Google Cloud Dataflow vs Redpanda comparison

Google and Redpanda Data are both solutions in the Streaming Analytics category. Google is ranked #7 with an average rating of 8.0, while Redpanda Data is ranked #15 with an average rating of 8.8. Additionally, 92% of Google users are willing to recommend the solution, compared to 100% of Redpanda Data users who would recommend it.

Google Cloud Dataflow

Read 13 Google Cloud Dataflow reviews

3,122 Views
2,513 Comparison Views

92% willing to recommend

Redpanda

Read 4 Redpanda reviews

202 Views
77 Comparison Views

100% willing to recommend

Google Cloud Dataflow

Redpanda

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Executive SummaryUpdated on Dec 17, 2024

Google Cloud Dataflow and Redpanda are competing products in the stream processing and data pipeline space. Based on comparative analysis, Redpanda seems to have the upper hand with its innovative feature set, making it a valuable investment for sophisticated data requirements.

Features: Google Cloud Dataflow provides advanced features for real-time data processing, including auto-scaling and integration with other Google Cloud services. Its flexibility and scalability add to its strengths. Redpanda is notable for its low-latency performance and high-throughput functionalities, alongside its ability to easily handle data streaming and analytics. Its robust architecture offers significant advantages to users focusing on high performance and resilience.

Room for Improvement: Google's focus could be improved by introducing more innovative features to compete with newer platforms. Additionally, enhancing real-time processing capacities would make it more competitive. Redpanda could improve by expanding its deployment options beyond on-premise and hybrid, offering more cloud-based services. Enhancing documentation and broader community support would further strengthen its user experience. Simplifying initial setup processes might also be beneficial for new users.

Ease of Deployment and Customer Service: Google Cloud Dataflow offers seamless cloud integration, leveraging Google's infrastructure for smooth deployment, along with robust customer service. Redpanda focuses on simplicity, offering straightforward on-premise deployment that can run in hybrid environments. While both have strong customer service, Google provides more extensive resources and documentation.

Pricing and ROI: Google Cloud Dataflow generally has transparent pricing with pay-as-you-go plans, optimized for budget-conscious operations. Redpanda, despite potentially higher initial costs, demonstrates significant long-term ROI with its efficient performance leading to reduced operational expenses. Google offers sustainable pricing, whereas Redpanda's high performance could lead to potential cost savings over time.

To learn more, read our detailed Google Cloud Dataflow vs. Redpanda Report (Updated: April 2025).

Buyer's Guide

Google Cloud Dataflow vs. Redpanda

April 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

Google Cloud Dataflow

Ranking in Streaming Analytics

7th

Average Rating

8.0

Reviews Sentiment

7.3

Number of Reviews

Ranking in other categories

No ranking in other categories

Redpanda

Ranking in Streaming Analytics

15th

Average Rating

8.8

Reviews Sentiment

7.8

Number of Reviews

Ranking in other categories

No ranking in other categories

Featured Reviews

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.

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Vishal M Godi

Big Data Teaching Assistant at Center for Cloud Computing and Big Data, PES University

High-performance message brokering with excellent documentation and an easy setup

The industry standard for this kind of platform is Kafka. Confluent Kafka has acquired it. Kafka is an open-source platform built by Apache. Confluent is the commercial version of it. The major improvement of Redpanda over Kafka is firstly, good documentation. Redpanda's documentation is very easily understandable, and they have a lot of examples. In addition to that, most of the setups include using another technology called Docker, which I am very familiar with. Setting up technologies using Docker is very convenient to me, and Redpanda has provided many templates for that. Redpanda has its own built-in metrics exporter, making it easier to monitor and check performance. What makes Redpanda superior is its performance since it's written in C++. C++ is pretty much the standard for high-performance applications.

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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 support team is good and it's easy to use."

"It is a scalable solution."

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

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

"I would rate the overall solution a ten out of ten."

"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 integration within Google Cloud Platform is very good."

More Google Cloud Dataflow pros

"What makes Redpanda superior is its performance since it's written in C++. C++ is pretty much the standard for high-performance applications."

"I tested it with ten-plus nodes, and it's highly scalable."

"The UI is modern."

"The cost savings have been significant."

Cons

"The deployment time could also be reduced."

"They should do a market survey and then make improvements."

"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."

"Promoting the technology more broadly would help increase its adoption."

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

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

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

More Google Cloud Dataflow cons

"The version control mechanism must be improved."

"The command-line tools need to be improved. To quickly check the status of the topics and all."

"When it comes to self-hosting, their documentation could be improved."

"Recently, for the documentation, they've built their own AI chatbot, which is focused on giving you answers based on their documentation. While using that, I did not find it to be very good."

Pricing and Cost Advice

"The tool is cheap."

"Google Cloud is slightly cheaper than AWS."

"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 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 Google Cloud Dataflow's pricing a four out of ten."

"The solution is cost-effective."

"Google Cloud Dataflow is a cheap solution."

"The solution is not very expensive."

"Redpanda is cheaper than its competitors."

"It's free. Everybody can use it, only support is paid."

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Top Industries

By visitors reading reviews

Financial Services Firm

17%

Manufacturing Company

12%

Retailer

11%

Computer Software Company

10%

No data available

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

Questions from the Community

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

What is your experience regarding pricing and costs for Redpanda?

Redpanda is actually a commercial platform, but they do provide free versions as well. I've been working only with the free versions.

See all answers

What needs improvement with Redpanda?

Recently, for the documentation, they've built their own AI chatbot, which is focused on giving you answers based on their documentation. While using that, I did not find it to be very good. Maybe ...

See all answers

What is your primary use case for Redpanda?

I have worked with Redpanda for the past two to three months. Mainly in the tech industry or software industry, there's a huge rise of streaming data. Redpanda serves as a very reliable and fast me...

See all answers

Comparisons

Databricks vs Google Cloud Dataflow

Compared 39% of the time

Apache Flink vs Google Cloud Dataflow

Compared 15% of the time

Apache NiFi vs Google Cloud Dataflow

Compared 12% 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

More Google Cloud Dataflow Competitors

Amazon Kinesis vs Redpanda

Compared 56% of the time

Apache Pulsar vs Redpanda

Compared 44% of the time

More Redpanda Competitors

Product Reports

Buyer's Guide

Google Cloud Dataflow

April 2025

Download Google Cloud Dataflow product report

Buyer's Guide

Redpanda

April 2025

Download Redpanda product report

Also Known As

Google Dataflow

No data available

Overview

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

Redpanda offers a modern, intuitive interface with efficient resource usage, seamlessly integrating with Kafka, and enhancing performance through fast operations and reliable support. Organizations benefit from its memory efficiency and high performance for demanding data workloads.

Built on a C++ foundation, Redpanda integrates easily with Kafka clients and stands out for fast operations, simplified Docker setup, and effective metrics monitoring. Performance is enhanced by memory efficiency and high throughput capabilities. The community provides robust support, and clear documentation aids the adoption process. However, improvements could be made in version control, command-line tools, and documentation, particularly in areas such as automation file management and chatbot documentation assistance. Redpanda is widely utilized in data streaming and normalization, efficiently handling large telemetry data volumes with minimal latency, essential for building asynchronous applications across microservices and monitoring systems.

What are the most important features of Redpanda?

Modern UI: Provides an intuitive interface for ease of use.
Efficient Resource Usage: Built on C++ for performance optimization.
Seamless Integration: Works well with Kafka clients for smooth operations.
Simplified Setup: Docker templates facilitate easy installation.
Built-in Metrics Exporters: Enhance monitoring and visibility.
High Performance: Ensures memory efficiency and quick data processing.

What benefits and ROI can users expect?

Fast Operations: Accelerates data processing for efficient workflows.
Reliable Support: Strong community aids troubleshooting and development.
Clear Documentation: Helps users quickly onboard and manage Redpanda.
Low Latency: Critical for applications requiring quick response times.
Sub-second Response: Supports demanding performance and latency needs.

Redpanda is commonly implemented in tech and software industries to streamline data streaming and normalization processes, handling high telemetry data volumes effectively. Its capacity for sub-second response times makes it crucial for companies developing asynchronous applications, especially in microservices and monitoring systems.

Redpanda Data

Sample Customers

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

Information Not Available

Buyer's Guide

Google Cloud Dataflow vs. Redpanda

April 2025

Free Report: Google Cloud Dataflow vs. Redpanda

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