Compare AWS Data Pipeline and Azure Data Factory 
- Users report that AWS Data Pipeline excels in its extraction capabilities, with a score of 9.1, while Azure Data Factory also performs well with a score of 9.3, but reviewers mention that Azure's integration with other Azure services enhances its extraction process significantly.
- Reviewers mention that Azure Data Factory shines in its ease of setup, scoring 9.0 compared to AWS Data Pipeline's lower score of 6.9. Users on G2 appreciate Azure's user-friendly interface and streamlined onboarding process.
- G2 users highlight that AWS Data Pipeline has strong automation features, scoring 9.2, but users say that Azure Data Factory's automation capabilities are more robust, particularly with its integration of triggers and scheduling, leading to a higher overall satisfaction.
- Users report that Azure Data Factory's scalability is impressive, with a score of 9.3, allowing for seamless handling of large data volumes, while AWS Data Pipeline, although capable, received a slightly lower score, indicating some limitations in scaling for certain use cases.
- Reviewers mention that the quality of support for Azure Data Factory is rated higher at 8.8 compared to AWS Data Pipeline's 8.3, with users on G2 noting faster response times and more comprehensive resources available for troubleshooting.
- Users say that the product direction for Azure Data Factory is highly rated at 9.8, indicating a strong commitment to continuous improvement and feature updates, while AWS Data Pipeline's score of 6.1 suggests some concerns among users regarding its future development and feature enhancements.
Simplicity compared to software like SQL
Data Pipeline can be a black box at times. Error messages are not good, and it is difficult to understand what exactly failed since it is an amazon service. The scheduler doesn't give timely notifications at times, so it is hard to determine the true state...
Ease to making and deploying the data pipeline in Azure Data factory and best for all migration of large of amount of database .
SOmetimes it becomes difficult to comprehend the errors due to which the data pipeline fails. Even after looking on internet doesn't help so may be the error message can be improved which helps users to comprehend and easily resolve it.
Simplicity compared to software like SQL
Ease to making and deploying the data pipeline in Azure Data factory and best for all migration of large of amount of database .
Data Pipeline can be a black box at times. Error messages are not good, and it is difficult to understand what exactly failed since it is an amazon service. The scheduler doesn't give timely notifications at times, so it is hard to determine the true state...
SOmetimes it becomes difficult to comprehend the errors due to which the data pipeline fails. Even after looking on internet doesn't help so may be the error message can be improved which helps users to comprehend and easily resolve it.
