Compare Azure Data Factory and TimeXtender 
- Users report that TimeXtender excels in its ease of use, with a score of 8.5, making it accessible for mid-market companies, while Azure Data Factory, with a score of 9.0, is noted for its user-friendly interface that appeals to enterprise-level users.
- Reviewers mention that Azure Data Factory shines in its extraction capabilities, scoring 9.1 compared to TimeXtender's 8.4, highlighting its robust integration with various data sources, which is crucial for large-scale data operations.
- G2 users indicate that TimeXtender's automation features, particularly in workflow automation, score 8.0, but Azure Data Factory outperforms with a score of 9.2, showcasing its advanced automation capabilities that streamline data processes effectively.
- Users on G2 report that TimeXtender's documentation management is rated at 7.8, which some find lacking, while Azure Data Factory's documentation is praised for being comprehensive and user-friendly, contributing to a better onboarding experience.
- Reviewers say that Azure Data Factory's scalability is a standout feature, scoring 9.3, which is essential for enterprises looking to grow their data operations, whereas TimeXtender's scalability is rated lower at 8.1, indicating potential limitations for larger datasets.
- Users report that both products have strong support, but TimeXtender's quality of support is rated slightly higher at 8.9 compared to Azure Data Factory's 8.8, with reviewers appreciating the responsiveness and helpfulness of TimeXtender's support team.
Easiness to use , no setup required and integration available with 100+ sources to fetch data.
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.
The fact that you visually have control over the different layers at all times aswell as TimeXtender handling alot of backend compatibility.
The no code dragging and dropping. The going live process. Devops has schema compare and approver. The valid and raw tables. The view without debugging like in ssms. The options of working together in one project.
Easiness to use , no setup required and integration available with 100+ sources to fetch data.
The fact that you visually have control over the different layers at all times aswell as TimeXtender handling alot of backend compatibility.
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.
The no code dragging and dropping. The going live process. Devops has schema compare and approver. The valid and raw tables. The view without debugging like in ssms. The options of working together in one project.
