We performed a comparison between Azure Data Factory and Fivetran based on real PeerSpot user reviews.
Find out in this report how the two Data Integration solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."The workflow automation features in GitLab, particularly its low code/no code approach, are highly beneficial for accelerating development speed. This feature allows for quick creation of pipelines and offers customization options for integration needs, making it versatile for various use cases. GitLab supports a wide range of connectors, catering to a majority of integration needs. Azure Data Factory's virtual enterprise and monitoring capabilities, the visual interface of GitLab makes it user-friendly and easy to teach, facilitating adoption within teams. While the monitoring capabilities are sufficient out of the box, they may not be as comprehensive as dedicated enterprise monitoring tools. GitLab's monitoring features are manageable for production use, with the option to integrate log analytics or create custom dashboards if needed. The data flow feature in Azure Data Factory within GitLab is valuable for data transformation tasks, especially for those who may not have expertise in writing complex code. It simplifies the process of data manipulation and is particularly useful for individuals unfamiliar with Spark coding. While there could be improvements for more flexibility, overall, the data flow feature effectively accomplishes its purpose within GitLab's ecosystem."
"UI is easy to navigate and I can retrieve VTL code without knowing in-depth coding languages."
"The most valuable feature is the copy activity."
"The solution has a good interface and the integration with GitHub is very useful."
"The two most valuable features of Azure Data Factory are that it's very scalable and that it's also highly reliable."
"We use the solution to move data from on-premises to the cloud."
"Data Flow and Databricks are going to be extremely valuable services, allowing data solutions to scale as the business grows and new data sources are added."
"The scalability of the product is impressive."
"The solution is stable. We've never faced any stability issues."
"The product has some seamless connectors, which are readily available."
"The product is very easy to use and very easy to configure."
"The compare feature is the most valuable piece of it."
"The most valuable feature of Fivetran is that it only synchronizes what needs to be synchronized."
"Fivetran can perform data migration incredibly fast, depending on the source and target."
"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."
"The simplicity of the solution is its valuable feature."
"There is room for improvement primarily in its streaming capabilities. For structured streaming and machine learning model implementation within an ETL process, it lags behind tools like Informatica."
"The solution needs to integrate more with other providers and should have a closer integration with Oracle BI."
"Currently, smaller businesses face a disadvantage in terms of pricing, and reducing costs could address this issue."
"We have experienced some issues with the integration. This is an area that needs improvement."
"There's space for improvement in the development process of the data pipelines."
"A room for improvement in Azure Data Factory is its speed. Parallelization also needs improvement."
"The solution should offer better integration with Azure machine learning. We should be able to embed the cognitive services from Microsoft, for example as a web API. It should allow us to embed Azure machine learning in a more user-friendly way."
"The number of standard adaptors could be extended further."
"I would like Fivetran to implement additional resource monitoring and restriction policies."
"The connections with SAP must be improved."
"The interface needs to be more user-friendly."
"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."
"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."
"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."
"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."
"The solution is very expensive. I would like to have a better integration of the solution with Azure."
Azure Data Factory is ranked 1st in Data Integration with 81 reviews while Fivetran is ranked 13th in Data Integration with 20 reviews. Azure Data Factory is rated 8.0, while Fivetran is rated 8.0. The top reviewer of Azure Data Factory writes "The data factory agent is quite good but pricing needs to be more transparent". On the other hand, the top reviewer of Fivetran writes "Solution reduces time-to-value; high ROI". Azure Data Factory is most compared with Informatica PowerCenter, Informatica Cloud Data Integration, Alteryx Designer, Snowflake and IBM InfoSphere DataStage, whereas Fivetran is most compared with AWS Database Migration Service, Qlik Replicate, Oracle GoldenGate, Informatica Cloud Data Integration and StreamSets. See our Azure Data Factory vs. Fivetran report.
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We monitor all Data Integration reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
