Fivetran Reviews & Product Details

What I like most about Fivetran is its reliability and ease of use, especially for ingesting MongoDB data into Snowflake. The data syncs consistently and accurately, and I have faced very few issues while using it.

The UI is intuitive and user-friendly, making it easy to set up connectors, monitor syncs, and troubleshoot when required. I also particularly like how Fivetran handles MongoDB's nested data and unnesting, which makes working with complex MongoDB structures much simpler.

Another major advantage is the ability to run transformations within Fivetran, allowing us to manage ingestion and transformation workflows in one platform.

From a Pricing / ROI perspective, I find the platform valuable because it significantly reduces the engineering effort required to build and maintain custom ingestion pipelines. The support and onboarding experience has also been helpful when setting up and working with connectors.

Finally, the increasing use of AI and intelligence features is a positive addition, as it helps make data engineering workflows more automated and efficient.

Overall, Fivetran provides a good combination of reliability, usability, automation, and reduced maintenance overhead, which is why it has been a strong choice for our data ingestion and transformation needs. Review collected by and hosted on G2.com.

There are not many major issues from my experience with Fivetran. One area that could be improved is the pricing, especially as data volumes and the number of connectors grow, since the overall cost can increase significantly over time.

For MongoDB, while the ingestion and unnesting capabilities work very well, some advanced configurations and transformation workflows could be made more intuitive and easier to manage directly through the UI. More intelligent recommendations around schema changes, transformation optimization, and monitoring would also make the platform even more useful.

That said, the reliability and the amount of engineering effort it saves largely offset these concerns in my experience. Review collected by and hosted on G2.com.

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.

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.

Every need I have to push data back from my DWH into various tools / locations, Census can do it for me. It allows our business to move at high speed and continue to be fully data focussed and data driven. It's UI/UX is very simple and intuitive so there's no steep learning curve like often is the case with similar tools. Review collected by and hosted on G2.com.

I haven't yet come across anything I dislike. Everything I've wanted to achieve with Census, I've been able to do so far. Review collected by and hosted on G2.com.

EDIT: I spoke with their product success team post-review and they genuinely did try to solve my problem, but they confirmed we were using the product correctly.

For event based systems like ours at Potions (we're an email product, kind of like Mailchimp on auto-pilot) we emit a lot of "small" event data like opens/clicks, and that tends to eat up MARs pretty fast. Better to build the solution in-house in these cases. Fivetran is probably better for lower volume/larger row data.

Their pricing hits an interesting "logarithmic curve" that we seemed to be nearing the peak of. I'd imagine folks with more volume & revenue than us might be fine using it, so I'm bumping my review to a 4. Review collected by and hosted on G2.com.

I still think small company pricing should be a lottttt more generous at our data volume; this has gotta be a rounding error of volume for you guys :( Review collected by and hosted on G2.com.

The fivetran connector library is large and the connectors generally work well. Support is good at proactively troubleshooting connector issues. Review collected by and hosted on G2.com.

Fivetran's pricing is terrible and their billing support doesn't give a shit. They sync rows inconsistently and have no billing controls, so you cannot pause or stop a sync when you hit a budget for a connector. This allows them to rack up thousands in MAR spend in minutes, with no ability to stop it or even respond to very inflexible budget alerts. Sometimes they'll randomly resync a whole table, and charge you thousands for it – including reference tables. When you raise it with billing they sometimes admit fault, but then you wait months to get a credit. Their sales team don't know their pricing, and largely do not care about you if you're not fortune 100. When you sign up you just give Fivetran a blank check and they will charge you as much as possible and do as little as possible. Review collected by and hosted on G2.com.

It's super easy and intuitive. It has a very specific function and it's very good at doing the function. It integrates with all of the systems we use and is easy to get them configured so the initial setup. Review collected by and hosted on G2.com.

Just the price pretty much. Besides that it does everything I would want it to do. Review collected by and hosted on G2.com.

What I like best about Fivetran is its simplicity and ease of use. The platform is incredibly intuitive, making it easy to set up and manage data pipelines without requiring extensive technical knowledge. The wide range of integrations is another major advantage, allowing seamless connections across various data sources and destinations. I also appreciate how Fivetran automates much of the data integration process, reducing manual effort, saving time, and making data management more efficient and reliable. Review collected by and hosted on G2.com.

One area for improvement is the accuracy and completeness of the documentation. While Fivetran is generally intuitive and easy to use, we encountered some inconsistencies in the documentation, particularly when setting up Fivetran Activations with Salesforce. During our proof of concept (POC), we found that certain documented steps were incorrect or did not work as expected. We had to experiment, troubleshoot, and ultimately revise the documentation based on our findings. Having more accurate and up-to-date documentation would help reduce implementation time and improve the overall user experience. Review collected by and hosted on G2.com.

What I appreciate most about Fivetran is that it's a no-code platform. During setup, there's no need to write custom logic for incremental loads, schema drift handling, or ingestion tuning — these are all pre-built and handled out of the box. Post-configuration, the platform is largely self-monitoring: silent failures are eliminated since you're automatically alerted when a source system goes down, and most of the pipeline runs without manual intervention. It also offers ready-made connectors for the majority of widely-used software tools, which significantly reduces integration overhead. Review collected by and hosted on G2.com.

Pricing can become a significant concern, especially for high-volume source systems like Oracle, since Fivetran's consumption-based model (tied to monthly active rows) scales quickly as record counts grow — costs can feel disproportionate for large, transaction-heavy databases. Review collected by and hosted on G2.com.

I use Fivetran regularly to manage our data integrations, especially connecting Google Sheets with our warehouse. One major issue we had was operations staff deleting sheet rows; Fivetran solved this instantly by automatically providing the _fivetran_deleted boolean column, giving us full historical visibility.

The UI/UX is intuitive and clean, making setup completely no-code. In terms of performance, syncs are fast, reliable, and handle automatic schema changes effortlessly. The support and onboarding experience was smooth with clear documentation. Overall, the ROI is massive because it saves our engineering team hours of manual pipeline work each week, and the automated metadata scheduling almost feels like having built-in intelligence monitoring our data integrity! Review collected by and hosted on G2.com.

One thing I dislike is how Fivetran handles column ordering during syncs, particularly with Google Sheets connectors. It doesn't preserve the original column order from the source sheet, often reorganizing them alphabetically or dynamically in the destination table. While it doesn't break the data itself, it makes quick manual checks and SQL queries slightly annoying when you expect columns in the same layout as the source. Additionally, the pricing can scale up quickly if you have high-volume tables with frequent updates. Review collected by and hosted on G2.com.

What I like most about Fivetran is how easy it is to use and how little maintenance it needs. The UI is straightforward, and setting up integrations with tools like Salesforce and Marketo is pretty simple. The syncs are generally reliable and perform well, even with large amounts of data. It saves our team a lot of time compared with building and maintaining pipelines ourselves, so the ROI is clear. The documentation and support have also been helpful whenever we run into an issue. I haven’t used many of the AI features yet, but the automated schema handling and monitoring already reduce a lot of manual work. Review collected by and hosted on G2.com.

The biggest downside is the pricing, which can be hard to predict when data volume grows. Most connectors work well, but when a sync fails, the error message is sometimes too technical or not specific enough, so troubleshooting can take longer than expected. Support is helpful, although response times can vary. I’d also like to see smarter monitoring that flags unusual sync behavior and explains the likely cause before it becomes a bigger issue. Review collected by and hosted on G2.com.