Apache NiFi Reviews

The web based UI is phenomenal in allowing an easier usage of configurations than Apache Flume. We will see Hortonworks begin to slowly move away from Flume and bring Apache NiFi into their platform. NiFi allows the user to track the data flow from source to destination with a visual representation and statistics at every connection and process along the way.

The installation and configuration of a simple process that moves a file from a local server, copies it into HDFS, and indexes the file in Solr. It took all of 30 minutes at most and each process is started, stopped, deleted with a few clicks.

NiFi has allowed my organization to greatly increase our visualization of data flows. This has subsequently increase our understanding of the entire systematic process from data receiving to endpoint visualization.

Some

I've used it for two months.

No issues were encountered with deployment, just be sure to have the most recent version of Java installed and configured.

The timeline of usage hasn't allowed for a thorough view of the product in order to truly assess any possible stability issues. Scalability was not an issue with our deployment.

Scalability was not an issue with our deployment.

Customer Service:

NiFi is open source through Apache, customer service is at a minimum, but their website offers answers to most questions my team encountered.

Technical Support:

NiFi is open source through Apache, technical support is at a minimum, but their website offers answers to most questions my team encountered.

We were previously using Apache Flume for our data ingestion. Flume was a solid product, but it just did not offer the visual representations and full start-to-end processing that our organization was looking for.

The initial setup is quite straightforward. The install files are quite handy as it allows the user to change which port the web-based UI is run off of so as to not interfere with any other processes that are using the default ports.

We implemented through an in-house team.

Being that this product is open-source, our ROI has been infinite, but since our implementation, our Big Data team have double our clients.

Free setup-cost. Day-to-day is simply the cost to maintain the server it is running on.

Phenomenal product. It is in its early stages, and through further development this product has the potential to be a powerhouse in the Big Data sector of the tech industry.

We are a research institution and use NiFi for its easy Java extensibility, built-in provenance capturing, and graphical web interface.

We are replacing a custom built Java data ingestion system that over time had become difficult to maintain and was brittle.

NiFi allows us to organize our ingestion as the directed graphs and provides a useful GUI, that can be used to configure the system and monitor data flows.

NiFi’s provenance capturing is also a big plus, as our legacy system did not do this sufficiently.

Most of our data is binary and we frequently must write our own processors. Also, there is no support for the stateful operations that require information from other data flows or look-up tables.

I have used this solution for more than one years.

There were no stability issues.

There were no scalability issues.

It is an open-source software, but there is an active and rapidly growing contributor and user base.

We previously used custom code and switched to simplify maintenance and improve our functionality.

The initial setup was very straightforward. NiFi is very easy to install and get running.

It’s free!

We looked at some proprietary solutions and also, evaluated StreamSets. The proprietary solutions were expensive and often didn’t suit our use cases. StreamSets didn’t have the same level of adoption.

Think about your data flows as the directed graphs between low-level processing modules, so you can re-use as much of the path as possible for different data streams. Don’t create entirely separate flows for new data sources, i.e., if you can avoid it.

The most valuable feature has been the range of clients and the range of connectors that we could use. The processing ability of the solution is also really good because it allows high volumes of data to flow in real time. 

Overall, this solution is reliable. 

The overall stability of this solution could be improved. In a future release, we would like to have access to more features that could be used in a parallel way. This would provide more freedom with processing. 

I have been using this solution for one year. 

This is a stable solution although the stability can depend on the process selection and how well we apply for it. 

I have not needed to contact their customer service and support. 

The initial setup is not very complex.

We use the free version of Apache NiFi.

I would rate this solution an eight out of ten.

On-premises

It is really good when it comes to dealing with pipelines.

Visually, this is a good product.

The challenge with Apache NiFi is that it's not cloud-native. This makes it different from our workflow.

The operations are over-complicated and when you build your pipeline, it's a nightmare to follow them. Then, as your pipeline or workflow becomes more complex, the operation of it gets worse. It is not easy to use and it requires a bigger ramp-up than any other solution that we have seen.

We have been using Apache NiFi for between five and six years.

This is a very solid product.

Scaling this product means using a bigger and more powerful machine. If it were cloud-native then it would be different.

We have used similar solutions like Apache Airflow, StreamSets, and others. The ramp-up for NiFi is a lot bigger and you have to spend a lot of time to understand how to use it.

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.

On-premises

I am implementing the ETL workflow using Apache NiFi to prepare data and upload it to the cloud. Our use case involves importing data from on-premise and private servers to build a data hub and data mart. The data mart is then published on the cloud.

We primarily use Apache NiFi for data preparation tasks.

The visual workflow aspect of Apache NiFi is an invaluable feature as it operates on a no-code platform that allows for easy drag-and-drop pipeline construction. Compared to Airflow, which requires programming before visual representation, Apache NiFi offers clarity in pipeline activities. This feature greatly aids in understanding what the pipeline is doing.

The logging system of Apache NiFi needs improvement. It is difficult to debug compared to Airflow, where task details and issues are clear. With Apache NiFi, I have encountered processes that die without any traceable error, which might relate to the inadequate logging system.

I have been working with Apache NiFi for about six months.

Sometimes, when I run Apache NiFi, processes crash without any clue, which might relate to the logging system. The process can die, and the logs do not show any detail to identify the problem, impacting stability.

For scalability, I would rate it an eight. We can run parallel pipelines simultaneously without issues unless memory is full. Scarcity of memory is the only constraint, but processing capabilities allow us to handle much simultaneously.

The technical support from the official Apache team is rated a three out of ten. Issues often require self-resolution or community help, as the support isn't effectively managed.

Negative

I have used Airflow before, which required programming first and then visual representation of the workflow.

There is another team responsible for setting up Apache NiFi, so I'm not involved in the deployment process.

Apache NiFi is open-source and free. Its integration with systems like Cloudera can be expensive, but Apache NiFi itself presents the best pricing as a standalone tool.

Prior to Apache NiFi, I used Airflow, which differed mainly in its approach to programming and workflow visualization.

Overall, I rate Apache NiFi an eight out of ten. I am quite happy with it.

On-premises