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, allowing for seamless integration with various data sources. In contrast, Azure Data Factory also performs well in this area but is noted for its superior transformation features, scoring 9.3, which reviewers say enhances data manipulation and preparation.
- Reviewers mention that Azure Data Factory's ease of setup is a significant advantage, scoring 9.0 compared to AWS Data Pipeline's lower score of 6.9. Users on G2 appreciate the intuitive interface and streamlined onboarding process of Azure Data Factory, making it more accessible for new users.
- G2 users highlight the automation capabilities of AWS Data Pipeline, scoring 9.2, which allows for efficient scheduling and execution of data workflows. However, users report that Azure Data Factory's automation features are more robust, particularly with its integration of triggers and monitoring tools, leading to a higher overall satisfaction.
- 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. Users appreciate the responsiveness and helpfulness of Azure's support team, which they say contributes to a better overall experience when troubleshooting issues.
- Users say that AWS Data Pipeline's scalability is impressive, with a score of 9.3, allowing businesses to handle large volumes of data efficiently. However, Azure Data Factory is often praised for its flexibility in scaling resources dynamically, which reviewers mention is crucial for adapting to changing data workloads.
- Reviewers report that the product direction for Azure Data Factory is highly rated at 9.8, indicating strong confidence in its future development and feature enhancements. In contrast, AWS Data Pipeline's lower score of 6.1 suggests some uncertainty among users regarding its long-term roadmap and updates.
Data Pipeline integrates really well with EMR, and it's easy to deploy pipelines via cloudformation making automation possible. We use it to manage complex map-reduce workflows and it usually works pretty smoothly.
It really should be redesigned as a json blob is a terrible way to organize SQL jobs. Why can't things be scheduled like a calendar or meeting request?
The best features according to me are Hybrid Data Integration, Data Movement, Orchestration and Scheduling and Integration with other Azure Services.
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.
Data Pipeline integrates really well with EMR, and it's easy to deploy pipelines via cloudformation making automation possible. We use it to manage complex map-reduce workflows and it usually works pretty smoothly.
The best features according to me are Hybrid Data Integration, Data Movement, Orchestration and Scheduling and Integration with other Azure Services.
It really should be redesigned as a json blob is a terrible way to organize SQL jobs. Why can't things be scheduled like a calendar or meeting request?
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.
