Compare Apache NiFi and Informatica PowerCenter 
- Users report that Informatica PowerCenter excels in data transformation capabilities, with a score of 9.1, while Apache NiFi follows closely with a score of 9.3. Reviewers mention that PowerCenter's robust ETL processes are particularly beneficial for complex data workflows.
- Reviewers mention that Apache NiFi shines in automation, scoring 9.3 compared to Informatica's 8.5. Users on G2 appreciate NiFi's ability to automate data flows seamlessly, which enhances operational efficiency.
- G2 users highlight that Informatica PowerCenter offers superior data security features, scoring 9.2, while NiFi's score is not specified. Reviewers say that PowerCenter's comprehensive access control and data governance capabilities are critical for enterprise-level applications.
- Users say that Apache NiFi provides better ease of setup with a score of 9.0, compared to Informatica's 7.4. Reviewers mention that NiFi's user-friendly interface and drag-and-drop functionality make it easier for teams to get started quickly.
- Reviewers mention that Informatica PowerCenter has a higher quality of support, scoring 8.5, while NiFi's support is rated lower at 7.5. Users report that PowerCenter's dedicated support team is responsive and knowledgeable, which is crucial for troubleshooting complex issues.
- Users on G2 report that both products have strong reporting capabilities, with PowerCenter scoring 8.5 and NiFi at 9.3. However, reviewers mention that NiFi's real-time data flow monitoring and reporting features are particularly advantageous for dynamic data environments.
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
- GUI based Power Data Integrator - Best in class with tight integration with industry leading databases like Oracle & Teradata - Always leading with cutting edge features like JSON webservice support, CDC, Cloud, Data Virtualization
Adaptability to cloud and big data is slow
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...
- GUI based Power Data Integrator - Best in class with tight integration with industry leading databases like Oracle & Teradata - Always leading with cutting edge features like JSON webservice support, CDC, Cloud, Data Virtualization
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
Adaptability to cloud and big data is slow
