Apache NiFi Reviews

I use NiFi as a tool for ETL, which stands for extract, transform, and load. It is particularly effective for integration methodologies. 

The tool is useful for designing ETL pipelines and is an open-source product. Data is often stored in different forms and locations. If I want to integrate and transform it, NiFi can help load data from one place to another while making transformations. 

I can handle stream or batch data and identify various data types on different platforms. NiFi can integrate with tools like Slack and perform required transformations before loading to the desired downstream. 

It is primarily a pipeline-building tool with a graphical UI, however, I can also write custom JARs for specific functions. NiFi is an open-source tool effective for data migration and transformations, helping improve data quality from various sources.

NiFi works on data and file levels, streamlining real-time data processes. It is highly effective for handling real-time data by working with APIs for immediate and continuous data extraction. For real-time data tasks, this front-end UI-based tool is superior to back-end platforms.

There are some areas for improvement, particularly with record-level tasks that take a bit of time. The quality of JSON data processing could be improved, as JSON workloads require manual conversions without a specific process. 

Enhancing features related to alerting would be helpful, including mobile alerts for pipeline issues. Integration with mobile devices for error alerts would simplify information delivery.

The product is stable for simple tasks, like using databases that are not distributed. However, for distributed environments like Hadoop or HBase, some vulnerabilities exist. While these are not major issues, they should not be ignored.

Scaling works well, allowing cluster expansion. However, I have never encountered very large clusters, so it's uncertain how well it supports extensive scaling.

The initial setup is fast, especially for communication stabilization. Although the product is open source, it functions as a cluster. For single-node environments, installation is simple. For company-wide or enterprise-level clusters, the initial stages may present issues with authentication and access. Stabilization, such as port communication, may not be immediately effective.

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.

On-premises

As a DevOps engineer, my day-to-day task is to move files from one location to another, doing some transformation along the way. For example, I might pull messages from Kafka and put them into S3 buckets. Or I might move data from a GCS bucket to another location. 

NiFi is really good for this because it has very good monitoring and metrics capabilities. When I design a pipeline in NiFi, I can see how much data is being processed, where it is at each stage, and what the total throughput is.

I can see all the metrics related to the complete pipeline. So, I personally like it very much.

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.

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. 

So I have to create a separate username and password, and then I have to share it with the individual team. So, that is the pain point to be at the enterprise level.

I have been using it for one and a half years. 

I would rate the stability a seven out of ten because there are a lot of processes that need to be implemented.  

It's scalable. It can easily scale on multiple nodes. Depending on the workload, it also handles that internally; like the workers, they coordinate with each other, and they share the workload with each other. So, it's pretty good in terms of scalability.

The initial setup is very easy, especially for users who are familiar with EDL or EMT. 

NiFi is one of the easiest tools on the market to learn and use. It is also a quick-win solution, which is good for first-time users who are developing data pipelines for EMT. NiFi makes it easy to track and trace the status of your pipelines, so you can be sure that they are working properly.

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.

Public Cloud

Our company uses the solution to ingest raw data. We have five repositories with a huge amount of data. We normalize the data to previously structured files, prepare it, and ingest it to devices. 

The size of any project team depends on the workflow or management activities but typically includes two to five users.

The user interface is good and makes it easy to design very popular workflows. 

There are nice parameters for migration.

It is very easy to schedule jobs that realize improvements and monetize.

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. 

I have been using the solution for five years. 

The stability is good so is rated an eight out of ten. 

The scalability can be a little bit limited in the 2020 version so is rated a seven out of ten. 

We contacted technical support by phone once and they were helpful, but there was some delay with their response. We would have preferred a quicker response time. 

After a bit of study, we can support most of our needs so don't really need technical support. 

The setup is very simple. We use a VM on our private cloud and install the solution on that VM. 

We implemented the solution in-house. We are a simpler, nonproductive environment and our implementation took about two hours. 

Implementation complexity and time depend on the use case. A production environment might take a bit longer to implement because there might be cluster situations. Environments with many rules or policies could also take longer to implement. 

One person with the right skills and knowledge can handle ongoing maintenance. 

I rate the solution an eight out of ten.

Private Cloud

Amazon Web Services (AWS)

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

One example is how Apache NiFi has helped us to create data pipelines to migrate data from Oracle to Postgres, Oracle to Oracle, Oracle to Minio, or other databases, such as relational databases, NoSQL databases, or object storage. We create templates for these pipelines so that we can easily reuse them for different data migration projects.

For example, we have a template for migrating data from Oracle to Postgres. This template uses an incremental load process. The template also checks the source and destination databases for compatibility and makes any necessary data transformations.

If our data is not more than ten terabytes, then NiFi is mostly used. But for a heavy table setup, I don't use NiFi for customers or enterprise solutions.

I use custom functions for specific features in Apache NiFi. I also use the processes available in NiFi. I can write custom functions to achieve the desired functionality, even if it is not explicitly available as a built-in NiFi feature.

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.

I have been working with Apache NiFi for one year. 

I would rate the stability an eight out of ten. 

I would rate the scalability a five out of ten because, in our experience, it doesn't scale correctly, especially if you don't use a Kubernetes system. 

If you want it to be scalable, you must use Kubernetes, but in our system, it's in VM and VM disc—external and not external. Increasing disc space is a very hard process. NiFi is not easily scalable. You can increase, but decreasing is not possible. So, it is easy to scale up, but scaling down is difficult.

There are around ten end users in our company. We plan to increase the further usage. 

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.

But if you want its custom mode and control, it's five out of ten. 

For the initial setup, if you configure to custom mode, it's five points. But if you use its single-mode configuration and installation, it's ten.

The deployment takes one week due to network access and some VM installation. Then, we install NiFi and deploy it. But, if you have all the scripts written automatically, it’s five minutes for us.

One person is enough for the deployment process. It's all about script writing in CAC, and it's one-button quick for deployment.

I am using it open source, so it means it's free for me to use. 

If the volume is manageable, I would recommend it. Overall, I would rate the solution a six out of ten. 

On-premises

We use the tool to transfer data from one service to another. It helps us to migrate data from one department to another. 

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 tool should incorporate more tutorials for advanced use cases. It has tutorials for simple use cases. 

I rate the tool's stability an eight out of ten.

I have relied on the documentation available on Apache NiFi's website for support. 

I tried to install the tool on my work laptop, and while it worked initially, it started to run slowly after some time. The department that handles the company's databases uses Apache NiFi on proper servers. I tried using it on my laptop to see if it worked, but it ran very slowly and consumed many resources from my machine.

I used the tool's free version.

I rate Apache NiFi an eight out of ten. 

We use the solution for data streaming.

We use the tool to stream live data. The end users can see the real-time data.

We can integrate the tool with other applications easily.

More features must be added to the product. As compared to Kafka, the tool must be improved.

I have been using the solution for two years. I am using the version that was released before the latest version.

The solution’s stability is good. I rate the stability a seven out of ten.

More than 100 people are using the solution in our organization. We can scale the tool easily. I rate the scalability a ten out of ten.

We find most of the solutions to our issues on the internet. We didn’t have to approach the technical support team.

The initial setup was straightforward. We can deploy the tool easily on a single node. It won’t take much time. If it is a multi-node cluster, it will take two to three hours.

We need two engineers to maintain the solution. These engineers maintain other solutions in our organization, too.

The solution is open-source.

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.

On-premises

Apache NiFi is used to orchestrate ingestion processes. For example, Apache NiFi ingests data from external sources such as external databases or external APIs. Custom transformation is then applied, and data is written inside the data lake.

Apache NiFi speeds up ingestion pipelines development. Ingestion pipelines that usually took a week to develop can now be developed in a couple of days.

Apache NiFi has extensive integration capabilities and integrates with many sources. It supports custom transformations, making it a very flexible tool that can be leveraged to perform most computation needs.

For transformation with Apache NiFi, JSONs are processed and denormalized to map information onto different tables. For source integration, the most valuable aspect was the ingestion from external APIs.

Apache NiFi is a very good tool, but there is room for improvement.

Apache NiFi has been used on different projects for a couple of years.

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.