Apache NiFi vs Apache Spark comparison

Apache NiFi and Apache Spark are both solutions in the Compute Service category. Apache NiFi is ranked #8 with an average rating of 7.7, while Apache Spark is ranked #5 with an average rating of 8.6. Apache NiFi holds a 8.4% mindshare in CS, compared to Apache Spark’s 11.3% mindshare. Additionally, 92% of Apache NiFi users are willing to recommend the solution, compared to 90% of Apache Spark users who would recommend it.

Apache NiFi

Read 13 Apache NiFi reviews

1,706 Views
764 Comparison Views

92% willing to recommend

Apache Spark

Read 66 Apache Spark reviews

1,415 Views
1,077 Comparison Views

90% willing to recommend

Apache NiFi

Apache Spark

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Executive Summary

We performed a comparison between Apache NiFi and Apache Spark based on real PeerSpot user reviews.

Find out in this report how the two Compute Service solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed Apache NiFi vs. Apache Spark Report (Updated: April 2025).

Buyer's Guide

Apache NiFi vs. Apache Spark

April 2025

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Helped 851,491 peers since 2012

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Categories and Ranking

Apache NiFi

Ranking in Compute Service

8th

Average Rating

7.8

Number of Reviews

Ranking in other categories

No ranking in other categories

Apache Spark

Ranking in Compute Service

5th

Average Rating

8.4

Reviews Sentiment

7.7

Number of Reviews

Ranking in other categories

Hadoop (1st), Java Frameworks (2nd)

Mindshare comparison

As of May 2025, in the Compute Service category, the mindshare of Apache NiFi is 8.4%, up from 7.5% compared to the previous year. The mindshare of Apache Spark is 11.3%, up from 10.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Compute Service

Featured Reviews

Bharghava Raghavendra Beesa

Senior Developer at Infosys

The tool enables effective data transformation and integration

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.

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Ilya Afanasyev

Senior Software Development Engineer at Yahoo!

Reliable, able to expand, and handle large amounts of data well

We use batch processing. It works well with our formats and file versions. There's a lot of functionality. In our pipeline each hour, we make a copy of data from MongoDB, of the changes from MongoDB to some specific file. Each time pipeline copied all of the data, it would do it each time without changes to all of the tables. Tables have a lot of data, and in the last MongoDB version, there is a possibility to read only changed data. This reduced the cost and configuration of the cluster, and we saved about $150,000. The solution is scalable. It's a stable product.

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Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"It's an automated flow, where you can build a flow from source to destination, then do the transformation in between."

"The most valuable features of this solution are ease of use and implementation."

"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 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."

"It is highly effective for handling real-time data by working with APIs for immediate and continuous data extraction."

"The most valuable feature has been the range of clients and the range of connectors that we could use."

"The initial setup is very easy."

"We can integrate the tool with other applications easily."

More Apache NiFi pros

"Spark can handle small to huge data and is suitable for any size of company."

"The product's deployment phase is easy."

"One of Apache Spark's most valuable features is that it supports in-memory processing, the execution of jobs compared to traditional tools is very fast."

"The data processing framework is good."

"Apache Spark provides a very high-quality implementation of distributed data processing."

"The tool's most valuable feature is its speed and efficiency. It's much faster than other tools and excels in parallel data processing. Unlike tools like Python or JavaScript, which may struggle with parallel processing, it allows us to handle large volumes of data with more power easily."

"Spark helps us reduce startup time for our customers and gives a very high ROI in the medium term."

"The most crucial feature for us is the streaming capability. It serves as a fundamental aspect that allows us to exert control over our operations."

More Apache Spark pros

Cons

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

"There are some claims that NiFi is cloud-native but we have tested it, and it's not."

"More features must be added to the product."

"I think the UI interface needs to be more user-friendly."

"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."

"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."

"The quality of JSON data processing could be improved, as JSON workloads require manual conversions without a specific process."

"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."

More Apache NiFi cons

"Stability in terms of API (things were difficult, when transitioning from RDD to DataFrames, then to DataSet)."

"For improvement, I think the tool could make things easier for people who aren't very technical. There's a significant learning curve, and I've seen organizations give up because of it. Making it quicker or easier for non-technical people would be beneficial."

"The solution must improve its performance."

"The product could improve the user interface and make it easier for new users."

"The initial setup was not easy."

"Apart from the restrictions that come with its in-memory implementation. It has been improved significantly up to version 3.0, which is currently in use."

"It should support more programming languages."

"From my perspective, the only thing that needs improvement is the interface, as it was not easily understandable."

More Apache Spark cons

Pricing and Cost Advice

"We use the free version of Apache NiFi."

"I used the tool's free version."

"It's an open-source solution."

"The solution is open-source."

"Licensing costs can vary. For instance, when purchasing a virtual machine, you're asked if you want to take advantage of the hybrid benefit or if you prefer the license costs to be included upfront by the cloud service provider, such as Azure. If you choose the hybrid benefit, it indicates you already possess a license for the operating system and wish to avoid additional charges for that specific VM in Azure. This approach allows for a reduction in licensing costs, charging only for the service and associated resources."

"Considering the product version used in my company, I feel that the tool is not costly since the product is available for free."

"It is an open-source platform. We do not pay for its subscription."

"Since we are using the Apache Spark version, not the data bricks version, it is an Apache license version, the support and resolution of the bug are actually late or delayed. The Apache license is free."

"The product is expensive, considering the setup."

"Apache Spark is not too cheap. You have to pay for hardware and Cloudera licenses. Of course, there is a solution with open source without Cloudera."

"On the cloud model can be expensive as it requires substantial resources for implementation, covering on-premises hardware, memory, and licensing."

"I did not pay anything when using the tool on cloud services, but I had to pay on the compute side. The tool is not expensive compared with the benefits it offers. I rate the price as an eight out of ten."

More Apache Spark pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

16%

Computer Software Company

14%

Manufacturing Company

10%

Retailer

Financial Services Firm

26%

Computer Software Company

13%

Manufacturing Company

Comms Service Provider

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

What do you like most about Apache NiFi?

The initial setup is very easy.

See all answers

What is your experience regarding pricing and costs for Apache NiFi?

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.

See all answers

What needs improvement with Apache NiFi?

The logging system of Apache NiFi needs improvement. It is difficult to debug compared to Airflow ( /products/apache-airflow-reviews ), where task details and issues are clear. With Apache NiFi, I ...

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What do you like most about Apache Spark?

We use Spark to process data from different data sources.

See all answers

What is your experience regarding pricing and costs for Apache Spark?

Apache Spark is open-source, so it doesn't incur any charges.

See all answers

What needs improvement with Apache Spark?

There is complexity when it comes to understanding the whole ecosystem, especially for beginners. I find it quite complex to understand how a Spark job is initiated, the roles of driver nodes, work...

See all answers

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Azure Stream Analytics vs Apache NiFi

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Apache Storm vs Apache NiFi

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Compared 11% of the time

Spark SQL vs Apache Spark

Compared 7% of the time

Amazon EMR vs Apache Spark

Compared 3% of the time

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Product Reports

Buyer's Guide

Apache NiFi

May 2025

Download Apache NiFi product report

Buyer's Guide

Apache Spark

May 2025

Download Apache Spark product report

Overview

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.

Apache

Spark provides programmers with an application programming interface centered on a data structure called the resilient distributed dataset (RDD), a read-only multiset of data items distributed over a cluster of machines, that is maintained in a fault-tolerant way. It was developed in response to limitations in the MapReduce cluster computing paradigm, which forces a particular linear dataflowstructure on distributed programs: MapReduce programs read input data from disk, map a function across the data, reduce the results of the map, and store reduction results on disk. Spark's RDDs function as a working set for distributed programs that offers a (deliberately) restricted form of distributed shared memory

Apache

Sample Customers

Macquarie Telecom Group, Dovestech, Slovak Telekom, Looker, Hastings Group

NASA JPL, UC Berkeley AMPLab, Amazon, eBay, Yahoo!, UC Santa Cruz, TripAdvisor, Taboola, Agile Lab, Art.com, Baidu, Alibaba Taobao, EURECOM, Hitachi Solutions

Buyer's Guide

Apache NiFi vs. Apache Spark

April 2025

Free Report: Apache NiFi vs. Apache Spark

Find out what your peers are saying about Apache NiFi vs. Apache Spark and other solutions. Updated: April 2025.

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