Apache NiFi Reviews

The primary use case is to collect data from different source systems. This includes different soft types of files, such as text files, bin files, and CSV files, to name a few. It is also used for API training.

It works for a large amount of data. It is oriented for endpoint solutions and high and low frequency in small packets of data, for example, files. It can also work well when integrated with Spark, they are complementary in some use cases. At times we work only with Nifi, at times only with Spark and other times when they are integrated.

The most valuable features of this solution are ease of use and implementation. It is very visual. We can orchestrate all of the data flow right on the user interface.

We can connect each processor that we design and we can make it quicker with faster development. This is an advantage in terms of development.

Regarding the use, it is very powerful because it scales easily based on the number of workers and nodes that you have. Also, the expiration based on the computation is very easy to configure.

There should be a better way to integrate a development environment with local tools. Most of the development is done on the console. For example, in Spark, we can develop on our local desktop and then deploy it to another environment. The integration using this solution is not simple.

Eventually, we can implement a local environment on our machines that is web-oriented and we have a browsing console to do it.

At times, it is not easy to integrate with other components that could be a part of the entire solution in terms of development.

There are issues with stability due to memory. It would be good to include a lock or an alarm to detect or alert you that it needs more resources.

If the solution could be integrated more it would repair every part of the flow. Eventually, if we are integrated with other technology, we need to have a notion that we need to plan and have the correct sizing. We could implement an ecosystem that could scale with the requirements.

In the next release, I would like to see the support of monitorization with the interface. Also an integrated development environment.

I have been working with Apache NiFi for a year and a half.

It's very stable if used properly. If we use it the way it is intended then it should work fine, it's a good solution.

There can be some issues with memory. If there were mechanisms in place to detect issues beforehand, that there was a need for available resources, it would be much easier.

This solution is very scalable, depending on how the solution is implemented.

We have four or five administrators who manage this solution.

It depends on the frequency of the data source and the use case on often it is being used, but generally, it is used intensively.

Because it is an open-source solution, there are forums and a community that will exchange information.

We used other solutions previously, but this is the best one. It was more stable, it is easier to use and it deploys quickly.

In terms of the setup, the most complex part is if you want to put it on the cloud. On a private cloud, it's tricky. If you set up on-premises, on the other hand, it's easier.

It can take a week or less to deploy the environment. 

We are part of the multinational department and they have their team in place. In my company, we do end to end. If you understand the plan it is easy to install a local environment to develop.

On the other end, if you are a customer then you need other departments to install it. Mainly departments for the infrastructure are required because there are certain requirements that the development team may not have. For example, contacts, security, and setting up hardware.

It's an open-source solution.

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.

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.

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

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.

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.