We performed a comparison between Azure Data Factory and Workato 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."It is easy to deploy workflows and schedule jobs."
"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."
"The most valuable feature of this solution would be ease of use."
"The most important feature is that it can help you do the multi-threading concepts."
"We have been using drivers to connect to various data sets and consume data."
"We have found the bulk load feature very valuable."
"The most valuable feature is the ease in which you can create an ETL pipeline."
"The best part of this product is the extraction, transformation, and load."
"The most valuable features are easy integration, quick tab to develop, quick tab to market, and interactive integration platform."
"It is easier to use than other technical open-source technologies."
"Their automation and workflows are very strong, and you can build the workflows very easily and quickly."
"Stability-wise, I rate the solution a ten out of ten."
"It is very easy to use. It takes complex workloads away, and it provides very smooth integration. It has got a lot of out-of-the-box connectors. It has got a lot of out-of-the-box APIs, which makes it really easy to integrate with applications for different use cases, whether it is in finance, HR, or customer operations. It is by far our favorite integration product."
"It's easy to understand, makes flows and is efficient."
"Workato is low code, intuitive, and easy to use."
"There aren't many third-party extensions or plugins available in the solution."
"Lacks in-built streaming data processing."
"You cannot use a custom data delimiter, which means that you have problems receiving data in certain formats."
"I would like to see this time travel feature in Snowflake added to Azure Data Factory."
"We are too early into the entire cycle for us to really comment on what problems we face. We're mostly using it for transformations, like ETL tasks. I think we are comfortable with the facts or the facts setting. But for other parts, it is too early to comment on."
"The one element of the solution that we have used and could be improved is the user interface."
"I have not found any real shortcomings within the product."
"Data Factory has so many features that it can be a little difficult or confusing to find some settings and configurations. I'm sure there's a way to make it a little easier to navigate."
"I would like to see the dashboarding and reporting processes improved."
"The only minor drawback would be if there was a UI at the front of it, for apps and for a portal as well, it would make it really easy. It would be really useful if there were more apps capabilities."
"Workato's Extract, transform, and load (ETL) and extract, load, and transform (ELT) are not very strong and could be improved for very complex transformations."
"A limitation is that their cloud presence is only in North America and Europe."
"It is tedious to make a tailor-fit function."
"The management dashboard in the solution is an area with shortcomings that needs improvement."
"From the deployment point of view, our customers encounter platform downtime where it does not serve the actual request on the systems."
Azure Data Factory is ranked 1st in Data Integration with 81 reviews while Workato is ranked 8th in Integration Platform as a Service (iPaaS) with 9 reviews. Azure Data Factory is rated 8.0, while Workato is rated 8.4. 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 Workato writes " Great automation and strong workflows useful for integration". Azure Data Factory is most compared with Informatica PowerCenter, Informatica Cloud Data Integration, Alteryx Designer, Snowflake and IBM InfoSphere DataStage, whereas Workato is most compared with Microsoft Azure Logic Apps, MuleSoft Composer, Oracle Integration Cloud Service, Fivetran and Alteryx Designer. See our Azure Data Factory vs. Workato report.
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