We performed a comparison between Azure Data Factory and SnapLogic 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 scalability of the product is impressive."
"Data Factory's best feature is the ease of setting up pipelines for data and cloud integrations."
"ADF is another ETL tool similar to Informatica that can transform data or copy it from on-prem to the cloud or vice versa. Once we have the data, we can apply various transformations to it and schedule our pipeline according to our business needs. ADF integrates with Databricks. We can call our Databricks notebooks and schedule them via ADF."
"The most valuable feature is the ease in which you can create an ETL pipeline."
"The most valuable features of Azure Data Factory are the flexibility, ability to move data at scale, and the integrations with different Azure components."
"The data mapping and the ability to systematically derive data are nice features. It worked really well for the solution we had. It is visual, and it did the transformation as we wanted."
"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."
"For me, it was that there are dedicated connectors for different targets or sources, different data sources. For example, there is direct connector to Salesforce, Oracle Service Cloud, etcetera, and that was really helpful."
"The solution is easy to implement and easy to use. It's basically just drag and drop."
"The solution could improve its API management."
"The API architecture makes it easy for orchestration."
"It is a scalable solution."
"SnapLogic is an IPA tool that leverages a low code environment to connect to multiple sources, extract data, and store it in Azure data lake."
"The initial setup is very straightforward."
"What I found most valuable in SnapLogic is the ETL feature, particularly the Transform Snap Pack, for example, any kind of reading or writing on Transform Snaps. Other than that, all the third-party connectivity tools such as the SAP Snap Pack, Salesforce Snap Pack, Workday Snap Pack, even the ServiceNow Snap Pack, I find all those are pretty useful in SnapLogic."
"An important tool for building prototypes and MVPs than can seamlessly turn into production jobs"
"The one element of the solution that we have used and could be improved is the user interface."
"Azure Data Factory can improve the transformation features. You have to do a lot of transformation activities. This is something that is just not fully covered. Additionally, the integration could improve for other tools, such as Azure Data Catalog."
"Data Factory could be improved by eliminating the need for a physical data area. We have to extract data using Data Factory, then create a staging database for it with Azure SQL, which is very, very expensive. Another improvement would be lowering the licensing cost."
"Lacks in-built streaming data processing."
"The Microsoft documentation is too complicated."
"The solution can be improved by decreasing the warmup time which currently can take up to five minutes."
"My only problem is the seamless connectivity with various other databases, for example, SAP."
"User-friendliness and user effectiveness are unquestionably important, and it may be a good option here to improve the user experience. However, I believe that more and more sophisticated monitoring would be beneficial."
"I would like to see more performance-related dashboards, ones that display the cost of a pipeline, for instance. Also, it would be helpful to have management dashboards for overseeing pipelines and connections."
"Ultra Pipelines provides real-time ingestion but it needs some adjustment."
"The problem is that SnapLogic doesn't offer a wide variety of connectors. For example, integrating with Salesforce is not that easy."
"SnapLogic doesn't provide any on-premises software, so users have only cloud-based software to use."
"One of the areas for improvement in SnapLogic is that the connectors for some of the applications should be more available in terms of testing in the dev environment. Another area for improvement is that the logging should be standardized, for example, the integration with an ELK stack should be required out-of-the-box, so you can ship the log and have it in the ELK stack. There should be integration with ELK stack for the log shipping."
"There is room for improvement with APM management and how task execution looks."
"The support is the most important improvement they could make."
"SnapLogic should have some inbuilt protocol mechanism in order to speed up."
Azure Data Factory is ranked 1st in Data Integration with 81 reviews while SnapLogic is ranked 14th in Data Integration with 21 reviews. Azure Data Factory is rated 8.0, while SnapLogic 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 SnapLogic writes "Easy to set up, easy to use, and is low-code". Azure Data Factory is most compared with Informatica PowerCenter, Informatica Cloud Data Integration, Alteryx Designer, Snowflake and IBM InfoSphere DataStage, whereas SnapLogic is most compared with IBM InfoSphere DataStage, AWS Glue, Informatica Cloud Data Integration, SSIS and Alteryx Designer. See our Azure Data Factory vs. SnapLogic 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.
