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 8.4% 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

1,880 Views
831 Comparison Views

92% willing to recommend

Apache Spark

Read 66 Apache Spark reviews

1,526 Views
1,149 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 849,686 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

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 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

"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."

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

"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."

"The initial setup is very easy."

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

"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."

More Apache NiFi pros

"The scalability has been the most valuable aspect of the solution."

"The fault tolerant feature is provided."

"ETL and streaming capabilities."

"This solution provides a clear and convenient syntax for our analytical tasks."

"I found the solution stable. We haven't had any problems with it."

"I like that it can handle multiple tasks parallelly. I also like the automation feature. JavaScript also helps with the parallel streaming of the library."

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

"I like Apache Spark's flexibility the most. Before, we had one server that would choke up. With the solution, we can easily add more nodes when needed. The machine learning models are also really helpful. We use them to predict energy theft and find infrastructure problems."

More Apache Spark pros

Cons

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

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

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

"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."

"There should be a better way to integrate a development environment with local tools."

"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 use case templates could be more precise to typical business needs."

More Apache NiFi cons

"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."

"Its UI can be better. Maintaining the history server is a little cumbersome, and it should be improved. I had issues while looking at the historical tags, which sometimes created problems. You have to separately create a history server and run it. Such things can be made easier. Instead of separately installing the history server, it can be made a part of the whole setup so that whenever you set it up, it becomes available."

"At times during the deployment process, the tool goes down, making it look less robust. To take care of the issues in the deployment process, users need to do manual interventions occasionally."

"It should support more programming languages."

"The main concern is the overhead of Java when distributed processing is not necessary."

"Apache Spark's GUI and scalability could be improved."

"The Spark solution could improve in scheduling tasks and managing dependencies."

"I would like to see integration with data science platforms to optimize the processing capability for these tasks."

More Apache Spark cons

Pricing and Cost Advice

"I used the tool's free version."

"We use the free version of Apache NiFi."

"It's an open-source solution."

"The solution is open-source."

"Apache Spark is an open-source solution, and there is no cost involved in deploying the solution on-premises."

"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."

"Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera."

"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."

"Apache Spark is an expensive solution."

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

"The product is expensive, considering the setup."

"Spark is an open-source solution, so there are no licensing costs."

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

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?

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?

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 30% of the time

Google Cloud Dataflow vs Apache NiFi

Compared 27% of the time

AWS Fargate vs Apache NiFi

Compared 10% of the time

Azure Stream Analytics vs Apache NiFi

Compared 7% of the time

Apache Storm vs Apache NiFi

Compared 4% of the time

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Spring Boot vs Apache Spark

Compared 26% of the time

AWS Batch vs Apache Spark

Compared 12% of the time

SAP HANA vs Apache Spark

Compared 12% 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

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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