Apache NiFi vs Google Cloud Dataflow comparison

The compared Apache and Google solutions aren't in the same category. Apache is ranked #7 in CS , with an average rating of 7.9, and holds a 9.5% mindshare in the category. Google is ranked #10 in SA , with an average rating of 8.2, and holds a 4.5% mindshare. Additionally, 95% of Apache users are willing to recommend the solution, compared to 93% of Google users who would recommend it.

Apache NiFi

Read 22 Apache NiFi reviews

1,725 Views
1,139 Comparison Views

95% willing to recommend

Google Cloud Dataflow

Read 14 Google Cloud Dataflow reviews

2,228 Views
1,872 Comparison Views

93% willing to recommend

Apache NiFi

Google Cloud Dataflow

Comparison Buyer's Guide

Download the report

Executive Summary

We performed a comparison between Apache NiFi and Google Cloud Dataflow based on real PeerSpot user reviews.

Find out what your peers are saying about Amazon Web Services (AWS), Apache, Spot - A Flexera company and others in Compute Service.

To learn more, read our detailed Compute Service Report (Updated: January 2026).

Buyer's Guide

Compute Service

January 2026

Download the complete report

Helped 879,853 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

5.6

Google Cloud Dataflow was appreciated for cost savings and time efficiency, though some considered its impact not fully assessable yet.

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 Google Cloud Dataflow 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

6.6

Google Cloud Dataflow support varies, with users praising technical resolution but highlighting inconsistent response times and accessibility.

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

The fact that no interaction is needed shows their great support since I don't face issues.

Jana Polianskaja

Data Engineer at Accenture

Google's support team is good at resolving issues, especially with large data.

Preethi Reddy

Senior Data Engineer at Accruent

Whenever we have issues, we can consult with Google.

Sunil Miragi

Senior Software Engineer at Dun & Bradstreet

For more quotes and insights, download the Google Cloud Dataflow 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

7.3

Google Cloud Dataflow excels in scalability and efficiency, making it ideal for real-time data processing and dynamic needs.

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

Google Cloud Dataflow has auto-scaling capabilities, allowing me to add different machine types based on pace and requirements.

Jana Polianskaja

Data Engineer at Accenture

As a team lead, I'm responsible for handling five to six applications, but Google Cloud Dataflow seems to handle our use case effectively.

Sunil Miragi

Senior Software Engineer at Dun & Bradstreet

Google Cloud Dataflow can handle large data processing for real-time streaming workloads as they grow, making it a good fit for our business.

Preethi Reddy

Senior Data Engineer at Accruent

For more quotes and insights, download the Google Cloud Dataflow 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

8.3

Google Cloud Dataflow is stable, reliably handles tasks, and benefits from automatic scaling, with minor issues on complex tasks.

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

I have not encountered any issues with the performance of Dataflow, as it is stable and backed by Google services.

Jana Polianskaja

Data Engineer at Accenture

The job we built has not failed once over six to seven months.

Sunil Miragi

Senior Software Engineer at Dun & Bradstreet

The automatic scaling feature helps maintain stability.

Preethi Reddy

Senior Data Engineer at Accruent

For more quotes and insights, download the Google Cloud Dataflow report

Room For Improvement

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

Google Cloud Dataflow needs better Kafka integration, improved error logs, reduced startup time, and enhanced Python SDK features.

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

Outside of Google Cloud Platform, it is problematic for others to use it and may require promotion as an actual technology.

Jana Polianskaja

Data Engineer at Accenture

Dealing with a huge volume of data causes failure due to array size.

Sunil Miragi

Senior Software Engineer at Dun & Bradstreet

I would like to see improvements in consistency and flexibility for schema design for NoSQL data stored in wide columns.

Preethi Reddy

Senior Data Engineer at Accruent

For more quotes and insights, download the Google Cloud Dataflow report

Setup Cost

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

Google Cloud Dataflow is praised for cost-effectiveness and scalability, offering competitive pricing influenced by pipeline complexity and company size.

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

It is part of a package received from Google, and they are not charging us too high.

Sunil Miragi

Senior Software Engineer at Dun & Bradstreet

For more quotes and insights, download the Google Cloud Dataflow report

Valuable Features

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

Google Cloud Dataflow offers seamless integration, multi-language support, scalability, and serverless data handling for efficient batch and streaming processes.

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

It supports multiple programming languages such as Java and Python, enabling flexibility without the need to learn something new.

Jana Polianskaja

Data Engineer at Accenture

The integration within Google Cloud Platform is very good.

Sunil Miragi

Senior Software Engineer at Dun & Bradstreet

Google Cloud Dataflow's features for event stream processing allow us to gain various insights like detecting real-time alerts.

Preethi Reddy

Senior Data Engineer at Accruent

For more quotes and insights, download the Google Cloud Dataflow report

Categories and Ranking

Apache NiFi

Average Rating

7.8

Reviews Sentiment

5.3

Number of Reviews

Ranking in other categories

Compute Service (7th)

Google Cloud Dataflow

Average Rating

8.0

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

Streaming Analytics (10th)

Mindshare comparison

Apache NiFi and Google Cloud Dataflow aren’t in the same category and serve different purposes. Apache NiFi is designed for Compute Service and holds a mindshare of 9.5%, up 7.7% compared to last year.
Google Cloud Dataflow, on the other hand, focuses on Streaming Analytics, holds 4.5% mindshare, down 7.8% since last year.

Compute Service Market Share Distribution
Product	Market Share (%)
Apache NiFi	9.5%
AWS Lambda	13.8%
AWS Batch	12.9%
Other	63.8%

Compute Service

Streaming Analytics Market Share Distribution
Product	Market Share (%)
Google Cloud Dataflow	4.5%
Apache Flink	12.3%
Databricks	10.0%
Other	73.2%

Streaming Analytics

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

Jana Polianskaja

Data Engineer at Accenture

Build Scalable Data Pipelines with Apache Beam and Google Cloud Dataflow

As a data engineer, I find several features of Google Cloud Dataflow particularly valuable. The ability to test solutions locally using Direct Runner is crucial for development, allowing me to validate pipelines without incurring the costs of full Dataflow jobs. The unified programming model for both batch and streaming processing is exceptional - requiring only minor code adjustments to optimize for either mode. This flexibility extends to language support, with robust implementations in both Java and Python, allowing teams to leverage their existing expertise. The platform's comprehensive monitoring capabilities are another standout feature. The intuitive interface, Grafana integration, and extensive service connectivity make troubleshooting and performance tracking highly efficient. Furthermore, seamless integration with Google Cloud Composer (managed Airflow) enables sophisticated orchestration of data pipelines.

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

879,853 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Manufacturing Company

16%

Financial Services Firm

11%

Computer Software Company

11%

University

Financial Services Firm

19%

Manufacturing Company

12%

Retailer

11%

Healthcare Company

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	3
Midsize Enterprise	2
Large Enterprise	10

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 Google Cloud Dataflow?

The product's installation process is easy...The tool's maintenance part is somewhat easy.

See all answers

What is your experience regarding pricing and costs for Google Cloud Dataflow?

Pricing is normal. It is part of a package received from Google, and they are not charging us too high.

See all answers

What needs improvement with Google Cloud Dataflow?

It can be improved in several ways. The system could function in an automated fashion and provide suggestions based on past transactions to achieve better scalability. Implementing AI-based suggest...

See all answers

Comparisons

AWS Lambda vs Apache NiFi

Compared 22% of the time

Apache Spark vs Apache NiFi

Compared 15% of the time

Amazon EC2 vs Apache NiFi

Compared 10% of the time

Apache Storm vs Apache NiFi

Compared 7% of the time

IBM Streams vs Apache NiFi

Compared 5% of the time

More Apache NiFi Competitors

Databricks vs Google Cloud Dataflow

Compared 26% of the time

Apache Flink vs Google Cloud Dataflow

Compared 19% of the time

Spring Cloud Data Flow vs Google Cloud Dataflow

Compared 6% of the time

AWS Lambda vs Google Cloud Dataflow

Compared 6% of the time

Qlik Talend Cloud vs Google Cloud Dataflow

Compared 6% of the time

More Google Cloud Dataflow Competitors

Product Reports

Buyer's Guide

Apache NiFi

January 2026

Download Apache NiFi product report

Buyer's Guide

Google Cloud Dataflow

December 2025

Download Google Cloud Dataflow product report

Also Known As

No data available

Google Dataflow

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

Google Dataflow is a unified programming model and a managed service for developing and executing a wide range of data processing patterns including ETL, batch computation, and continuous computation. Cloud Dataflow frees you from operational tasks like resource management and performance optimization.

Google

Sample Customers

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

Absolutdata, Backflip Studios, Bluecore, Claritics, Crystalloids, Energyworx, GenieConnect, Leanplum, Nomanini, Redbus, Streak, TabTale

Find out what your peers are saying about Amazon Web Services (AWS), Apache, Spot - A Flexera company and others in Compute Service. Updated: January 2026.

DOWNLOAD NOW

879,853 professionals have used our research since 2012.

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.