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- Apache Sqoop-vs-Azure Data Factory
Compare Apache Sqoop and Azure Data Factory 
- Users report that Apache Sqoop excels in data extraction capabilities, particularly with its ability to efficiently transfer large volumes of data from relational databases to Hadoop, with a strong focus on performance and speed.
- Reviewers mention that Azure Data Factory shines in its transformation features, offering a user-friendly interface for data transformation tasks, which allows for complex data workflows without extensive coding knowledge.
- G2 users highlight that Apache Sqoop has a robust automation feature, enabling scheduled data imports and exports, which is crucial for maintaining up-to-date data in Hadoop environments.
- Users on G2 report that Azure Data Factory's scalability is a significant advantage, as it can handle varying workloads seamlessly, making it suitable for enterprises with fluctuating data processing needs.
- Reviewers say that while Apache Sqoop has a strong ease of setup, Azure Data Factory's ease of use is often rated higher, with many users appreciating its intuitive drag-and-drop interface for building data pipelines.
- Users mention that the quality of support for Azure Data Factory is generally perceived as superior, with many reviewers praising the responsiveness and helpfulness of the support team compared to Apache Sqoop's support experience.
Using sqoop on the command line has provided me a quick and easy way to transfer data into sandbox databases or data lakes. This has made it easier for people in Analytics roles like myself to get the data sources together quickly to create models using...
Not very effective with de-normalized data as we need to execute multiple joins in map reduce.
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.
Using sqoop on the command line has provided me a quick and easy way to transfer data into sandbox databases or data lakes. This has made it easier for people in Analytics roles like myself to get the data sources together quickly to create models using...
The best features according to me are Hybrid Data Integration, Data Movement, Orchestration and Scheduling and Integration with other Azure Services.
Not very effective with de-normalized data as we need to execute multiple joins in map reduce.
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.
