Compare Apache NiFi and Apache Sqoop 
- Users report that Apache NiFi excels in data flow automation with its intuitive user interface, allowing for easy creation and management of data pipelines, while Apache Sqoop is noted for its robust capabilities in efficiently transferring large volumes of data between Hadoop and relational databases.
- Reviewers mention that Apache NiFi's extraction capabilities score highly at 9.5, with features like the "GetHTTP" processor enabling seamless data ingestion from various sources, whereas Apache Sqoop, while strong in loading data, has a lower score of 8.8, indicating some limitations in its loading flexibility.
- G2 users highlight that Apache NiFi's transformation features are rated at 9.3, with users appreciating the "UpdateAttribute" processor for modifying data attributes on-the-fly, contrasting with Apache Sqoop's transformation capabilities, which are less emphasized and not as versatile.
- Reviewers say that Apache NiFi's scalability is rated at 8.3, which some users find limiting for extremely large datasets, while Apache Sqoop's ease of setup and administration is rated higher at 9.2, making it a preferred choice for users looking for straightforward integration with existing systems.
- Users on G2 report that the quality of support for Apache Sqoop is rated at 8.5, with many users appreciating the community-driven support, while Apache NiFi's support is rated lower at 7.5, indicating potential challenges in finding timely assistance.
- Reviewers mention that Apache NiFi's product direction is rated at 7.5, suggesting some uncertainty about future updates, while Apache Sqoop boasts a perfect score of 10.0 in this area, indicating strong confidence among users regarding its ongoing development and enhancements.
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
Using sqoop on the command line has provided me a quick and easy way to transfer data into sandbox databases or data lakes. This has made it easier for people in Analytics roles like myself to get the data sources together quickly to create models using...
Not very effective with de-normalized data as we need to execute multiple joins in map reduce.
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...
Using sqoop on the command line has provided me a quick and easy way to transfer data into sandbox databases or data lakes. This has made it easier for people in Analytics roles like myself to get the data sources together quickly to create models using...
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
Not very effective with de-normalized data as we need to execute multiple joins in map reduce.
