Compare Apache NiFi and Azure Data Factory 
- Users report that Apache NiFi excels in data extraction capabilities, with a rating of 9.5, highlighting its robust support for various data sources and formats, making it a preferred choice for complex data ingestion tasks.
- Reviewers mention that Azure Data Factory shines in the loading phase, achieving a score of 9.4, particularly due to its seamless integration with Azure services and its ability to handle large-scale data loads efficiently.
- G2 users indicate that both products have similar transformation capabilities, each scoring 9.3, but users on G2 note that Apache NiFi offers more flexibility with its flow-based programming model, allowing for intricate data transformation workflows.
- Users say that while both platforms have comparable ease of setup scores (9.0), Azure Data Factory is often praised for its user-friendly interface and guided setup process, making it more accessible for new users.
- Reviewers mention that Apache NiFi's reporting features are rated highly at 9.3, with users appreciating its detailed data provenance and flow tracking, which are crucial for compliance and auditing purposes.
- Users report that Azure Data Factory outperforms in scalability with a score of 9.3, as it can easily scale to accommodate growing data needs, especially in cloud environments, which is a significant advantage for enterprises looking to expand their data operations.
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
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.
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...
Ease to making and deploying the data pipeline in Azure Data factory and best for all migration of large of amount of database .
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
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.
