Compare Apache NiFi and SAP Data Services 
- Users report that Apache NiFi excels in its data extraction capabilities, with a score of 9.5, allowing for seamless integration with various data sources. Reviewers mention that its "data provenance" feature is particularly useful for tracking data flow and transformations.
- Reviewers mention that SAP Data Services shines in its transformation functionality, scoring 9.3, which is enhanced by its robust ETL capabilities. Users on G2 highlight the "data quality transformation" features as a significant advantage for ensuring data integrity.
- G2 users indicate that Apache NiFi has a higher ease of setup score (9.0) compared to SAP Data Services (7.5), with reviewers saying that its user-friendly interface and drag-and-drop functionality make it accessible for users with varying technical skills.
- Users say that while Apache NiFi has a solid automation score of 9.3, SAP Data Services offers more comprehensive automation features, particularly in scheduling and job management, which some reviewers find essential for large-scale data operations.
- Reviewers mention that Apache NiFi's scalability is rated at 8.3, which some users find limiting for very large datasets, while SAP Data Services is noted for its ability to handle enterprise-level data volumes more effectively, making it a preferred choice for larger organizations.
- Users report that the quality of support for SAP Data Services is rated higher (8.8) compared to Apache NiFi (7.5), with many reviewers highlighting the responsiveness and expertise of SAP's support team as a key factor in their satisfaction with the product.
It's open-source and community-supported, you can build anything you want, from simple file ingestion to Kafka, S3, etc... The ability to create Process groups and isolate your workloads. The number of prebuilt processors. The flow-based programming comes...
Tracking lineage at a row level is important in data lake ingestion implementation. Can Lineage be controlled at per-row level? Batch transformation performance. Need to Benchmark. May require Kafka
The best thing about is it provide on-premises deployment. Also we can access data in broader way as data connectivity is great in application.
It's open-source and community-supported, you can build anything you want, from simple file ingestion to Kafka, S3, etc... The ability to create Process groups and isolate your workloads. The number of prebuilt processors. The flow-based programming comes...
The best thing about is it provide on-premises deployment. Also we can access data in broader way as data connectivity is great in application.
Tracking lineage at a row level is important in data lake ingestion implementation. Can Lineage be controlled at per-row level? Batch transformation performance. Need to Benchmark. May require Kafka
