Compare Airbyte and Apache NiFi 
- Reviewers mention that Apache NiFi excels in its extraction capabilities, scoring 9.5, which users attribute to its robust data flow management and the ability to handle diverse data sources seamlessly.
- Users report that Airbyte shines in its loading functionality, achieving a score of 9.0, with many highlighting its ease of integration with various destinations, making data loading straightforward and efficient.
- G2 users indicate that Apache NiFi's transformation features are strong, with a score of 9.3, but some reviewers express that the learning curve can be steep, which may hinder new users from fully leveraging its capabilities.
- Users on G2 mention that Airbyte's transformation capabilities are less robust, scoring only 7.2, with feedback suggesting that it lacks some advanced transformation features that users find in Apache NiFi.
- Reviewers say that Apache NiFi's automation features are highly rated at 9.3, with users appreciating its ability to automate complex workflows, which enhances productivity and reduces manual intervention.
- Users report that while Airbyte has a solid overall score for ease of use at 9.0, some find that its user interface could be more intuitive, especially for those unfamiliar with data integration tools.
- Full control of infrastructure setup
- 600+ Connectors
- Core Reporting
Airbyte provides a straightforward way to ingest data from different sources into our data warehouse. The biggest benefit is it allows us to skip writing custom ETL code for each data source.
1. Usage is not very transparent it goes up and down without explaination 2. The schema created in the destination is not very easy to read.
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
Airbyte provides a straightforward way to ingest data from different sources into our data warehouse. The biggest benefit is it allows us to skip writing custom ETL code for each data source.
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...
1. Usage is not very transparent it goes up and down without explaination 2. The schema created in the destination is not very easy to read.
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
