Apache NiFi vs Apache Spark comparison

Apache NiFi and Apache Spark are both solutions in the Compute Service category. Apache NiFi is ranked #7 with an average rating of 7.9, while Apache Spark is ranked #5 with an average rating of 8.7. Apache NiFi holds a 9.5% mindshare in CS, compared to Apache Spark’s 11.2% mindshare. Additionally, 95% of Apache NiFi users are willing to recommend the solution, compared to 90% of Apache Spark users who would recommend it.

Apache NiFi

Read 22 Apache NiFi reviews

1,725 Views
1,139 Comparison Views

95% willing to recommend

Apache Spark

Read 68 Apache Spark reviews

3,530 Views
953 Comparison Views

90% willing to recommend

Apache NiFi

Apache Spark

Comparison Buyer's Guide

Download the report

Executive SummaryUpdated on May 21, 2025

Apache Spark and Apache NiFi compete in the big data processing and integration category. Apache Spark may have the upper hand due to its extensive in-memory computing and scalability capabilities, despite NiFi’s intuitive data flow management interface.

Features: Apache Spark is known for in-memory computing, enabling high-speed data processing and real-time analytics with Spark Streaming. It also provides extensive capabilities for machine learning with MLlib and efficient large-scale data analysis using Spark SQL. Apache NiFi is recognized for its user-friendly visual tools for designing data pipelines, real-time data integration, and comprehensive connectors that simplify diverse data flow management.

Room for Improvement: Apache Spark users desire enhancements in scalability and stability, improved documentation, and advanced monitoring tools. Additional stream processing capabilities and machine learning algorithms are also suggested. Apache NiFi users call for better stability, reduced operational complexity, enhanced integration features, and better JSON processing. Both could benefit from improved user interfaces and advanced alert systems.

Ease of Deployment and Customer Service: Apache Spark offers flexible deployment options in On-premises, Hybrid, and Public Cloud environments. Community support is vibrant but experiences vary, with better results seen using commercial support. Apache NiFi is praised for its visual pipeline management, with similarly flexible deployment options. Customer service is primarily community-driven, with some positive experiences from commercial support.

Pricing and ROI: Both Apache Spark and Apache NiFi are open-source, thus available without licensing fees, allowing cost-effective deployment. Apache Spark costs can rise with infrastructure needs, yet it promises high ROI through enhanced processing capacity. Apache NiFi, while free at its core, may incur costs in complex integration setups. Both provide substantial efficiency and cost savings over time.

To learn more, read our detailed Apache NiFi vs. Apache Spark Report (Updated: December 2025).

Buyer's Guide

Apache NiFi vs. Apache Spark

December 2025

Download the complete report

Helped 879,422 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:

ROI

Sentiment score

4.8

Apache NiFi users experience significant time savings and improved efficiency, although exact cost benefits aren't always quantifiable.

Sentiment score

6.1

Apache Spark enhances machine learning, cutting operational costs by up to 50%, with efficiency reliant on resources and expertise.

Thanks to improvements on both our side in how we run processes and enhancements to Apache NiFi, we have reduced the time commitment to almost not needing to interact with Apache NiFi except for minor queue-clearance tasks, allowing it to run smoothly.

Bj Tan

Data Engineer at The Kudelski Group

It supports not just ETL but also ELT, allowing us to save significant time.

Nishant Khandelwal

Team Lead Technical Specialist (Production Support) at a recreational facilities/services company with 11-50 employees

There may be return on investment based on the technology and easily moving our workloads onto Apache NiFi from our previous system.

SyedIsmail

Senior Consultant - Data Analytics at a comms service provider with 201-500 employees

For more quotes and insights, download the Apache NiFi report

No quotes available

For more quotes and insights, download the Apache Spark report

Customer Service

Sentiment score

5.7

Apache NiFi users find support adequate, using documentation and forums, with mixed reviews on official support and positive Cloudera experiences.

Sentiment score

5.9

Apache Spark support feedback varies, with mixed reviews on community forums, vendor support, and documentation adequacy.

The customer support is really good, and they are helpful whenever concerns are posted, responding immediately.

reviewer2785560

Cloud Data Architect at a healthcare company with 10,001+ employees

Customer support for Apache NiFi has been excellent, with minimal response times whenever we raise cases that cannot be directly addressed by logs.

Nishant Khandelwal

Team Lead Technical Specialist (Production Support) at a recreational facilities/services company with 11-50 employees

I would rate the customer support of Apache NiFi a 10 on a scale of 1 to 10.

reviewer2784384

Partner at a tech vendor with 10,001+ employees

For more quotes and insights, download the Apache NiFi report

I have received support via newsgroups or guidance on specific discussions, which is what I would expect in an open-source situation.

Devindra Weerasooriya

Data Architect at Devtech

For more quotes and insights, download the Apache Spark report

Scalability Issues

Sentiment score

6.7

Apache NiFi's scalability varies; some users succeed with clusters, while others struggle without Kubernetes, impacting implementation.

Sentiment score

7.5

Apache Spark excels in scalability, efficiently handling large data workloads with ease, though it requires skilled infrastructure management.

Depending on the workload we process, it remains stable since at the end of the day, it is just used as an orchestration tool that triggers the job while the heavy lifting is done on Spark servers.

Debashish Dhar

Manager at a tech company with 10,001+ employees

Scaling up is fairly straightforward, provided you manage configurations effectively.

Bj Tan

Data Engineer at The Kudelski Group

Based on the workload, more nodes can be added to make a bigger cluster, which enhances the cluster whenever needed.

reviewer2785560

Cloud Data Architect at a healthcare company with 10,001+ employees

For more quotes and insights, download the Apache NiFi report

No quotes available

For more quotes and insights, download the Apache Spark report

Stability Issues

Sentiment score

7.4

Apache NiFi is generally stable but may face occasional crashes and reliability challenges, especially in distributed environments.

Sentiment score

7.4

Apache Spark is generally stable, trusted by companies; newer versions enhance reliability, though memory issues may arise without proper configuration.

I have seen Apache NiFi crashing at times, which is one of the issues we have faced in production.

Debashish Dhar

Manager at a tech company with 10,001+ employees

Apache NiFi is stable in most cases.

SyedIsmail

Senior Consultant - Data Analytics at a comms service provider with 201-500 employees

For more quotes and insights, download the Apache NiFi report

Apache Spark resolves many problems in the MapReduce solution and Hadoop, such as the inability to run effective Python or machine learning algorithms.

Omar Khaled

Data Engineer at a tech company with 10,001+ employees

Without a doubt, we have had some crashes because each situation is different, and while the prototype in my environment is stable, we do not know everything at other customer sites.

Devindra Weerasooriya

Data Architect at Devtech

For more quotes and insights, download the Apache Spark report

Room For Improvement

Apache NiFi needs improved integration, UI, scalability, security features, and seamless CI/CD processes for enhanced usability and efficiency.

Apache Spark requires improvements in scalability, usability, documentation, memory efficiency, real-time processing, and broader language support for better performance.

Apache NiFi should have APIs or connectors that can connect seamlessly to other external entities, whether in the cloud or on-premises, creating a plug-and-play mechanism.

Debashish Dhar

Manager at a tech company with 10,001+ employees

The history of processed files should be more readable so that not only the centralized teams managing Apache NiFi but also application folks who are new to the platform can read how a specific document is traversing through Apache NiFi.

Nishant Khandelwal

Team Lead Technical Specialist (Production Support) at a recreational facilities/services company with 11-50 employees

The initial error did not indicate it was related to memory or size limitations but appeared as a parsing error or something similar.

Bj Tan

Data Engineer at The Kudelski Group

For more quotes and insights, download the Apache NiFi report

Various tools like Informatica, TIBCO, or Talend offer specific aspects, licensing can be costly;

Devindra Weerasooriya

Data Architect at Devtech

For more quotes and insights, download the Apache Spark report

Setup Cost

Apache NiFi is an open-source solution with no setup costs, offering time-saving efficiencies despite potential integration expenses.

Apache Spark is cost-effective but may incur expenses from hardware, cloud resources, or commercial support, impacting deployment costs.

The pricing in Italy is considered a little bit high, but the product is worth it.

reviewer2784384

Partner at a tech vendor with 10,001+ employees

For more quotes and insights, download the Apache NiFi report

No quotes available

For more quotes and insights, download the Apache Spark report

Valuable Features

Apache NiFi simplifies data flow orchestration with drag-and-drop features, scalability, and integration, enhancing productivity and reliability.

Apache Spark offers fast in-memory processing, scalable analytics, MLlib for machine learning, SQL support, and seamless integration with languages.

Apache NiFi has positively impacted my organization by definitely bridging the gap between the on-premises and cloud interaction until we find a solution to open the firewall for cloud components to directly interact with on-premises services.

Debashish Dhar

Manager at a tech company with 10,001+ employees

Development has improved with a reduction in time spent being the main benefit; before we needed a matter of days to create the ingestion flows, but now it only takes a couple of hours to configure.

reviewer2784747

Senior Data Engineer at a tech vendor with 10,001+ employees

The ease of use in Apache NiFi has helped my team because anyone can learn how to use it in a short amount of time, so we were able to get a lot of work done.

SyedIsmail

Senior Consultant - Data Analytics at a comms service provider with 201-500 employees

For more quotes and insights, download the Apache NiFi report

Not all solutions can make this data fast enough to be used, except for solutions such as Apache Spark Structured Streaming.

Omar Khaled

Data Engineer at a tech company with 10,001+ employees

The solution is beneficial in that it provides a base-level long-held understanding of the framework that is not variant day by day, which is very helpful in my prototyping activity as an architect trying to assess Apache Spark, Great Expectations, and Vault-based solutions versus those proposed by clients like TIBCO or Informatica.

Devindra Weerasooriya

Data Architect at Devtech

For more quotes and insights, download the Apache Spark report

Categories and Ranking

Apache NiFi

Ranking in Compute Service

7th

Average Rating

7.8

Reviews Sentiment

5.3

Number of Reviews

Ranking in other categories

No ranking in other categories

Apache Spark

Ranking in Compute Service

5th

Average Rating

8.4

Reviews Sentiment

6.9

Number of Reviews

Ranking in other categories

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

Mindshare comparison

As of January 2026, in the Compute Service category, the mindshare of Apache NiFi is 9.5%, up from 7.7% compared to the previous year. The mindshare of Apache Spark is 11.2%, down from 11.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Compute Service Market Share Distribution
Product	Market Share (%)
Apache Spark	11.2%
Apache NiFi	9.5%
Other	79.3%

Compute Service

Featured Reviews

Yamini Vadlamudi

architect with 51-200 employees

Unified data flows have simplified large-scale ingestion and have improved SLA reliability

Improvements can be made in the way of the UI. From the deployment perspective, Git configurations are available in 2.6 versions and 2.0 and later versions of Apache NiFi. Before 2.0, templates had to be created and stored in Apache NiFi Registry, which is available. However, templates still need to be imported and exported manually if moving from one environment to another environment. Even in 2.0 versions, although GitHub configurations are available, how it will function needs to be evaluated. Seamless CI/CD deployments are somewhat tricky and challenging when it comes to Apache NiFi with the proper approvals, moving that flow to another environment, and giving the proper RBAC controls. These are areas that could be improved. Documentation is adequate, but the only pain point is the deployment aspect.

Read full review

Devindra Weerasooriya

Data Architect at Devtech

Provides a consistent framework for building data integration and access solutions with reliable performance

The in-memory computation feature is certainly helpful for my processing tasks. It is helpful because while using structures that could be held in memory rather than stored during the period of computation, I go for the in-memory option, though there are limitations related to holding it in memory that need to be addressed, but I have a preference for in-memory computation. The solution is beneficial in that it provides a base-level long-held understanding of the framework that is not variant day by day, which is very helpful in my prototyping activity as an architect trying to assess Apache Spark, Great Expectations, and Vault-based solutions versus those proposed by clients like TIBCO or Informatica.

Read full review

See which vendors are best for you

Use our free recommendation engine to learn which Compute Service solutions are best for your needs.

See recommendations

879,422 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Manufacturing Company

16%

Computer Software Company

12%

Financial Services Firm

11%

University

Financial Services Firm

25%

Computer Software Company

11%

Manufacturing Company

Comms Service Provider

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	5
Midsize Enterprise	1
Large Enterprise	18

By reviewers
Company Size	Count
Small Business	28
Midsize Enterprise	15
Large Enterprise	32

Questions from the Community

What is your experience regarding pricing and costs for Apache NiFi?

The experience with pricing, setup cost, and licensing was fine, as the integration with the AWS Marketplace was very good. The pricing in Italy is considered a little bit high, but the product is ...

See all answers

What needs improvement with Apache NiFi?

I believe Apache NiFi could be improved with easier, out-of-the-box provided monitoring solutions. While Apache NiFi has an API that generates logs, it would be beneficial to have simpler access to...

See all answers

What is your primary use case for Apache NiFi?

I have been using Apache NiFi virtually daily, as it is part of my main responsibility in my current role. My main use case for Apache NiFi involves integrating various data sources and performing ...

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?

Apache Spark is open-source, so it doesn't incur any charges.

See all answers

What needs improvement with Apache Spark?

Areas for improvement are obviously ease of use considerations, though there are limitations in doing that, so while various tools like Informatica, TIBCO, or Talend offer specific aspects, licensi...

See all answers

Comparisons

AWS Lambda vs Apache NiFi

Compared 23% of the time

Google Cloud Dataflow vs Apache NiFi

Compared 15% of the time

Amazon EC2 vs Apache NiFi

Compared 10% of the time

Apache Storm vs Apache NiFi

Compared 8% of the time

IBM Streams vs Apache NiFi

Compared 5% of the time

More Apache NiFi Competitors

Spring Boot vs Apache Spark

Compared 18% of the time

AWS Lambda vs Apache Spark

Compared 8% of the time

AWS Batch vs Apache Spark

Compared 7% of the time

SAP HANA vs Apache Spark

Compared 6% of the time

Amazon EC2 vs Apache Spark

Compared 5% of the time

More Apache Spark Competitors

Product Reports

Buyer's Guide

Apache NiFi

December 2025

Download Apache NiFi product report

Buyer's Guide

Apache Spark

December 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

December 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: December 2025.

DOWNLOAD NOW

879,422 professionals have used our research since 2012.

See our Apache NiFi vs. Apache Spark report.

See our list of best Compute Service vendors.

We monitor all Compute Service reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.