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 7.6, while Apache Spark is ranked #4 with an average rating of 8.6. Apache NiFi holds a 8.0% mindshare in CS, compared to Apache Spark’s 11.2% 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

1,880 Views
831 Comparison Views

92% willing to recommend

Apache Spark

Read 65 Apache Spark reviews

1,526 Views
1,149 Comparison Views

90% willing to recommend

Apache NiFi

Apache Spark

Comparison Buyer's Guide

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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: March 2025).

Buyer's Guide

Apache NiFi vs. Apache Spark

March 2025

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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

4th

Average Rating

8.4

Reviews Sentiment

7.7

Number of Reviews

Ranking in other categories

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

Mindshare comparison

As of April 2025, in the Compute Service category, the mindshare of Apache NiFi is 8.0%, up from 7.1% compared to the previous year. The mindshare of Apache Spark is 11.2%, up from 9.7% 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

"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. I would rate my experience with the initial setup a ten out of ten, where one point is difficult, and ten points are easy."

"Visually, this is a good product."

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

"The initial setup is very easy."

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

"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."

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

More Apache NiFi pros

"The most significant advantage of Spark 3.0 is its support for DataFrame UDF Pandas UDF features."

"The product's initial setup phase was easy."

"The most valuable feature of Apache Spark is its ease of use."

"Apache Spark can do large volume interactive data analysis."

"Provides a lot of good documentation compared to other solutions."

"Features include machine learning, real time streaming, and data processing."

"The deployment of the product is easy."

"The features we find most valuable are the machine learning, data learning, and Spark Analytics."

More Apache Spark pros

Cons

"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."

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

"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."

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

"More features must be added to the product."

"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."

"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."

More Apache NiFi cons

"When using Spark, users may need to write their own parallelization logic, which requires additional effort and expertise."

"There were some problems related to the product's compatibility with a few Python libraries."

"The logging for the observability platform could be better."

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

"More ML based algorithms should be added to it, to make it algorithmic-rich for developers."

"It should support more programming languages."

"It needs a new interface and a better way to get some data. In terms of writing our scripts, some processes could be faster."

"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

"The solution is open-source."

"We use the free version of Apache NiFi."

"It's an open-source solution."

"I used the tool's free version."

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

"The solution is affordable and there are no additional licensing costs."

"Apache Spark is an expensive solution."

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

"It is quite expensive. In fact, it accounts for almost 50% of the cost of our entire project."

"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."

"The tool is an open-source product. If you're using the open-source Apache Spark, no fees are involved at any time. Charges only come into play when using it with other services like Databricks."

"The product is expensive, considering the setup."

More Apache Spark pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

17%

Computer Software Company

14%

Manufacturing Company

10%

Retailer

Financial Services Firm

27%

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?

I used the tool's free version.

See all answers

What needs improvement with Apache NiFi?

See all answers

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?

Compared to other solutions like Doc DB, Spark is more costly due to the need for extensive infrastructure. It requires significant investment in infrastructure, which can be expensive. While cloud...

See all answers

What needs improvement with Apache Spark?

The Spark solution could improve in scheduling tasks and managing dependencies. Spark alone cannot handle sequential tasks, requiring environments like Airflow scheduler or scripts. For instance, o...

See all answers

Comparisons

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Compared 31% of the time

Google Cloud Dataflow vs Apache NiFi

Compared 30% of the time

AWS Fargate vs Apache NiFi

Compared 10% of the time

Azure Stream Analytics vs Apache NiFi

Compared 8% of the time

Apache Storm vs Apache NiFi

Compared 4% of the time

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Spring Boot vs Apache Spark

Compared 27% of the time

SAP HANA vs Apache Spark

Compared 12% of the time

AWS Batch vs Apache Spark

Compared 11% of the time

Cloudera Distribution for Hadoop vs Apache Spark

Compared 7% of the time

Azure Stream Analytics vs Apache Spark

Compared 3% of the time

More Apache Spark Competitors

Product Reports

Buyer's Guide

Apache NiFi

April 2025

Download Apache NiFi product report

Buyer's Guide

Apache Spark

April 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

March 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: March 2025.

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