Apache NiFi Reviews

Name: Apache NiFi
Brand: Apache
Rating: 3.9 (11 reviews)

3.9 out of 5

11 reviews
90% willing to recommend

372 followers

What is Apache NiFi?

Apache NiFi is an easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.

Get the Apache NiFi Buyer's Guide and find out what your peers are saying about Apache NiFi, Apache Spark, AWS Lambda and more!

Apache NiFi is the #8 ranked solution in top Compute Service solutions. PeerSpot users give Apache NiFi an average rating of 7.8 out of 10. Apache NiFi is most commonly compared to Apache Spark: Apache NiFi vs Apache Spark. Apache NiFi is popular among the large enterprise segment, accounting for 68% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a financial services firm, accounting for 19% of all views.

Helped 816,406 peers since 2012

Featured reviews

SabinaZeynalova

Data Engineer Team Lead at Unibank

Apache NiFi is slow to control and needs to be improved. I have to run many jobs and there are already large tables, which can make it difficult to control NiFi on time. There is no one to tell me when there is an incident and my server is down. When we manually start the NiFi process, it is not always started correctly. We can write scripts to run when a message is received from Airflow saying that the firewall is not running. This script will automatically start all servers, including the application servers. It will also check the status of all my NiFi processes and send a callback message with the results. I have written down all the processes that are monitored. We run many jobs, and there are already large tables. When we do not control NiFi on time, all reports fail for the day. So it's pretty slow to control, and it has to be improved. In future releases, there are extra features I’d like to add. For example, NiFi is not suitable for migration, and the replication in NiFi is really not good. Because when you process ten years of data, you can't manage all the transactions; it is not enough. Moreover, the handling of monthly transactions is not enough due to a lack of partitioning for dates. And, when we grade a monthly ticket, we must process all data then rerun our ETL jobs. If it's possible, enhancing the partitioning in NiFi for features would be beneficial.

Read full review

Bruno_Silva

Partner at Timestamp, SA

The use case templates could be more precise to typical business needs. Available templates and model workflows are very high-level so don't really match real needs. It would help to have templates that allow us to see business opportunities. It would help to be able to copy workflow to another device rather than having to ingest it.

Read full review

Arjun Pandey

Engineering Lead- Cloud and Platform Architecture at a financial services firm with 1,001-5,000 employees

The good thing about Apache NiFi is that it has a concept called a flow file, and there's something called a flow file processor. The processor is the building block of your entire job. They have close to 500 processors for each purpose. For example, for reading from Kafka, Ni-Fi has a processor called "consumer Kafka". To write to S3, they have a processor called "put S3". Now, if I read from Kafka and write my own application, I'd need to ensure the library I'm using tracks my messages. I'd also need to handle any failures by rereading messages and ensuring acknowledgment. But all this complexity is already handled by Apache processor. They have around 500 processors, with a community investing significant effort into developing them. I can design your processor with a single click, export the entire workflow, and import it. The format is actionable, so NiFi is immediately set up. It's also distributed in nature so that I can scale it across nodes based on the workload. These nodes share their state. If one node goes down during processing, that data might be lost, but any subsequent data is safe. Such occurrences are rare. In essence, if you want a quick solution, Apache NiFi is a strong contender. There are other solutions like AirFlow and some paid pipeline options. AirFlow is open-source but can be complicated. For ETL or ERT solutions, there are pricier options. But if I need a pipeline that I can monitor step by step, Apache NiFi is a good choice. It integrates with Prometheus metrics, allowing me to embed them in my workflow. There's also a processor for integration with Slack, and I can receive notifications when the workflow is completed or fails. Another feature I appreciate is "back pressure," which NiFi handles automatically. It maintains its own queue and addresses back-pressure issues. If, for instance, an upstream entity isn't fast enough, items get stored in a queue, managed internally by NiFi's back pressure algorithm.

Read full review

Apache NiFi mindshare

As of November 2024, the mindshare of Apache NiFi in the Compute Service category stands at 8.0%, up from 5.9% compared to the previous year, according to calculations based on PeerSpot user engagement data.

Compute Service

Key learnings from peers

Valuable Features

"Apache NiFi is user-friendly. Its most valuable features for handling large volumes of data include its multitude of integrated endpoints and clients and the ability to create cron jobs to run tasks at regular intervals."
"The initial setup is very easy."
"The initial setup is very easy. I would rate my experience with the initial setup a ten out of ten, where one point is difficult, and ten points are easy."

Room for Improvement

"The tool should incorporate more tutorials for advanced use cases. It has tutorials for simple use cases."
"There is room for improvement in integration with SSO. For example, NiFi does not have any integration with SSO. And if I want to give some kind of rollback access control across the organization. That is not possible."
"We run many jobs, and there are already large tables. When we do not control NiFi on time, all reports fail for the day. So it's pretty slow to control, and it has to be improved."