Fivetran Reviews

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 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 use Fivetran for bidirectional data synchronizations between systems.

I didn't set up any transformation in Fivetran. I wanted Fivetran to take care of the log sequence number comparison between source and destination systems so that it would pick up only the data that needed to be synchronized, and it did it beautifully.

The most valuable feature of Fivetran is that it only synchronizes what needs to be synchronized. You need to capture the in-source updates and digits from the source; this is the only information you want. You don't want to download the whole thing each time. Fivetran does this just beautifully.

Fivetran should add more connectors because its competitors, like Airbyte, have more connectors. All of these systems have a problem, namely that they are pure cloud player solutions. I often work in hybrid solutions where customers have on-prem things mixed with cloud things.

Pure cloud player solutions never provide a good solution for what is on-prem, which leads to a gap. When you look at systems such as ActiveBatch, they have a very, very strong on-prem platform. They are also looking at the web and cloud things, but they are not that good at the cloud things. So there is nothing that is equally good for both on-prem and cloud.

Fivetran should add a feature to connect to the SQL server on-prem.

I have been using Fivetran for a month.

Fivetran is a very stable solution. After you set it up, you can forget about it. I rate Fivetran a nine out of ten for stability.

Fivetran saved me a lot of work. I could have hired a bunch of engineers to write Python's code for me, but Fivetran was already built. So, it was easier for me to focus on my line of business and purchase a commercial product without trying to replicate this myself in Python. That's not a return on investment; that is savings.

I am using the latest version of Fivetran.

Fivetran is easy to set up. It took me one or two hours to set up my flow, and then I forgot about it. So for me, it's perfect because it doesn't take a lot of time. It's not like some other solutions where you need two weeks of engineering to get them to work.

I would advise users to try Fivetran before they commit to it.

When I compare Fivetran with Airbyte, I like Airbyte better than Fivetran.

Overall, I rate Fivetran 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.

I'm responsible for replicating the data from one source to another. It is constantly running, and we do nothing other than looking at monitoring reports. Once it is established, it is pretty much hands-off.

We are not using its latest version. We are slow to migrate to the new version. We wait until there is a need to upgrade, and we are generally one or two versions behind.

It has improved the way our organization functions.

The compare feature is the most valuable piece of it.

The documentation can be laid out better to make it easier to find things, and I really wish there was built-in support for changing passwords.

Some features don't work as advertised for the platform/repository database, and HVR is not always the fastest at getting results.

I have been using this solution for three years.

We like its stability.

We like its scalability.

They are adequate.

I have used GoldenGate replication previously.

Installing the software is really straightforward. Getting your replication stream working is a little bit more complicated, but it is not overly complicated.

Its deployment was done in-house. It took weeks. 

There are two people to manage it and make sure that it is working. They are part of the data team or the infrastructure team making sure that we're building our analytic pipeline.

I don't have the exact information, but I know it is high, and it is on a yearly basis. There is no additional cost for what we're doing.

We're always open to doing things cheaper, so we might potentially implement a different solution.

We looked at Informatica, but one of the sources that we had was only supported with HVR. That's why we went with HVR.

I would rate HVR an eight 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.

We use the solution to replicate data from our ERP.

The product is very easy to use and very easy to configure. We can do an end-to-end configuration in a few minutes.

The connections with SAP must be improved. The environment has some limitations.

I have been using the solution since January 2023.

The product’s stability is very good.

The tool’s scalability is very good.

The initial setup is very easy. It is not that complex. I was part of the deployment process. It is still ongoing. We are not running production for now.

We need two or three IT employees to maintain the solution.

The solution has good pricing. I rate the pricing a six out of ten.

It's a good integration tool. It is easy to use. Overall, I rate the solution a nine out of ten.

Fivetran is used as an ingestion tool to move data between different sources and destinations. It orchestrates a series of pipelines that can be easily set up manually without any technical experience. These pipelines can run daily according to a specific schedule, depending on the licensing level. Fivetran addresses schema changes and updates destination tables unless there's a major difference, which is helpful in integrating systems from third parties.

Before using Fivetran, most integrations were done manually, leading to a lead time of four to eight weeks for a new connection. Now, I can set up new connections in anywhere from a day to a week and a half, reducing data ingestion lead times by sixty to eighty percent. 

There is a lot less maintenance to do on the pipelines, which has freed up development hours by about twenty percent. Overall, there's a big gain in development hours and less time maintaining data.

The simplicity and scalability are the strongest features of Fivetran. The learning curve is not steep, making it easy to get accustomed to. It's transparent in understanding when something fails, and the logging is detailed, which helps in auditing and accessing information rapidly. 

For scalability, Fivetran charges less per row the more you use it, allowing you to move more data economically as you set up additional data pipelines.

The biggest area for improvement is in customization, particularly in how Fivetran socializes its tools. Many features like deploying Fivetran on-premises for security purposes or using connectors specific to systems like SAP are not well communicated. New users might not be aware of these features due to the lack of socialization when there are new releases or updates.

I have been using the solution for about four years.

I would rate the stability a nine out of ten. I have never had an issue with downtime at all, though I have encountered technical issues using the API in a more advanced fashion for custom setups.

I rate the scalability as a solid nine. All you have to do is plug in a new data source, which contributes to your quota for different plans. Fivetran also provides flexibility in managing data ingestion costs through different plans.

The customer service is very good and thorough, however, it can be slow, usually taking about three to five business days for a response. Generally, I would rate it a seven out of ten because of this lead time.

Neutral

Before Fivetran, everything was developed by hand using Python. This switch to Fivetran was a big improvement.

The initial setup for the standard cloud licenses is straightforward and simple. It's a matter of connecting data sources to destinations, which Fivetran's platform largely automates once you input the correct information.

I handled the initial deployment myself.

Fivetran has drastically reduced the lead time for setting up new connections to one day to one and a half weeks, down from four to eight weeks. It has also decreased the time required for pipeline maintenance, resulting in a gain of sixty to seventy percent more development hours.

The pricing is based on how much data is ingested, not on the number of rows. The tool's features expand with higher licensing tiers, such as enterprise or business critical, which provide more secure options. The pricing is reasonable, allowing small companies to operate at a cost below forty thousand a year while larger companies experience diminishing costs per data unit as usage increases, projected not to exceed one hundred thousand per month.

We evaluated Airbyte, Snowflake, and Databricks. Fivetran was ultimately chosen due to its simplicity in setup and configuration. 

Snowflake and Databricks set up pipelines in a more complex way, and Databricks' orchestration workflow incurred heavier monthly costs.

Fivetran is a very good tool for simplifying data management and ingestion. It is particularly useful for tracing data lineage, ensuring data flows correctly, and is beneficial if managing multiple data tools requires minimizing complexity.

I'd rate the solution eight out of ten.