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 7.8% mindshare in CS, compared to Apache Spark’s 11.3% mindshare. Additionally, 91% of Apache NiFi users are willing to recommend the solution, compared to 90% of Apache Spark users who would recommend it.

Apache NiFi

Read 12 Apache NiFi reviews

2,034 Views
912 Comparison Views

91% willing to recommend

Apache Spark

Read 65 Apache Spark reviews

1,646 Views
1,251 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 842,194 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 March 2025, in the Compute Service category, the mindshare of Apache NiFi is 7.8%, up from 6.9% compared to the previous year. The mindshare of Apache Spark is 11.3%, up from 9.2% 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

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

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

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

"Visually, this is a good product."

"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."

More Apache NiFi pros

"Now, when we're tackling sentiment analysis using NLP technologies, we deal with unstructured data—customer chats, feedback on promotions or demos, and even media like images, audio, and video files. For processing such data, we rely on PySpark. Beneath the surface, Spark functions as a compute engine with in-memory processing capabilities, enhancing performance through features like broadcasting and caching. It's become a crucial tool, widely adopted by 90% of companies for a decade or more."

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

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

"We use it for ETL purposes as well as for implementing the full transformation pipelines."

"The main feature that we find valuable is that it is very fast."

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

"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."

"I feel the streaming is its best feature."

More Apache Spark pros

Cons

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

"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 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 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."

"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."

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

More Apache NiFi cons

"The setup I worked on was really complex."

"Apache Spark should add some resource management improvements to the algorithms."

"The management tools could use improvement. Some of the debugging tools need some work as well. They need to be more descriptive."

"The migration of data between different versions could be improved."

"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."

"When you first start using this solution, it is common to run into memory errors when you are dealing with large amounts of data."

"Apache Spark can improve the use case scenarios from the website. There is not any information on how you can use the solution across the relational databases toward multiple databases."

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

More Apache Spark cons

Pricing and Cost Advice

"It's an open-source solution."

"We use the free version of Apache NiFi."

"I used the tool's free version."

"The solution is open-source."

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

"The product is expensive, considering the setup."

"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."

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

"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

17%

Computer Software Company

14%

Manufacturing Company

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 29% of the time

Google Cloud Dataflow vs Apache NiFi

Compared 29% of the time

AWS Fargate vs Apache NiFi

Compared 9% 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

AWS Fargate vs Apache Spark

Compared 3% 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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