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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.