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."This solution has provided us with an easier, and more efficient way to carry out data migration tasks."
"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 initial setup is very quick and easy."
"We haven't had any issues connecting it to other products."
"The most important feature is that it can help you do the multi-threading concepts."
"It has built-in connectors for more than 100 sources and onboarding data from many different sources to the cloud environment."
"The most valuable feature of Azure Data Factory is the core features that help you through the whole Azure pipeline or value chain."
"The most valuable feature is the copy activity."
"The initial setup is very straightforward."
"It's more developer-friendly, and development can be done at a faster phase."
"The API architecture makes it easy for orchestration."
"You can use other languages, such as Python, and easily connect to other systems."
"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."
"I found SnapLogic valuable and what I found most valuable about it was its ETL feature. I also found its automation feature valuable. It can be used for automating manual activities. It can be used as a middleware for certain transactional data processing and minimal datasets and ETL activities."
"The feature I found most valuable in SnapLogic is low-code development. Low-code development has been very useful for simple processes, which is required for business users such as extracting details from a file or getting things reported by calling your web service. Calling your web service also becomes easier with SnapLogic because of the snaps available, so if you have the documentation, you can call an API. You don't have to write all those clients to call an API, so that is another feature I found very easy in SnapLogic. Configuring and managing all the file systems also become very handy with the solution."
"The solutions ability to connect "snaps" or components to the graphic user interface is very intuitive, prevents errors, and makes implementations easy."
"DataStage is easier to learn than Data Factory because it's more visual. Data Factory has some drag-and-drop options, but it's not as intuitive as DataStage. It would be better if they added more drag-and-drop features. You can start using DataStage without knowing the code. You don't need to learn how the code works before using the solution."
"The need to work more on developing out-of-the-box connectors for other products like Oracle, AWS, and others."
"My only problem is the seamless connectivity with various other databases, for example, SAP."
"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."
"There are limitations when processing more than one GD file."
"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."
"There's no Oracle connector if you want to do transformation using data flow activity, so Azure Data Factory needs more connectors for data flow transformation."
"The pricing scheme is very complex and difficult to understand."
"Connecting to data behind enterprise firewalls has been tricky."
"I don't think the support has better knowledge about technologies and tool support. There were lots of times when we had an issue, and it took me quite a long time to explain the problem. I feel like some of the support staff don't know their product well."
"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."
"The dashboards regarding scheduled tasks need further improvement."
"SnapLogic should have some inbuilt protocol mechanism in order to speed up."
"What could be improved in SnapLogic is that it was not capable in terms of processing a large number of datasets, but at that point, SnapLogic was evolving. It didn't give a lot of Snaps. I heard recently there are a lot of Snaps getting added and the solution was being enhanced, particularly to connect different data sources. When I was working with SnapLogic six months to one year back, I faced the issue of it not being capable of handling a huge volume of datasets or didn't have much of Snaps, and that was the drawback. If there is any large number of data sets, that's based on or depends on your configuration. If it is a huge volume of data, other traditional ETL tools such as Informatica and Talend can process millions and billions of records, while in SnapLogic, the Snaplex fails or it returns an error in terms of processing that huge volume of data. Informatica, Talend, or any other ETL tool can run for hours in terms of jobs, while SnapLogic jobs fail when the threshold is reached. SnapLogic isn't able to withstand processing, but I don't know if that's still an issue at present, because the solution is getting enhanced and it's been more than six months to one year since I last worked with SnapLogic. There are now a lot of Snaps getting added to the solution, and if it can overcome the limitations I mentioned, SnapLogic could be the go-to tool because currently, it's not being used as much in organizations. It's being used comparatively less compared to other retail tools."
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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