Fivetran Reviews

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.

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.

We are currently using the private model of this solution. It is CDC and we take data from an OLTP system to populate a data warehouse and to populate a reporting system. 

The ease of usability is the most valuable feature. It's very easy and quick to set up. It also has a central hub as opposed to GoldenGate which is one direct interface. For GoldenGate I would have needed three interfaces whereas with HVR I have a central interface that manages everything. 

We have a number of connections to heterogeneous databases and that could be expanded on. One of the databases is an additional database that should be coming online but there is some difficulty with it. Other than this, in comparison to other products it's competing with, including Oracle, it beats them hands down for everything, including the price.

I would like for them to incorporate additional transformations. A valuable aspect of the product is that it does inflight transformations and that could be expanded. 

Their technical support is excellent, better than Oracle. The online support is very good but equally, their support is immediate and willing. Up until now, they have taken our suggestions and considered incorporating them into the next version. 

The initial setup was straightforward. 

The pricing is considerably less than its competitor, GoldenGate. One of the things that we liked about it and that sold us on it, is that we only licensed production and it then enabled us to install the product on any other non-production. Whereas with Oracle, it's in every environment. In the three environments that I have HVR, I only need to license production. Whereas with Oracle I would've had to license all three environments. 

I would suggest not to discount it because of its low price and small company. Most of the technicians are ex-Oracle employees and they have a passion for data transformations and data support. They are very proactive which is a breath of fresh air when you compare it with Oracle support. 

I would rate it 8.5 out of ten. 

We use Fivetran to build data engineering pipelines. It helps us do a lot of data transformation and data extraction. 

Fivetran helps us save time and be more efficient because we do things with no-code/low-code. 

The most important feature of the solution is its ability to build data pipelines in less time. 

We experience cost issues because Fivetran is charged on a usage basis. When you reach a certain level, the tool should focus on reducing the costs. The solution is expensive when you are moving gigabytes and petabytes of data. 

It should also focus more on REST APIs and webhooks. 

I have been using the solution for two years. 

The solution is very stable and reliable. 

The product's scalability is high. 

Fivetran's technical support is average and can take weeks to respond. 

Fivetran's deployment is simple. 

I rate the solution a  seven out of ten. 

Fivetran is an ingestion tool used to move data between different sources and destinations. It helps orchestrate pipelines that can be set up manually without technical experience. It facilitates daily pipeline runs based on a specific schedule determined by the licensing level.

Fivetran has significantly reduced data ingestion lead times by about sixty to eighty percent. It requires less maintenance on pipelines, freeing up development hours by approximately twenty percent. Additionally, the platform allows for faster setup of new connections, reducing lead time from four to eight weeks to one or a week and a half for complex integrations.

The simplicity and scalability stand out as the most valuable features of Fivetran. It is easy for users to get accustomed to the tool due to its gentle learning curve and clear error transparency. The platform offers transparent logging for detailed auditing. Moreover, scalability is emphasized as Fivetran charges less per row as you use the service more.

Fivetran could improve in the area of customization, especially in how they socialize their tools. The feature of being able to deploy Fivetran on-premises for enhanced security should be highlighted more during discussions with executives.

I have used Fivetran for about four years now.

I rate the stability of Fivetran as nine out of ten. We have never experienced downtime issues.

I rate the scalability of Fivetran as nine out of ten. It offers a flexible cost structure that benefits those who use it more extensively.

Customer service is thorough but could be faster, rated as seven out of ten. They usually take about three to five business days to respond.

Neutral

Previously, everything was developed by hand using Python before moving to Fivetran. Fivetran was a significant improvement.

The standard cloud licenses are very easy to set up. However, more complex deployments like self-hosted can be more technical.

I managed the setup alone. In all the last two companies I've worked at, it has been a solo effort.

Fivetran has drastically improved our operations. We've reduced lead times for data ingestion by sixty to eighty percent and have fewer maintenance requirements, leading to a twenty percent increase in development hours. The system has simplified manual integration processes, leading to quicker setup times for new connections.

The pricing is sensible and depends on the data ingestion quotas. The standard enterprise allows for advanced development, while the free tier offers a limited feature set. High security options require a business-critical license.

We evaluated Airbyte, Snowflake, and Databricks before choosing Fivetran. Snowflake and Databricks were less competitive due to their complexity and higher resource costs.

Fivetran is a great tool for organizations needing to simplify data management and ingestion processes. It's ideal for those without a large team to handle multiple data tools.

I'd rate the solution eight out of ten.

SysTrack load is the best feature. 

This solution needs to improve its real-time data and transformation availability. You have to extract a load before you can do any transformation. 

Another improvement that can be made is with load extraction. This functionality should be implemented with obligating data and data masking.

I have been using Fivetran for a couple of months. 

The stability is an eight out of ten. 

I rate the pricing a six out of ten.

Overall, I rate the solution a seven out of ten.

I mainly use HVR Software for replication and migration.

HVR Software is an asynchronous replication solution. Its arrays are powerful enough to handle migrations even when the replication is happening in the background, without causing any trouble with the ongoing traffic.

HVR Software's most valuable features are that it's simple to use and allows us to use the command line to quickly complete the entire universal replicative setup.

In the next release, I'd like to see an automated version of HVR with a script that can be run on a management server where we can provide all the information within the script, or the script can prompt the user to enter the storage area and volume details so that with just a few clicks, the solution will be deployed automatically in the background.

I've been using HVR Software for fourteen months.

HVR Software is stable.

HVR Software is scalable.

HVR Software's technical support could be improved. Whenever we log a case, the response that we get from the support is a bit delayed.

Positive

The initial setup was straightforward because we had all the required documentation.

We used an in-house team.

I've heard that the license for HVR is a bit costly compared to its competitors, but since it's reliable and efficient, I think the customer shouldn't be bothered about the cost.

I would rate HVR Software as ten out of ten.

We mainly use Fivetran for ETL.

There's a very good layer that allows you to connect different data sources and establish a very good ETL workflow. You can manage all of your connectors individually, which gives you a very good ability to trace which one of your ETL processes is running and when. The solution also allows you to schedule each of those runs with different cadences, depending on which plan you are on. At the same time, for those runs, you have the ability to hash, unhash, or map any data that might be sensitive or personally identifiable, and that's pretty robust. It's a feature in the standard plan, so you don't need the enterprise plan for that. One very powerful and cool feature is how you can integrate the solution with other transformation tools, such as dbt, or run the transformations within Fivetran itself to create a transformation layer on top of your ETL layer. That allows you to manage the entire ETL workflow end to end from within the platform without the need for any additional tool.

When it comes to managing ETL pipelines, these features are valuable.

Given that Fivetran is a fully managed third-party solution, the customization could improve because Fivetran gives more thought to people who don't want to manage analytics workflows rather than engineers who want to be able to customize pipelines more thoroughly.

I've had experience with Fivetran for about two-and-a-half to three years.

The scalability will directly relate to the plan you're on. It will be very important to see that the provisioning in compute and the provisioning you have to run workflows and jobs will also be directly related to your use case. Managing scalability in that way is a bit complex, but you get a lot of support from Fivetran.

From a customer support standpoint, Fivetran does an amazing job in establishing and helping you customize while ensuring you have the right plan for your data needs. I have full contact with our account executive, and she's super nice and walks us through many of these use cases that will be important in the future and many of the estimations we might need for the workflow we plan on integrating.

Positive

I have used Splunk before, and it's more technical. Splunk allows you to version things and have a good engineering process around your ETL pipeline. It also has very good logging and documentation tools. But now that Fivetran has a better exposed API, it is much more flexible. But Splunk beats Fivetran regarding things like customization, for example. We switched to Fivetran because we reduced the engineering team. We had fewer people to manage all the different ETL processes, so we had to leverage more automated third-party tools, and Fivetran made onboarding new engineers a lot easier.

Fivetran is very easy to set up. It's a matter of having the connection information of each one of the endpoints you want the ETL through. The solution is very comprehensive and easy to set up. It even gives you assistance on where to find the information that you might need, depending on the data source. It has pre-built connectors for the most common data sources, so it also helps you expedite that process if you are not super tech-savvy.

Fivetran has a pricing model that scales the more data sources you add. When you have a lot of workflows and complex use cases, pricing goes down as you use it more. That's a very good feature of its pricing. Fivetran might be a bit pricey for a very limited or small number of data sources that might be easily collected in other forms. The pricing gets better the bigger you are.

Comparing Fivetran to other tools such as dbt, AWS Glue, or Azure Data Factory, the other tools are more robust and allow us to run custom transformations.

To anyone who uses the product, I say monitor. You have a dashboard to monitor your MAR, which is a row-level metric Fivetran uses to gauge how much you are consuming. If you can monitor your MAR closely, you can get a very good understanding of how much data you're moving, and it will allow you to adjust the cadences you might need to get a better bang for your buck. Watch your numbers and try to plug in as many data sources as you need because that will help you with pricing.

I rate Fivetran a seven or a seven-point five out of ten. It's solid and offers a very simple UI and way to get set up. It might fall short for people who want more advanced use cases or people who have knowledge and tools that they can integrate, and that's where things get a bit more technical. But I feel the leap from just getting started with it to getting very technical with it is very large. There's no middle ground.