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 #3 with an average rating of 8.6. Apache NiFi holds a 7.9% 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

2,136 Views
991 Comparison Views

91% willing to recommend

Apache Spark

Read 65 Apache Spark reviews

1,769 Views
1,353 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: January 2025).

Buyer's Guide

Apache NiFi vs. Apache Spark

January 2025

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Helped 838,713 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

3rd

Average Rating

8.4

Reviews Sentiment

7.7

Number of Reviews

Ranking in other categories

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

Mindshare comparison

As of February 2025, in the Compute Service category, the mindshare of Apache NiFi is 7.9%, up from 6.5% compared to the previous year. The mindshare of Apache Spark is 11.3%, up from 8.5% 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 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 user interface is good and makes it easy to design very popular workflows."

"The initial setup is very easy."

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

"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."

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

More Apache NiFi pros

"The product’s most valuable feature is the SQL tool. It enables us to create a database and publish it."

"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."

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

"It is highly scalable, allowing you to efficiently work with extensive datasets that might be problematic to handle using traditional tools that are memory-constrained."

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

"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."

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

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

More Apache Spark pros

Cons

"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."

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

"More features must be added to the product."

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

"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."

"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."

More Apache NiFi cons

"This solution currently cannot support or distribute neural network related models, or deep learning related algorithms. We would like this functionality to be developed."

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

"Technical expertise from an engineer is required to deploy and run high-tech tools, like Informatica, on Apache Spark, making it an area where improvements are required to make the process easier for users."

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

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

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

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

"I know there is always discussion about which language to write applications in and some people do love Scala. However, I don't like it."

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."

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

"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."

"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."

"Apache Spark is an open-source tool."

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

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

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

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

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

Google Cloud Dataflow vs Apache NiFi

Compared 31% of the time

AWS Lambda 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

More Apache NiFi Competitors

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

February 2025

Download Apache NiFi product report

Buyer's Guide

Apache Spark

February 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

January 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: January 2025.

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