Fivetran Reviews

In my current project, the specific use case of Fivetran is bridging the gap between siloed data, allowing me to extract and have it in the same data warehouse. Fivetran works with its seamless connections to sources and destinations, helping to avoid reinventing extraction logic from scratch. Fivetran already has data models, so it pulls in data quickly, providing it all in the same data warehouse.

I've worked extensively with Fivetran, mainly used for extraction purposes, and I've worked with the transformation element in it as well. Fivetran not only has built-in connectors but also provides SDK connectors, allowing us to develop our own connectors in an easy manner. I don't have to write raw Python scripts or dumping scripts; it offers straightforward examples and guidelines, making it much simpler to develop custom connectors inside Fivetran. We've been able to develop many custom connectors as well, which is unique and beneficial for having everything centralized instead of having those connectors located elsewhere.

One of the best features by Fivetran is its clean, simple, and intuitive UI. It includes a transformation section where I can deploy my DBT queries and scripts. It also supplies good tracking capabilities for billing estimates and user permissions, allowing for customization to the desired level. The number of connectors it has remains a standout feature, and within connectors, the options available are very helpful. Although it sometimes appears static due to its built-in nature, it offers good flexibility for data transformation and caching, which I appreciate because it saves us extensive script-writing time.

The experience of using SDK connectors can be improved, as it lacks computational power, which is an area that needs enhancement. Additionally, for some connections, I want more flexibility during ingestion, specifically for transformations needed beforehand. When we tried to use a built-in Oracle connector, it didn't allow for the tweaks we needed, which led us to the SDK connector route and caused delays in development.

I've been working for almost four years in this field, and it has been a really great learning and challenging opportunity, allowing me to work on various different stacks and to learn and grow extensively.

In my experience, Fivetran is stable with very few instances of downtime or reliability issues, and overall, I am very happy with it.

Fivetran's scalability has been tested effectively, and it has been working well for our organization's growing data needs. However, the performance with SDK connectors could be improved, as it took around 10 to 12 days to conduct historic ingestion for just two years, which I find unsatisfactory.

Customer support from Fivetran is quite good; it's really nice and responsive. I am very happy with my experiences in reaching out to them.

Positive

Before Fivetran, we used Skyvia, which is a no-code ETL tool. I also considered Data Factory as an alternative. However, I found Skyvia lacking in maturity and Data Factory better suited for enterprise solutions but limited in connectors. Scripting solutions demanded more work and included additional costs, which led to my decision to switch to Fivetran.

My experience with pricing, setup costs, and licensing has been satisfactory; however, I notice that as row numbers increase significantly, it can get a bit more expensive. Overall, it's fairly reasonable.

We see fewer employees needed due to Fivetran's efficiency, which translates to money saved, as employee wages are much higher compared to the costs associated with the software itself.

Fivetran provides time savings, cost reductions, and improvements in data quality. For example, in working with Shopify, we faced numerous errors and challenges with in-house complicated connectors. The time directly correlates to cost; when the time investment decreases, fewer developers are needed, which means significant savings. Generally, we have seen very few instances of data quality issues since implementing Fivetran.

Before selecting Fivetran, I evaluated other options including Stitch, Rivery, Hevo, and Skyvia among others.

My advice for those looking to use Fivetran is to rely on its documentation, which is very straightforward and accurate. Avoid depending too much on AI for Fivetran issues; the dedicated documentation suffices for most inquiries. If problems arise, Fivetran's support team is quick to assist. I rate Fivetran 9 out of 10.

The usual use cases for Fivetran that I work with involve real-time replication from Oracle Fusion, Oracle DB, and some of the APIs. I also work with replication from S3.

The most valuable feature of Fivetran so far is the data replication. The real-time data replication is what I see best in the market where it reduces the overhead of customers needing to maintain the pipeline. Even when an error occurs, it automatically tries to solve the issue without manual interaction needed.

The effectiveness of Fivetran's pre-built connectors in enhancing my customer's productivity is significant because everything is easily configurable. The documentation is very clear, so there's no difficulty in finding the necessary steps. Everything is pre-documented. The pre-built connectors that are available are excellent. Most functionalities are covered, and in most cases, it works according to expectations.

If Fivetran can build in more transformations, that would be really helpful in my opinion.

I have been using Fivetran collectively based on project requirements. I work with AWS, GCP, Snowflake, and Fivetran as part of my experience. My experience with Fivetran specifically extends to about two to three years, depending on project requirements.

I have not experienced any challenges with deployment.

From my experience, Fivetran is very reliable. They have 99.9% accuracy on the data load and they maintain transparency. When the data is loaded, they clear it from their storage, keeping only metadata for MAR calculation and pricing purposes.

I often communicate with the technical support of Fivetran whenever we encounter issues. I would rate them a seven. If they could provide support more quickly, that would be great.

Positive

I usually participate in the initial setup and deployment of Fivetran. I handle the solutioning from start to end.

I don't use Fivetran's automated ETL feature, but after loading, we use dbt for the transformation.

I have not used Fivetran's Schema Drift Management feature.

The integration with data warehouses such as Snowflake and BigQuery has been successful. Fivetran has clear documentation on configuration for both warehouses.

Regarding pricing, Fivetran uses MAR pricing, which I would consider moderate. The pricing consideration ultimately depends on the project budget since the client is responsible for payment while I implement the solution.

On a scale of 1-10, I rate Fivetran a 9.

Positive

I work in a company where Fivetran is being used, and we pay the licenses to them. We use it as a customer with a lot of different sources to our databases, such as Google Cloud Storage. Fivetran is used to establish data connections in the pipeline, for example, from SAP S4HANA to Google Cloud Storage.

Fivetran provides a user-friendly interface, which makes data pipelining and connecting the source and the destination easier.

The user interface is mostly UI-based, eliminating the need for a lot of coding. Furthermore, Fivetran offers scheduled pipeline extraction up to twelve times in a day.

There is not a lot of options to customize the pipeline, and the cost is high. Additionally, conducting extractions with transformations, like removing null values or doing simple calculations, is difficult. Vendor support is also slow to respond.

I have been using it for a year.

The solution is not stable due to issues with vendor support when problems occur.

Vendor support is not very good, and it takes time to reach out.

Positive

The vendor handles installations on our PCs, and we receive updates either monthly or half-yearly.

The vendor's partners are involved in updates being held on a scheduled basis.

The cost is high for using Fivetran.

The response sense is not very high, so it's a problem. Pricing is an expensive solution.

I'd rate the solution ten out of ten.

We have implemented Fivetran at client sites to connect to various platforms like Dynamics 365 and SharePoint. My objective was to determine which solution was better for handling geospatial data.

It has been beneficial in quickly getting data ready for AI or machine learning projects due to its easy connectors, which expedite data availability.

The ease of setting up the connectors and transformations is highly valuable. We used the SaaS version of Fivetran to connect to Dynamics 365, and it was great and easy, although we couldn't filter data on it.

I would like to see an improvement in the support offered by Fivetran. Their support is very limited, and even when they respond, they often can't help. The ability to handle geospatial data types would also be beneficial, as well as the ability to filter data in the connectors.

I have used it for about a year across different sites and for various purposes, using different connectors.

Fivetran's automatic schema migrations have been perfect, without any issues.

Their support is limited. When you do get a hold of someone, they often cannot provide much assistance.

Positive

The pricing for small corporations, particularly when using connectors such as those for SharePoint, is not affordable. For large corporations doing change data capture, it is very affordable.

For geospatial data, I would recommend using other solutions like FME. A simple Azure Data Factory setup can handle filtering, which some connectors don’t facilitate.

I would recommend Fivetran based on specific use cases. Its ease of use and minimal setup requirements make it one of the easiest solutions, although it might not be suitable for all cases.

I'd rate the solution seven out of ten.

Negative

At our company, we are using Fivetran for change data capture and to sync between two different databases using the high volume replication feature. 

Fivetran offers native connectivity with Sybase, which is highly valuable. The solution also allows real-time syncing and especially between Sybase and Snowflake, which is the solution's use case in our organization. Fivetran provides seamless integration without any data loss, and data changes are reflected in real time. Due to the change data capture capability, Fivetran allows the seamless data flow from one way to another.  

Fivetran has multiple offerings and I have majorly worked with the high volume replication feature. An in-line data quality checking capability is missing in the solution. Whatever data gets synced or converted from one format to another as part of an ETL task using Fivetran, a data quality check cannot be performed. 

As part of the data observation, when the data flows from the source to the target, there isn't a single interface provided by Fivetran to observe the data flow process, check for issues, and implement a comparison with historical transformation. The data observability could also be better in the product in terms of the gaps present. 

I have been using Fivetran for nearly two years. 

The solution offers satisfying stability. 

Fivetran is reasonably scalable and easy to use and I haven't faced any challenges with scaling.  

The customer support from Fivetran is satisfying. I would rate customer support an eight out of ten. 

Positive

The on-prem deployment of Fivetran is a little bit complex, but the on-cloud deployment through the vendor's SaaS offering is comparatively easier and simpler. Just a couple of professionals are required to deploy Fivetran. 

I would rate the pricing a seven out of ten. The product is reasonably expensive. 

The solution provides a satisfying user experience by utilizing its automation capabilities. I would rate the product a seven out of ten. 

We have a number of different data sources that we collect information in from; for example, Salesforce for sales, Marketo for marketing, NetSuite for finance. We wanted to centralize all that data into a data warehouse, which is why we use Fivetran to manage all of those data pipelines.

Salesforce is our primary data source for financial, sales, and go-to-market reporting. Fivetran allowed me to pull Salesforce data into our centralized data warehouse and build reporting and dashboarding and analytics on top of it without needing to hire a data engineer to build or maintain that pipeline. This saved us about $200,000 a year in full-time headcount cost and I was able to get Salesforce data into our data warehouse in about a day, as opposed to weeks or maybe months if we had to hire someone. Fivetran reduced our time-to-value by enabling us to use that data in the first place.

The Fivetran features I have found the most valuable are the managed pipelines themselves. It is very valuable for us to be able to set up a connection to a business application and have that pipeline managed by Fivetran and synced to our data warehouse. The Fivetran logs, which allow me to see if there's anything happening in the pipelines that I need to be concerned about or why a connector is not connecting, for example. 

We also use Fivetran data libraries. They have an integration with another tool called DBT, which allows us to run code in DBT that automatically transforms our raw Fivetran data into useful tables and views. Fivetran data library packages have been very useful for us.

Fivetran has room for improvement in data pipeline observability. The Fivetran logs are fairly basic, compared to, for example, the insight Fivetran gives into helping users understanding the performance of data pipelines. So I think their observability into the pipeline itself could be improved.

In addition, Fivetran is in the very early stages of allowing other companies to access its metadata API, but that's something that could use improvement, and I know that they're working on right now.

We use a separate tool for "reverse ETL", which is the opposite of what Fivetran does; it pushes data from your data warehouse back out to business applications. If Fivetran pulls data from those same applications, they should also enable users to push it back. I would love to do both ETL and reverse ETL in the same tool. It would be nice if Fivetran offered both their regular offering plus the reverse ETL option as well.

Fivetran is stable. I have never had an issue with it. 

Whenever a pipeline goes down or there's an issue with it, I get an email about itand the issue is always been resolved within the same day.

Fivetran is scalable. It can get expensive if you're sending a lot of data through Fivetran, but it has scaled well within our company. We have not had any issues with scaling and Fivetran continues to add connectors that we make use of. For us, Fivetran has been able to scale both in terms of the data we bring into our warehouse and the amount of data that we use as well. 

We have one direct user of Fivetran, 250 indirect users.

I'm the only direct user of Fivetran, but everyone else at the company who uses data that's managed by Fivetran is an indirect users.

We just expanded our use of Fivetran last week. We added NetSuite last week. Our increase in usage will depend on new connectors that Fivetran releases over time.

Fivetran's technical support is timely and efficient. 

Positive

The initial setup was very straightforward. It took some 20 minutes.

Our deployment was done in-house. I did it. Easy deployment is one of the best things about Fivetran. 

We have absolutely seen a high return on investment. We would have to hire multiple data engineers to do what Fivetran does and each would cost us about $200,000 a year.

Wee pay between $20,000 and $30,000 a year for Fivetran. 

Five or six years ago, when I first was introduced to Fivetran, we compared it against Matillion and Alooma, and Fivetran won that proof of concept. I've been a Fivetran user ever since. 

In my most recent company, I didn't have a need to compare it against other solutions because – from previous experience – it works, it does exactly what I need it to do, and is an extremely high ROI for us.

Fivetran is very easy to set up and get going, so there's no real need to put it off because of technical requirements or it not being the right time. My first piece of advice is: the faster that you can get it implemented, the better.

My second piece of advice is: Fivetran is going to sync raw data to your warehouse, so there is going to be additional work that you have to do to model your data to make it useful for reporting, dashboarding, or model building. Just because Fivetran is loading thinking data to your warehouse does not mean that your data work is done. It is almost just the beginning of what you need to do. Once data lands in your warehouse, you are going to have to do some modeling on top of it to make it useful.

Our company uses the solution for the extraction part of data warehousing and ETL or ELT processing. Once we extract, we use Snowflake to database as soon as possible. Later on, we process the data. 

We currently have five to ten users with no significant growth plans. We recently added one new team member and he is already accessing the solution. 

It is extremely easy to add a new integration and connect it with a destination in a very short period of time. This is particularly useful for multiple servers or databases.

The portal is very intuitive and easy to use. New users can easily navigate, complete setups, and troubleshoot.

More connectors are needed for exotic, popular, and rising star portals. Creating a comprehensive, one-stop shop for bringing data as quickly as possible would improve the solution. If I check a list of data sources, only 10% of new providers are supported by the solution. Most companies want various data sources for processing down the road. We use the solution as a primary source so we have to decide if it will work for a use case or if we will need to create a custom integration.

Customer support should be offered via a portal system instead of the current chat platform. In a portal system, it is fairly easy to look back and see the history of your discussion. With a chat platform, once you close a session you have to open a new one. The history is lost and you don't have a record of who you communicated with to receive that advice. For example, I might chat with support today about an issue that recurs tomorrow. My colleague catches up with me to continue work on the issue but I can't share the chat history for further discussion. Chat platforms are not a collaborative tool. A portal system is an elegant way to ensure two-way communication. 

I have been using the solution for eighteen months. 

The solution is very, very stable. The infrastructure is doing a great job because 99.99% of the time it is up and running. Since I've been using the solution, there have been no major outages. The availability is excellent. 

The solution self-recovers or cures any minor issues very quickly. 

The solution is scalable. 

We are not the biggest company so we do not have a high data demand. We are growing rapidly but for our use cases and scale, the solution is more than sufficient. 

It is more about how you stay cost effective than how the solution scales. 

I have contacted support via chat several times for assistance when integrations are down or there are problems on the source side. Logs are very descriptive and I get the details needed to determine there is a problem. I use support to confirm my next steps. 

Support provides good communication and my experiences have been good. They don't copy and paste guidelines but rather live chat with me about my particular use case. 

I rate support an eight out of ten. 

Positive

The solution is a typical SaaS product so setup is fairly easy. You get a web URL, login credentials, and you are in. There are baby steps and good guidelines for getting up and running. I can't say setup is different than any other kind of tool for the same purpose, but the solution does the heavy lifting here. 

I am a tech geek with a lot of senior level experience and I find the setup to be intuitive and easy. It might be a bit more difficult for new users who are not heavily involved in the data world. 

I rate setup a ten out of ten. 

We implemented the solution in-house. 

The pricing model is okay and mid to large companies will not have an issue with it. 

The pricing is good for small companies but there is an upfront cost. The solution does not offer a free tier. Smaller companies with tight budgets might want to opt for an open-source product or one that offers a free tier.

The solution is our first weapon of choice. We bring data in and have a process for evaluating which tools match a particular use case. For example, a well-known source like Facebook ads has a connector for the solution, so we use it. If a use case is more exotic and we can't find a connector, then we create something or search for an open-source option. 

The solution works well for mid to large sized businesses and enterprises that need a good, reliable partner. The only possible problem is with a business that wants to keep data inside their own environment. Compliances and other things sometimes require you to send data to the solution. 

The solution is good for small companies but budgets might preclude use. There are open-source options that use similar logic to the solution. 

The difference between the solution and other products is its focus on extraction. Other items such as formation are included but the main focus remains on extraction. Whereas Data Factory or Hevo have extraction but put a spotlight on transformation. 

It is important to choose the product that best matches your use case. 

Before choosing the solution, be sure to do your research about the cost. Just like with any other SaaS tool, your costs can explode. Do a deep dive into the pricing model and speak with support about it. Determine the cost of your operation, both per record and generally speaking. If you have plans to scale your business ten times, then you will have ten times the data and that will affect your budget. If you are a data-driven organization, then you might end up with a hundred times the data. Decide if the cost will stay efficient or if it will cause serious budget issues. It is vital to stay compliant and remain cost efficient. 

Another important factor is being able to trust your partner. You want a reliable and stable partner who offers you good support. 

The solution is shoot and forget. We set up integrations and the solution does all the heavy lifting. We pay for the service and everyone is happy. 

I rate the solution a nine out of ten. 

For this project, we are using both Fivetran and HVR; however, HVR has a limited number of connections, with only around 30 sources, whereas Fivetran has around 200 source connections. Therefore, if we are unable to connect with HVR, we are connecting with Fivetran.

Fivetran is a SaaS solution, so it is always on a public cloud.

Fivetran is remarkably easy to use; I haven't encountered any other data integration tool that is as user-friendly.

Fivetran has a lot of connectors and can currently connect to more than 200 sources. However, occasionally we come across a source that Fivetran does not currently connect to. We can request them to make a connector, but this can take some time. In the meantime, if we want to read through that API or add a custom connection, we cannot do it on Fivetran itself. We must use AWS Lambda and integrate it with our Fivetran account, and this must be done through the public cloud. The number of connectors has room for improvement.

I would like Fivetran to implement additional resource monitoring and restriction policies. I don't want to spend more than 100 or 1000 credits per week or have restrictions based on data sources. If a data source suddenly produces a large amount of data, I could manually pause the credits, but it would be very helpful if I could set policies like this.

I have been using the solution for almost one and a half years.

The solution is stable and I have not encountered any issues.

Fivetran is a scalable product that operates on a pay-as-you-go model, so we must be mindful of the potential for building a connector to it. I have not encountered this issue personally, but I am aware of people in my organization whose clients have experienced similar problems due to its scalability. Currently, we are migrating around 1000 rows per week. If someone were to run a full day, we might migrate 10,000 rows, which could consume up to 5000 credits for Fivetran in a week. I am uncertain of the dollar value of these credits, but it was estimated that they would last two months, yet they were used up in a week. Additionally, Fivetran does not have a resource monitoring feature, so we can monitor how much it has charged us, but we cannot set a limit, such as $2,000, to stop the usage.

We have contacted the Fivetran team for HVR support since HVR is supported by Fivetran, and they have been very responsive.

The initial setup is the easiest of all the data integration solutions. We don't need to do much; I have only done it with Snowflake and Databricks. We just need to set up a target or destination or protocol destination, which is really easy. On the left side, we have all the information we need to provide to Fivetran. On the right side, they have step-by-step documentation or procedures on how to find this or how to do that, even for connecting to data sources. Fivetran always gives us step-by-step instructions on how to do this, and Fivetran usually has fewer than 15 steps.

The implementation was completed in-house.

I give the solution a nine out of ten.

Fivetran is the most user-friendly data integration tool I have encountered. I have not needed any assistance from the Fivetran team, nor do I know anyone who has.

If we have data coming from multiple sources that need to be connected, such as our CRM, Salesforce, ERP, and SAP systems, it would take a lot of time to write programs for each one. To save time and allow our data engineers to focus on more useful tasks, it is better to use Fivetran. However, we should be mindful of the cost, as Fivetran uses a pay-as-you-go model. It is best to connect with the Fivetran team rather than setting up the connection directly. They may offer free data migration for a source for up to two weeks, depending on our negotiations. This can be very useful for the initial sync and can save a lot of costs.