The customizability of Apache NiFi helps even with unique use cases, as I mentioned before, given that Apache NiFi can be used in this capacity. While there are better applications or software options available, when you are trying to keep it simple and finding ways to utilize a couple of processors for a unique solution, you can do that in Apache NiFi. For example, we have several notification-type pipelines we have built in Apache NiFi, such as reading from a SQL database to identify users who have not completed training and then sending them an email reminder to complete that training. We have that running regularly, week by week. Another instance involves a processing data flow that scans for specific data found in logs, which triggers an email notification to the relevant team letting them know that a unique identifier has appeared, allowing them to handle the situation. I encountered some odd cases such as increasing concurrent threads on a processor, which should work similarly to copying several processors, yet functional throughput varies. It seems that using a distributed processor yields better throughput than just increasing the concurrent threads on one processor, which has been odd but is a workaround we had to adopt to boost throughput. Resolving such quirks could elevate the rating further. I rate Apache NiFi an eight out of ten. I choose eight because, as open-source software, there is always room for improvement, but the tradeoff between learning how to use the software and the savings it provides, along with its customizability, ranks it pretty high. It is effective for what it does and continues to improve, so it could score higher if there are significant enhancements in custom-built processors and ongoing improvements in functionality.
On a scale of one to ten, I would rate Apache NiFi an eight. I chose eight out of ten because there is scope for improvement; in this age, folks want more automation, which is why I kept it at eight. My advice for others considering using Apache NiFi is that it is a good tool, but there are also other options to consider before making a decision. I gave this review an overall rating of eight out of ten.
Apache NiFi should be considered if a scalable and flexible tool is needed for building ETL pipelines and reducing time to production. This review has a rating of 8.
Apache NiFi receives a rating of 9 out of 10. This rating of 9 out of 10 for Apache NiFi was chosen because of the documentation and the support of the product. The advice for others looking into using Apache NiFi is to test the solution with a POC and then go to production in a quick way.
I recommend the product for its data privacy features. It allows secure data handling because the data is stored on my nodes. However, a skilled technician is necessary due to the reliance on Java, especially for back-end operations and error debugging. Enterprise versions may offer easier troubleshooting. As an open-source solution, good support is crucial. I rate the overall product as eight out of ten.
Engineering Lead- Cloud and Platform Architecture at a financial services firm with 1,001-5,000 employees
Real User
Oct 25, 2023
If I were to advise someone, I would ask the user what endpoints they want to touch. If I want to read something from Kafka and I want to put this thing on the S3 bucket, what is the alternative I have? I have Kafka Connect, where I can connect Kafka with one Kafka, and I can put it into an S3 bucket. Is this scalable? No. Is this monitoring No. We can't monitor it. We can't scale it. It's going to be a complete black box. The person who knows Kafka Connect, or Kafka, can understand what is happening there while using Kafka Connect. But if I compare it, I literally don't need to understand what Kafka is. I know, "Okay, this is Kafka. These are the endpoints, and this is the URL I have to point to." That's it. My job is done. I will create a complete flow pipeline within, let's say, thirty minutes or something without having any current knowledge. I can read, I can Google it, and I can just implement it. For people who are new to big data technologies like Kafka and BigQuery, I would give this solution an eight out of ten. Let's say you need to build a solution to read from Kafka and write to an S3 bucket. You could use Kafka Connect, but if your requirements change and you need to start reading from a database instead, Kafka Connect will not work. With Apache NiFi, you can easily modify your flow pipeline to start reading from the database instead.
The solution must be improved to compete with Kafka. As it is an open-source tool, it will take time to get all the functions. I would recommend the product to others. Overall, I rate the product a seven out of ten.
The architect needs to evaluate the entire architecture with this platform so eventually, we are left with our architects and we need to get approval from them to do that. I would rate this solution an eight out of ten.
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.
The customizability of Apache NiFi helps even with unique use cases, as I mentioned before, given that Apache NiFi can be used in this capacity. While there are better applications or software options available, when you are trying to keep it simple and finding ways to utilize a couple of processors for a unique solution, you can do that in Apache NiFi. For example, we have several notification-type pipelines we have built in Apache NiFi, such as reading from a SQL database to identify users who have not completed training and then sending them an email reminder to complete that training. We have that running regularly, week by week. Another instance involves a processing data flow that scans for specific data found in logs, which triggers an email notification to the relevant team letting them know that a unique identifier has appeared, allowing them to handle the situation. I encountered some odd cases such as increasing concurrent threads on a processor, which should work similarly to copying several processors, yet functional throughput varies. It seems that using a distributed processor yields better throughput than just increasing the concurrent threads on one processor, which has been odd but is a workaround we had to adopt to boost throughput. Resolving such quirks could elevate the rating further. I rate Apache NiFi an eight out of ten. I choose eight because, as open-source software, there is always room for improvement, but the tradeoff between learning how to use the software and the savings it provides, along with its customizability, ranks it pretty high. It is effective for what it does and continues to improve, so it could score higher if there are significant enhancements in custom-built processors and ongoing improvements in functionality.
On a scale of one to ten, I would rate Apache NiFi an eight. I chose eight out of ten because there is scope for improvement; in this age, folks want more automation, which is why I kept it at eight. My advice for others considering using Apache NiFi is that it is a good tool, but there are also other options to consider before making a decision. I gave this review an overall rating of eight out of ten.
Apache NiFi should be considered if a scalable and flexible tool is needed for building ETL pipelines and reducing time to production. This review has a rating of 8.
Apache NiFi receives a rating of 9 out of 10. This rating of 9 out of 10 for Apache NiFi was chosen because of the documentation and the support of the product. The advice for others looking into using Apache NiFi is to test the solution with a POC and then go to production in a quick way.
Overall, I rate Apache NiFi an eight out of ten. I am quite happy with it.
I recommend the product for its data privacy features. It allows secure data handling because the data is stored on my nodes. However, a skilled technician is necessary due to the reliance on Java, especially for back-end operations and error debugging. Enterprise versions may offer easier troubleshooting. As an open-source solution, good support is crucial. I rate the overall product as eight out of ten.
I rate Apache NiFi an eight out of ten.
If I were to advise someone, I would ask the user what endpoints they want to touch. If I want to read something from Kafka and I want to put this thing on the S3 bucket, what is the alternative I have? I have Kafka Connect, where I can connect Kafka with one Kafka, and I can put it into an S3 bucket. Is this scalable? No. Is this monitoring No. We can't monitor it. We can't scale it. It's going to be a complete black box. The person who knows Kafka Connect, or Kafka, can understand what is happening there while using Kafka Connect. But if I compare it, I literally don't need to understand what Kafka is. I know, "Okay, this is Kafka. These are the endpoints, and this is the URL I have to point to." That's it. My job is done. I will create a complete flow pipeline within, let's say, thirty minutes or something without having any current knowledge. I can read, I can Google it, and I can just implement it. For people who are new to big data technologies like Kafka and BigQuery, I would give this solution an eight out of ten. Let's say you need to build a solution to read from Kafka and write to an S3 bucket. You could use Kafka Connect, but if your requirements change and you need to start reading from a database instead, Kafka Connect will not work. With Apache NiFi, you can easily modify your flow pipeline to start reading from the database instead.
If the volume is manageable, I would recommend it. Overall, I would rate the solution a six out of ten.
The solution must be improved to compete with Kafka. As it is an open-source tool, it will take time to get all the functions. I would recommend the product to others. Overall, I rate the product a seven out of ten.
I rate the solution an eight out of ten.
I would rate this solution an eight out of ten.
There are some claims that NiFi is cloud-native but we have tested it, and it's not. I would rate this solution a seven out of ten.
The architect needs to evaluate the entire architecture with this platform so eventually, we are left with our architects and we need to get approval from them to do that. I would rate this solution an eight out of ten.