and business logic.

For us, the biggest benefit is the engineering time saved and the reduced operational overhead. Fivetran allows our team to spend more time building reliable data products and analytics instead of constantly maintaining source-specific ingestion pipelines. Review collected by and hosted on G2.com.