Apache NiFi Vs Apache Kafka Comparison: Features & Functionalities

When it comes to data migration tools, you will find Apache NiFi and Apache Kafka at the top of the ladder. Since two tools are at the top, comparisons are bound to happen. Although Apache NiFi Vs Kafka overlap each other in terms of usability, NiFi might carry an edge over Kafka. NiFi and Kafka have different sets of functions, use cases, architecture, and benefits. To answer when should one use Apache NiFi as opposed to Kafka, we will unravel the functions and limitations of both! 

As for now, let’s start with the introduction of both tools and then compare Apache NiFi and Apache Kafka based on their features or functionalities.

What Is Apache NiFi?

Apache NiFi is a data flow framework capable of moving data from one source to another. Apache NiFi is well-known for its effortless flow of data with enhanced security features. If you are an organization, you can simply transfer your company’s essential data to the destination with NiFi. 

The encrypted platform of the NiFi data flow pipeline will allow an extra layer of security for the data. Moreover, the tool will ensure data retention at all costs. 

Features of Apache NiFi as of 2024:

• Improvement For Business

The latest version of Apache NiFi comes with the ability to improve the data flow and user experience. This feature makes the functioning smoother for individuals to design, monitor, and manage complex data flows. However, the interactive interface of NiFi works on better utilization of resources that allows users to instantly create and modify the data pipelines. 

Not only this, but it also tracks the entire data movement and identifies the issues immediately. This latest update works on optimizing the entire data flow process which reduces the time and effort for efficient data operations. 

• Security Enhancements

For maintaining better performance and data security, Apache NiFi comes with a robust security enhancement in 2024. This framework works on protecting the data at rest and in transit, this ensures that the crucial information remains safe throughout the data lifecycle process. In addition, Apache NiFi’s role-based access control system provides granular permission management that defines the access levels for different users. 

This makes sure that only authorized users can access and modify the critical data flows with security updates. 

• Boost Business Performance 

For the data integration process, performance is considered a key factor, and in this area, Apache NiFi comes as a framework to deliver the significant part. The latest version of NiFi is capable of handling a massive amount of data with greater efficiency. Whether we talk about dealing with high-level data streams or complex tasks, Apache NiFi handles the demands of an organization with its performance capabilities. It has faster data processing time and provides real-time data analytics for improving business efficiency.

Also Read: Apache NiFi Benefits For Businesses

• Cloud Integration

Since all organizations are migrating themselves to the cloud, there comes Apache NiFi adapts to meet all business needs with its cloud integration features. As of 2024, Apache NiFi is working on improving the support for cloud and hybrid deployments that make it easier to deploy, manage, and scale the data flows across different verticals. Whether an individual is utilizing the private cloud, public cloud, or a hybrid approach, NiFi’s integration capability makes sure that things are consistent and reliable.

What Is Apache Kafka?

Apache Kafka is an open-source real-time data streaming platform used by a plethora of small and big companies all around the world. With Java and Scala in the store, Kafka lays out a platform for easy coding. Along with real-time streaming in Kafka, the best part about Kafka is that you can store as much data as you want and edit it later. For this, you’ve got Kafka clusters at the input and output source of the platform. The involvement of standard languages makes it an easy-to-use tool for developers.

Read More: Why Ksolves Prefer Apache Kafka for ETL?

What’s New in Apache Kafka as of 2024?

• Latest Innovation Upgrades

With the ground-breaking enhancements of Apache Kafka architecture and its streaming capabilities, it evolves properly. The latest version of Kafka streams improves the business scalability and performance by introducing the latest advancements. These business enhancements guarantee the real-time processing of data which makes it more effective and simpler to develop and implement stream processing apps. 

• Security Updates

When we talk about growing the security of the organization, then utilizing Apache Kafka is considered the best approach. Kafka is capable of implementing new encryption methods to safeguard the data both in transit and at rest. These enhancements make sure that the crucial information remains protected as data will flow from Kafka pipelines. 

It also includes additional compliance features that make it easier for enterprises to meet industry standards and regulations. These security updates provide stronger data protection maintain regulatory compliance and reduce the risk of legal issues. 

• Kafka Management Tools

Apache Kafka clusters’ effective management and monitoring are important for maintaining high performance and reliability. The upgraded Kafka version introduces improved metrics and management tools that offer proper insights into cluster performance and health. The enhanced Kafka metrics help admins monitor the multiple aspects of Kafka operations such as resource utilization. In addition, Kafka’s error-handling mechanisms simplify the management process and quickly resolve the issues. 

• Kafka Stream Processing 

When we talk about Kafka streams, it improves performance and scalability and works on handling massive amounts of data volumes. With Kafka’s new features and optimizations, it is easier to develop and deploy complex stream processing applications. These enhancements include better integration with Kafka streams to expand SQL functionality and improve performance.

Dive Deeper into Apache NiFi: Webinar on Simplifying Data Workflows

If you’re looking to understand how Apache NiFi can simplify data workflows and enhance your data processing strategy, we have an insightful webinar hosted by our team of experts. This session covers how NiFi’s web-based user interface and its built-in processors and connectors make it an ideal tool for managing diverse data ingestion sources.

Watch the webinar to explore:

• How NiFi enables real-time data processing for large-scale data flows.
• Data provenance tracking to ensure full visibility into the data journey.
• Seamless data flow management, simplifying workflows even with complex data pipelines.

Watch the Webinar video here:

This in-depth exploration of NiFi’s features and benefits will help you see why it’s an essential tool for streamlining data workflows, especially when compared to alternatives like Apache Kafka.

Apache NiFi and Apache Kafka Comparison 

Must Read: Comparison of Apache NiFi vs Apache Airflow

Functionalities of Apache NiFi Vs Apache Spark

Starting with the Apache NiFi, here are the functions of the platform.

1. NiFi stays strong even if the scale of the data is huge while providing guaranteed delivery to the destination. Most importantly, NiFi won’t take much time to transfer the data with its lightning-fast speed. 
2. With NiFi, you can pull out the queued data at any instance. There are times when the data is not required and sending it might hamper the security of your organization. This is where Apache NiFi provides this added layer of security for the users. 
3. You can sort the data according to your preference as NiFi allows prioritization. The feature will help you during emergency tasks. In normal conditions, data is sorted in the oldest-first loop but you change it accordingly. 
4. Once you have sent the data, you can modify it according to the flow. NiFi offers the visual representation of your data flow, so you can rectify it at the same time. 
5. Scalability is the best feature of Apache NiFi. With this feature, you can induce a larger amount of data in comparison to any other induction platform. 

Hopping on to the features of Apache Kafka, here are some of them. 

1. Kafka is well-known for its throughput speed, which can shoot messages at 2 ms. Therefore, the processing time is low and you can enjoy the high output. 
2. The platform allows you to store as much data as you want and process it later. Now, you don’t need to worry about the data with Kafka due to its durability and enhanced Kafka security. 
3. Pick any event of your choice and connect through Kafka! Indeed, the open source allows connection with hundreds of event sinks. 
4. Kafka can handle petabytes of data in one go! You can expand and contract the storage according to your accessibility at any instance.   
5. Stream processing applications in Kafka use state stores to store and query data provided by Kafka Streams.
6. Any stream processing application in Kafka can take advantage of the Kafka Streams library.

Limitations of Apache NiFi Vs Apache Kafka

We are done with the positive side of both tools! Therefore, let’s switch over to the limitations of the two. Starting with the Apache NiFi, here are the limitations:. 

1. Once the node is detached from the cluster, it’s difficult to fetch the data from that node. Although you can manually copy the data from that node, it will occupy your time and energy. However, if you are on the receiver end, you cannot find the data from the detached node until the admin follows the above procedure. 
2. Another limitation is the state persistence issue of the Apache NiFi. The issue makes it difficult for the source to fetch the data. If you experience an issue in the first node, you can expect the impact in other nodes, n as well. 

With these two limitations, NiFi presents a strong case for itself. However, it would be hollow to comment before considering the limitations of Apache Kafka. So, let’s move on to that. 

1. The lack of management and monitoring tools makes Kafka difficult for new users. Although there are a few tools available for the users, the incomplete set creates difficulty in error handling in Kafka for startups. 
2. In case you require to change the message while sending, Kafka will create issues. You can use the system calls, but that will kill the atmosphere of switching to Kafka initially. 
3. Constant compressing and decompressing of the data affect the throughput value of the software. Apache Kafka is well-known for its throughput speed, as we’ve already discussed. If you fail to retrieve a similar speed, the functionality of the platform demeans itself. 
4. As you continue to increase the queues in the cluster, Kafka will initiate a clumsy behavior.  

Wrapping Up

You might have noticed that the comparison of Apache NiFi Vs Apache Kafka turns to one direction at some points, while the other in the rest. With throughput speed, data alteration, and data compressions, Apache NiFi carries an edge over Kafka. So, when you are looking for lightning speed, data modulation, and enhanced security, you should opt for Apache NiFi as opposed to Kafka. 

If you are an organization and looking for the best Apache NiFi development company as well as an Apache Kafka development company, Ksolves is always a call away! 

With our expertise in the field backed by experienced developers, Ksolves is rated as one of India’s best Apache NiFi consulting companies and an expert at Kafka error handling. We have efficient NiFi and Kafka monitoring setup tools, follow efficient security measures, and implement the best strategies for the successful integration of these software platforms. Just a reminder, we are one of the best Apache Kafka consulting companies in India as well. So, feel free to contact us for more information regarding any solution.

Integrating Apache Cassandra with NiFi and Kafka for Optimal Big Data Solutions

When comparing Apache NiFi and Apache Kafka, both offer robust solutions for managing real-time data streams. However, effectively storing and managing this data is equally crucial for long-term analytics and scalability. That’s where Apache Cassandra comes in, offering a highly scalable and fault-tolerant database solution ideal for big data applications.

At Ksolves, our team of Datastax-certified Cassandra experts specializes in building custom Cassandra solutions tailored to your needs. With over 50 skilled developers and 12+ years of experience, we can help integrate Cassandra into your NiFi or Kafka pipelines, ensuring seamless data management and reliability.

Watch this video to learn more about how our Apache Cassandra development services can help optimize your big data infrastructure: