Apache NiFi vs Google Cloud Dataflow comparison

The compared Apache and Google solutions aren't in the same category. Apache is ranked #8 in CS , with an average rating of 7.6, and holds a 8.0% mindshare in the category. Google is ranked #7 in SA , with an average rating of 8.0, and holds a 7.4% mindshare. Additionally, 92% of Apache users are willing to recommend the solution, compared to 92% of Google users who would recommend it.

Apache NiFi

Read 13 Apache NiFi reviews

1,880 Views
831 Comparison Views

92% willing to recommend

Google Cloud Dataflow

Read 13 Google Cloud Dataflow reviews

3,122 Views
2,513 Comparison Views

92% willing to recommend

Apache NiFi

Google Cloud Dataflow

Comparison Buyer's Guide

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Executive Summary

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

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Buyer's Guide

Compute Service

March 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

Number of Reviews

Ranking in other categories

Compute Service (8th)

Google Cloud Dataflow

Average Rating

8.0

Reviews Sentiment

7.3

Number of Reviews

Ranking in other categories

Streaming Analytics (7th)

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 8.0%, up 7.1% compared to last year.
Google Cloud Dataflow, on the other hand, focuses on Streaming Analytics, holds 7.4% mindshare, up 7.0% 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.

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

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."

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

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

"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."

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

"It is highly effective for handling real-time data by working with APIs for immediate and continuous data extraction."

"Visually, this is a good product."

"The initial setup is very easy."

More Apache NiFi pros

"The best feature of Google Cloud Dataflow is its practical connectedness."

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

"The most valuable features of Google Cloud Dataflow are scalability and connectivity."

"It allows me to test solutions locally using runners like Direct Runner without having to start a Dataflow job, which can be costly."

"It is a scalable solution."

"I don't need a server running all the time while using the tool. It is also easy to setup. The product offers a pay-as-you-go service."

"The service is relatively cheap compared to other batch-processing engines."

"Google Cloud Dataflow is useful for streaming and data pipelines."

More Google Cloud Dataflow pros

Cons

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

"More features must be added to the product."

"There should be a better way to integrate a development environment with local tools."

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

"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."

"There are some claims that NiFi is cloud-native but we have tested it, and it's not."

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

More Apache NiFi cons

"There are certain challenges regarding the Google Cloud Composer which can be improved."

"The deployment time could also be reduced."

"The solution's setup process could be more accessible."

"The authentication part of the product is an area of concern where improvements are required."

"The technical support has slight room for improvement."

"Google Cloud Data Flow can improve by having full simple integration with Kafka topics. It's not that complicated, but it could improve a bit. The UI is easy to use but the experience could be better. There are other tools available that do a better job."

"Google Cloud Dataflow should include a little cost optimization."

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

More Google Cloud Dataflow cons

Pricing and Cost Advice

"I used the tool's free version."

"We use the free version of Apache NiFi."

"The solution is open-source."

"It's an open-source solution."

"Google Cloud is slightly cheaper than AWS."

"The tool is cheap."

"On a scale from one to ten, where one is cheap, and ten is expensive, I rate Google Cloud Dataflow's pricing a four out of ten."

"Google Cloud Dataflow is a cheap solution."

"The price of the solution depends on many factors, such as how they pay for tools in the company and its size."

"On a scale from one to ten, where one is cheap, and ten is expensive, I rate the solution's pricing a seven to eight out of ten."

"The solution is cost-effective."

"The solution is not very expensive."

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Top Industries

By visitors reading reviews

Financial Services Firm

16%

Computer Software Company

14%

Manufacturing Company

Retailer

Financial Services Firm

17%

Manufacturing Company

12%

Retailer

12%

Computer Software Company

11%

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 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?

I am not sure, as we built only one job, and it is running on a daily basis. Everything else is managed using BigQuery schedulers and Talend. However, occasionally, dealing with a huge volume of da...

See all answers

Comparisons

AWS Lambda vs Apache NiFi

Compared 31% of the time

Apache Spark vs Apache NiFi

Compared 13% of the time

AWS Fargate vs Apache NiFi

Compared 10% 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

Databricks vs Google Cloud Dataflow

Compared 41% of the time

Apache Flink vs Google Cloud Dataflow

Compared 14% of the time

Amazon MSK vs Google Cloud Dataflow

Compared 9% of the time

Spring Cloud Data Flow vs Google Cloud Dataflow

Compared 7% of the time

Amazon Kinesis vs Google Cloud Dataflow

Compared 5% of the time

More Google Cloud Dataflow Competitors

Product Reports

Buyer's Guide

Apache NiFi

April 2025

Download Apache NiFi product report

Buyer's Guide

Google Cloud Dataflow

April 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 by NetApp and others in Compute Service. Updated: March 2025.

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