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- Apache NiFi-vs-Informatica Data Engineering
Compare Apache NiFi and Informatica Data Engineering 
- 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 drag-and-drop interface simplifies the process of building data flows.
- Reviewers mention that Informatica Data Engineering shines in its transformation functionalities, scoring 9.3, which is particularly beneficial for complex data manipulation tasks. Users on G2 appreciate the robust set of transformation functions available, making it easier to handle intricate data workflows.
- G2 users highlight that Apache NiFi's automation features, rated at 9.3, enable users to set up automated data flows efficiently. Users say that the ability to schedule and trigger data flows based on events is a significant advantage for real-time data processing.
- Users report that Informatica Data Engineering has a higher quality of support, with a score of 8.6, compared to Apache NiFi's 7.5. Reviewers mention that the support team is responsive and knowledgeable, which is crucial for enterprises relying on timely assistance.
- Reviewers mention that Apache NiFi's scalability, rated at 8.3, can be a limitation for larger enterprises, as some users have experienced challenges when scaling up their data flows. In contrast, users on G2 report that Informatica Data Engineering handles large-scale data operations more effectively.
- Users say that while both products have strong reporting capabilities, Apache NiFi's reporting score of 9.3 is particularly praised for its real-time monitoring features, allowing users to visualize data flows and performance metrics easily, which is essential for operational oversight.
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
Informatica is simple, fast, easy to learn integrate and get started. I have liked the inbuilt functions which made the job really easy and saved a lot of manual effort
It's slow and sometimes having more clarity is needed
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...
Informatica is simple, fast, easy to learn integrate and get started. I have liked the inbuilt functions which made the job really easy and saved a lot of manual effort
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
It's slow and sometimes having more clarity is needed
