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

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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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Helped 845,040 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 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 user interface is good and makes it easy to design very popular workflows."

"The initial setup is very easy."

"Visually, this is a good product."

"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."

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

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

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

More Apache NiFi pros

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

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

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

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

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

"The most valuable feature is the Fault Tolerance and easy binding with other processes like Machine Learning, graph analytics."

"The distribution of tasks, like the seamless map-reduce functionality, is quite impressive."

"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

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

"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."

"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."

"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."

"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."

More Apache NiFi cons

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

"Apache Spark could improve the connectors that it supports. There are a lot of open-source databases in the market. For example, cloud databases, such as Redshift, Snowflake, and Synapse. Apache Spark should have connectors present to connect to these databases. There are a lot of workarounds required to connect to those databases, but it should have inbuilt connectors."

"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."

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

"The graphical user interface (UI) could be a bit more clear. It's very hard to figure out the execution logs and understand how long it takes to send everything. If an execution is lost, it's not so easy to understand why or where it went. I have to manually drill down on the data processes which takes a lot of time. Maybe there could be like a metrics monitor, or maybe the whole log analysis could be improved to make it easier to understand and navigate."

"There could be enhancements in optimization techniques, as there are some limitations in this area that could be addressed to further refine Spark's performance."

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

"One limitation is that not all machine learning libraries and models support it."

More Apache Spark cons

Pricing and Cost Advice

"It's an open-source solution."

"We use the free version of Apache NiFi."

"The solution is open-source."

"I used the tool's free version."

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

"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."

"They provide an open-source license for the on-premise version."

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

"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."

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

"It is an open-source solution, it is free of charge."

"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

17%

Computer Software Company

14%

Manufacturing Company

Retailer

Financial Services Firm

28%

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 12% of the time

Cloudera Distribution for Hadoop vs Apache Spark

Compared 8% of the time

Azure Stream Analytics vs Apache Spark

Compared 4% of the time

More Apache Spark Competitors

Product Reports

Buyer's Guide

Apache NiFi

March 2025

Download Apache NiFi product report

Buyer's Guide

Apache Spark

March 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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