Azure Data Factory vs Palantir Gotham comparison

"The tool's most valuable features are its connectors. It has many out-of-the-box connectors. We use ADF for ETL processes. Our main use case involves integrating data from various databases, processing it, and loading it into the target database. ADF plays a crucial role in orchestrating these ETL workflows."

"I find that the solution integrates well with cloud technologies, which we are using for different clouds like Snowflake and AWS"

"I like that it's a monolithic data platform. This is why we propose these solutions."

"This solution has provided us with an easier, and more efficient way to carry out data migration tasks."

"We have found the bulk load feature very valuable."

"The solution includes a feature that increases the number of processors used which makes it very powerful and adds to the scalability."

"We haven't had any issues connecting it to other products."

"This solution will allow the organisation to improve its existing data offerings over time by adding predictive analytics, data sharing via APIs and other enhancements readily."

"This solution is seamless. From one platform, we can do just about anything."

"When we initiated the cluster, it took some time to start the process."

"Real-time replication is required, and this is not a simple task."

"There's space for improvement in the development process of the data pipelines."

"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 user interface could use improvement. It's not a major issue but it's something that can be improved."

"Customer service is not satisfactory. Third-party personnel handle support and rely on a knowledge repository."

"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."

"There is always room to improve. There should be good examples of use that, of course, customers aren't always willing to share. It is Catch-22. It would help the user base if everybody had really good examples of deployments that worked, but when you ask people to put out their good deployments, which also includes me, you usually got, "No, I'm not going to do that." They don't have enough good examples. Microsoft probably just needs to pay one of their partners to build 20 or 30 examples of functional Data Factories and then share them as a user base."

"I think there should be less coding involved. Currently, using it involves a tremendous amount of coding."

"Understanding the pricing model for Data Factory is quite complex."

"The solution is cheap."

"The pricing is pay-as-you-go or reserve instance. Of the two options, reserve instance is much cheaper."

"The cost is based on the amount of data sets that we are ingesting."

"The price you pay is determined by how much you use it."

"I rate the product price as six on a scale of one to ten, where one is low price and ten is high price."

"The licensing cost is included in the Synapse."

"The licensing model for Azure Data Factory is good because you won't have to overpay. Pricing-wise, the solution is a five out of ten. It was not expensive, and it was not cheap."