Fivetran Reviews

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. 

Real-time streaming and SAP are the primary use cases of the solution.

The simplicity of the solution is its valuable feature.

The solution is very expensive. I would like to have a better integration of the solution with Azure.

I have been using the solution for one year.

It is a stable solution.

It is a scalable solution that is recommended for only enterprise companies because small and medium companies do not have the expertise and money to run Fivetran.

The technical support is good.

Previously, we used ADA tester and the solution is easy to implement with Azure Cloud. It is easier than Fivetran. Fivetran on the other hand is good for real time.

The solution is not easy to setup for a startup and SME but if an enterprise wants to do everything hands-on, it's okay for them. The deployment takes a couple of weeks to complete. Two people are needed for implementation.

The licensing costs are extremely high for the usage of somebody who has one GB or two GB of usage per day for real-time traffic. There are many other players in the market which are similarly priced or competitively priced. On average per month, it used to come around 12,000-15,000 USD, which is very high.

I rate the overall solution a seven out of ten.

This solution was previously called HVR, which stands for "high-volume replicator." It is now part of Fivetran. It is useful when you need to migrate a large volume of data. We use it for massive data migrations from 30 or 40 sources, including the file system database and some transitional systems. 

In our case, we had historical and active data. Everything was in on-premise Oracle databases. The historical data itself was around 4.5 terabytes. Fivetran is currently deployed in an AWS EC2 instance.

Fivetran can perform data migration incredibly fast, depending on the source and target. 

The documentation is decent, but it's hard to find information online about Fivetran. For example, if you try to search for an error code, you won't find much information about it in forums. 

The CI/CD capabilities aren't user-friendly. It's easy for simple tasks, but sometimes I get error codes that aren't well-defined in the system or the documentation. 

We also experience some lag when migrating between two database sources. Data transformation is expensive, so it's segregated across multiple accounts. It would be helpful if we could unify those into a single transformation component or integrate the solution with other transformation tools. It was built for data replication, not transformation. 

We have been using Fivetran for the last five or six months. 

I rate Fivetran nine out of 10. The only problems we've faced were due to network issues, but there were some bugs in the previous version. 

Fivetran can handle large amounts of data. 

I rate Fivetran support nine out of 10. They've been excellent so far. Some of my team is in the United States because our organization has partnered with Fivetran. They respond quickly when we raise a ticket. I have no complaints. 

It comes down to cost and volume. I don't think other tools are as cost-effective as Azure if we're dealing with vast amounts of data. However, Fivetran or other tools make more sense if we're dealing with less data because they're easier to use. We did POCs for Matillion and Fivetran. Migrating more than 4 or 5 terabytes of data from on-prem to Snowflake cloud would have cost a lot.

I wasn't involved with setting up Fivetran, but I'm told it was straightforward. You have to set up a hub and then install it. If you have an on-prem database, it's better to install an agent. That will help with encryption, data transfer, compression, and other things. You can do it without an agent, but the agent will help. We have agents installed on all our data sources. Updates are also easy. We only need to stop the instance while installing the newest version and restart it. 

We started an annual subscription for HVR, and Fivetran acquired it in March last year. They have the same pricing model based on monthly active rows of data migrated. 

I rate Fivetran eight out of 10. I would recommend Fivetran depending on the volume of data. If you only need to migrate a few gigabytes, it might be too costly for you. It's better to go with other tools in those cases. 

If you need to work with large volumes of data, go with HVR or Fivetran. It's easy to set up, but getting a trial version is difficult. You need to talk to Fivetran support for a demo. There isn't a free version on their website that you can install. If you contact Fivetran or a Snowflake partner, you can get a two-week trial.

We use FiveTran to do data integration and pipelines into data warehouses for analytics. We do the typical use case of bringing in from our internal systems, data syncing, change data capture, as well as bringing from third-party systems, like Salesforce, NetSuite, and even Google Analytics and web platforms. We use the solution primarily in an ELT framework. 

The main benefit is just being able to onboard new sources of data. One of the things that we did was create a staging database in FDLC. We set up a new connection, and a new source in destination. We're able to sync and set up a one hundred gigabyte database from PostgreSQL to Redshift, a completely new implementation that's subsequent to the initial one, within two days. We are able to completely replicate an entire staging environment within a two-day timeframe.

There's the general feature of the platform where it just makes it very easy to integrate different things, but I would say a specific difference is their integration of DBT, being able to have the transformation components be driven by Fivetran.

One of the traditional issues with the platform has been logging. The logging, while they have it, is not particularly verbose, so when there are issues it becomes hard to do. They also have internal logs versus customer-facing logs. We've asked FiveTran to provide more exposure on that or to be able to subscribe to it via an API or Datadog or something like that to pull from their system. 

Another thing is mainly their breadth of being able to pull from different systems. They have some of this already, but they're pushing to do some integrations with Excel online. Some of the pain points we're looking at are trying to integrate some of the items in the Microsoft stack, so SharePoint and Excel, and then some of the newer Azure services.

The solution is mostly stable. For the breadth and number of connections, it's okay. The thing sometimes with Fivetran is that they'll have random outages for some functions. I have had a couple of cases where there were some critical errors that have taken too long to fix. One issue was that stripe data was not sinking correctly, and it took over two months to get resolution on that. 

When the solution is working, it works well. When it breaks, it is very difficult to troubleshoot and fix because it becomes almost like you have an in-house ETL process that you have somebody outsourced trying to fix. Plus, they're trying to fix it for multiple customers at the same time, and sometimes that can be competing. 

I would say the solution is mostly stable 98% of the time, but the 2% that it isn't, there are usually critical issues.

The solution is very scalable. In terms of the breadth of connections and things like that, it's definitely there. In terms of volumes, they're not necessarily in charge of the platforms themselves. For instance, Fivetran doesn't control the speed of our databases, but as long as it's working in concert with customer systems, it can work well. I think there's just some work that needs to be done in terms of tuning those capabilities so that it remains consistently scalable. For instance, when we're doing syncing on PostgreSQL and things like that, there's certain features and flags that you can use to make the process faster, so there's some coordination there. Other than that, once it's set up, it's usually pretty good.

On a daily basis, we have four or five people using it in the business intelligence and analytics area. The SRE team uses it, and I think sometimes software engineering uses it if they want to ingest data from other systems. Also, our business intelligence engineers and the site reliability engineer, plus data engineers use it as well.

The solution does require maintenance right now. Sometimes there will be alerts that come up in the system if you have schema drift or something like that. Usually, the business intelligence engineers manage that.

We use the solution as our primary ingest for all the data into warehouse. We're looking to expand it. We're on Redshift, but we have another company that uses Azure and the SQL server and Synapse, so we're planning on expanding use there as well.

Earlier on, there were growing pains with the tech support. Early on, when we had to engage with tech support, it was usually for more critical issues, so to me, that's almost like a four out of ten. Especially if we had business breaking issues like a severity one, those probably didn't get as much attention as we needed them to get. 

It has gotten a little bit better. I've heard they've reorganized some of their processes and handoffs because they try to do twenty-four by seven, so they have handoffs to different regions and are trying to do better with them. I think it's improving, but I haven't had to use them recently. 

Step one was connecting to them, then opening up ports to our cloud, verifying connections, connecting to our different databases from source destinations, testing, and implementing. Obviously, with the initial onboarding there's also security and things like that. 

The initial deployment was fairly small, so it didn't take a particularly long time, maybe a week on and off, in terms of just working with the team and opening ports and connecting. We're on AWS, so some of it was on our side having to do IM rolls and whitelisting.

From a day-to-day perspective of onboarding new ones, it's really just pointing towards sources, then the destinations, and then just doing verifications. Day-to-day is pretty easy. 

Our deployment was handled in-house by two people. One is an SRE engineer and the other is an analytics BI.

There are two things, but they haven't been fully quantified. One is the time required for onboarding new data sources, and then two is that we don't have to stand up a data engineering department or function. I would say potentially Fivetran could replace at least one full-time engineer. As far as ROI, we could say maybe one FTE worth of time, though, obviously, there's the contract expense that goes with that.

We started off with just our credit card and we made payments. Now we're on a separate and negotiated contract.

The pricing generally can be very expensive and a little bit opaque, but they can be negotiated down because it is a SA solution. They've changed the pricing model. They do it by monthly rows now, I think. Also, their pricing practices, when we experienced them, were not very good. They would automatically renew a contract without negotiation, which is not good practice from a client perspective. I would say they're a little bit on the expensive side, and their contract process is not particularly good, but there is a lot of potential flexibility.

My advice is to be very clear about how many rows or the volume of data because that is the main driver of the cost. Then, be wary of the contract terms if it has an escalator per year. Also, obviously just catalog all the different sources and then, if there are sources that aren't available, see if they're on the roadmap or if they have the capability of doing custom connectors.

If I'm comparing it to other solutions in the market, I'd give this solution an eight out of ten. 

I think it does a very good job of being able to quickly stand up and connect to sources. It's even possible from a startup perspective. If you only have one person, you can connect three, four, five, or ten different systems and be up and running in a very short timeframe without having to do custom work. The stability is good, the pricing is okay, and the service is okay, and I think there is significant value in the product. There are more competitors coming about that might offer more customization, but I think that out of the box, Fivetran is probably the easiest to use.

We are ingesting data from various SaaS tools.

The general data ingestion is valuable. It's used for a lot of data. It provides about 90% of the data we use in our data warehouse without needing data engineering.

Fivetran is very expensive for data sources with a lot of rows, such as email data. I would like to see cheaper pricing for data sources like that.

I have been using this solution for three years.

Some connectors are more stable than others. It seems it depends on which API they're connecting to.

It's scalable. We have six users who are using this solution. In terms of our plans to increase its usage, if we start using additional SaaS tools, we will add them to Fivetran.

I would rate them a four out of five.

Positive

We didn't use any other solution previously.

It was straightforward.

It was implemented in-house. It requires less than one person for deployment and maintenance.

We have seen an ROI.

I can't give exact amounts because that's based on usage, but it's more expensive than some of its competitors.

We evaluated other options.

It's excellent for any eCommerce data and advertising data, but consider another solution for the data that uses a lot of rows.

I would rate it an eight out of ten.

We are doing a proof of concept, and we are still building the operating model. We are using it for moving data from one source to another. We will be using HVR with Snowflake.

It is not like a traditional ETL, but it gives quite a lot of flexibility. 

It should have a few more monitoring functionalities. It should also support integration with more data sources. Currently, it supports Snowflake, AWS, and Google Cloud, but it can also support SAP integration. They can make it more flexible.

I have been using this solution for around six months.

It seems to be stable, but we have not yet used it in production. It is still in development. 

We are a large enterprise, but it also seems suitable for small and medium enterprises.

We have not contacted their support.

We did not do any deployment at all. We started around six months back, and we are just in the early stages of doing a proof of concept. 

We didn't evaluate other options.

Based on our experience so far, I would rate it an eight out of ten. We will get to know more about how it is in a couple of months.

We have multiple data available from multiple sources. We use the tool to collect the data in one data warehouse.

The product has some seamless connectors, which are readily available.

The connectors from some websites are not available. It is hard to get the data and work on it. The product should expose the APIs in a better way. The cloud functions are very code-centric. A low-code tool or a no-code tool would give us more flexibility. The environment must be more development-friendly.

I have been using the solution for two to three months.

The tool’s stability is good.

Five people use the solution in our organization. We need two people to maintain the tool.

The technical support must be improved. There are a lot of communication barriers. We raise a ticket and wait for days. The team has integrations with communication channels, like Slack, but we have to wait for the support team to look up the issue and answer. The team was proactive during the proof of concept, but the support got slower as soon as we got the license.

Neutral

The product is cloud-based. The deployment process is very straightforward. We get the data from different websites and integrate it into our database. We just sync the data on a daily or weekly basis. We needed two people for the deployment. It took two to three days to deploy the tool.

The solution is affordable. The pricing model is good.

We evaluated Domo, Snowflake, and Airbyte. We chose Fivetran because it was a prominent product in the market.

I did the proof of concept, and my organization is in the process of deploying the solution. We have a lot of issues. People who want to work with the product must list the requirements of the extraction website and the web sources from which the data needs to be extracted. They should choose Fivetran only if the connector is readily available. They must not search for custom connectors. Overall, I rate the tool a seven out of ten.