Apache NiFi vs IBM Streams comparison

The compared Apache and IBM solutions aren't in the same category. Apache is ranked #8 in CS , with an average rating of 7.7, and holds a 8.9% mindshare in the category. IBM is ranked #20 in SA , with an average rating of 7.0, and holds a 0.9% mindshare. Additionally, 92% of Apache users are willing to recommend the solution, compared to 100% of IBM users who would recommend it.

Apache NiFi

Read 13 Apache NiFi reviews

2,408 Views
1,421 Comparison Views

92% willing to recommend

IBM Streams

Read 5 IBM Streams reviews

513 Views
369 Comparison Views

100% willing to recommend

Apache NiFi

IBM Streams

Comparison Buyer's Guide

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Executive Summary

We performed a comparison between Apache NiFi and IBM Streams based on real PeerSpot user reviews.

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Buyer's Guide

Compute Service

July 2025

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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

Average Rating

7.8

Reviews Sentiment

7.4

Number of Reviews

Ranking in other categories

Compute Service (8th)

IBM Streams

Average Rating

8.2

Reviews Sentiment

7.2

Number of Reviews

Ranking in other categories

Streaming Analytics (20th)

Mindshare comparison

Apache NiFi and IBM Streams aren’t in the same category and serve different purposes. Apache NiFi is designed for Compute Service and holds a mindshare of 8.9%, up 7.7% compared to last year.
IBM Streams, on the other hand, focuses on Streaming Analytics, holds 0.9% mindshare, down 0.9% since last year.

Compute Service

Streaming Analytics

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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Ahmed_Emad

Territory Sales Leader at Sumerge

A solution for data pipelines but has connector limitations

We have used Kafka for seven years. IBM streams gives you many OOTB features that can boost the time-to-market, especially when it comes to reporting and monitoring for example. Confluent is recognized as one of the leaders in this space and the main reason for this is related to the complete vision of the platform also the large number of connectors. This gives the edge and competitive advatnage.

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Top Industries

By visitors reading reviews

Financial Services Firm

15%

Computer Software Company

13%

Manufacturing Company

10%

Retailer

Financial Services Firm

32%

Computer Software Company

19%

Government

Comms Service Provider

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

Questions from the Community

What do you like most about Apache NiFi?

The initial setup is very easy.

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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.

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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 is your experience regarding pricing and costs for IBM Streams?

The solution’s licenses pricing is different from one region to another region. I rate the solution’s pricing a seven out of ten.

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What needs improvement with IBM Streams?

the limited number of connectors. This shall be overcome with work-arounds or eventually buying additional connectors to complete the solution.

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What is your primary use case for IBM Streams?

We use the solution for data pipeline by modernizing the traditional ETL jobs done through advanced streaming. Another use case is building the g2g streaming platform, which facilitates data exchan...

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Comparisons

AWS Lambda vs Apache NiFi

Compared 28% of the time

Google Cloud Dataflow vs Apache NiFi

Compared 24% of the time

Apache Spark vs Apache NiFi

Compared 16% of the time

AWS Fargate vs Apache NiFi

Compared 10% of the time

Apache Storm vs Apache NiFi

Compared 7% of the time

More Apache NiFi Competitors

Confluent vs IBM Streams

Compared 53% of the time

Apache Flink vs IBM Streams

Compared 47% of the time

More IBM Streams Competitors

Product Reports

Buyer's Guide

Apache NiFi

July 2025

Download Apache NiFi product report

Buyer's Guide

Streaming Analytics

July 2025

Download IBM Streams product report

Also Known As

No data available

IBM InfoSphere Streams

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

IBM Streams is an advanced analytic platform that allows user-developed applications to quickly ingest, analyze and correlate information as it arrives from thousands of data stream sources. The solution can handle very high data throughput rates, up to millions of events or messages per second. Streams helps you analyze data in motion, simplify development of streaming applications, and extend the value of existing systems.

IBM

Sample Customers

Macquarie Telecom Group, Dovestech, Slovak Telekom, Looker, Hastings Group

Globo TV, All England Lawn Tennis Club, CenterPoint Energy, Consolidated Communications Holdings, Darwin Ecosystem, Emory University Hospital, ICICI Securities, Irish Centre for Fetal and Neonatal Translational Research (INFANT), Living Roads, Mobileum, Optibus, Southern Ontario Smart Computing Innovation Platform (SOSCIP), University of Alberta, University of Montana, University of Ontario Institute of Technology, Wimbledon 2015

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