Apache Flink vs Google Cloud Dataflow comparison

Why PeerSpot?

Apache and Google are both solutions in the Streaming Analytics category. Apache is ranked #4 with an average rating of 8.0, while Google is ranked #12 with an average rating of 8.4. Apache holds a 6.9% mindshare in SA, compared to Google’s 3.5% mindshare. Additionally, 95% of Apache users are willing to recommend the solution, compared to 93% of Google users who would recommend it.

Apache Flink

Read 19 Apache Flink reviews

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5,311 Comparison Views

95% willing to recommend

Google Cloud Dataflow

Read 15 Google Cloud Dataflow reviews

3,224 Views
2,773 Comparison Views

93% willing to recommend

Apache Flink

Google Cloud Dataflow

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 17, 2024

Google Cloud Dataflow and Apache Flink are competing products in the realm of stream and batch data processing. Google Cloud Dataflow often has a favorable edge with its simplified managed service model, making it more appealing for businesses seeking ease of use, whereas Apache Flink may emerge as a superior choice due to its robust features and flexibility, attracting those who value detailed customization over simplicity.

Features: Google Cloud Dataflow offers seamless integration with Google Cloud Platform services, providing comprehensive scalability and real-time processing capabilities. It is equipped with auto-scaling and adaptive workload handling. Apache Flink stands out with stateful computations, low latency, and unified stream and batch processing, offering versatility and support for complex event processing.

Room for Improvement: Google Cloud Dataflow may enhance its flexibility to support wider coding language preferences beyond what is currently available. Its interface can be enriched for more intuitive use. Apache Flink, while powerful, demands significant manual configuration, and improvements could be made in reducing setup complexity. Further enhancement of community support resources would also benefit new users.

Ease of Deployment and Customer Service: Google Cloud Dataflow simplifies deployment with its fully managed service and benefits from Google’s global support network, facilitating quick problem-solving. On the other hand, Apache Flink requires more manual efforts for deployment, demanding technical expertise for optimal performance. Its open-source nature allows for flexibility but relies heavily on community support instead of a dedicated customer service model.

Pricing and ROI: Google Cloud Dataflow provides cost advantages with its pay-as-you-go model, appealing to businesses mindful of budget considerations. Its ROI is enhanced by operational cost reduction through managed services. Apache Flink, being open-source, incurs low initial setup costs but may require investment in dedicated resources for operation and maintenance. Its ROI could be higher for organizations leveraging its capabilities for complex data processing tasks.

To learn more, read our detailed Apache Flink vs. Google Cloud Dataflow Report (Updated: September 2026).

Buyer's Guide

Apache Flink vs. Google Cloud Dataflow

September 2026

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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 Flink

Ranking in Streaming Analytics

4th

Average Rating

7.8

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

No ranking in other categories

Google Cloud Dataflow

Ranking in Streaming Analytics

12th

Average Rating

8.0

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of October 2026, in the Streaming Analytics category, the mindshare of Apache Flink is 6.9%, down from 14.6% compared to the previous year. The mindshare of Google Cloud Dataflow is 3.5%, down from 5.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Mindshare Distribution
Product	Mindshare (%)
Apache Flink	6.9%
Google Cloud Dataflow	3.5%
Other	89.6%

Streaming Analytics

Featured Reviews

Sanjay Srivastava

Software Architect at IBM

Streaming workflows have improved data integration and support real-time pipelines across platforms

We are not using Apache Flink in its advanced window capabilities. We are using the Apache Flink job in Apache SeaTunnel, meaning we can write the code inside Apache SeaTunnel. Currently, we are moving; both solutions are there. We are doing it on-premises with the help of Kubernetes and OpenShift. The main reason why Apache Flink is better is that it has more functions, and being open source with easy code in Apache SeaTunnel helps us achieve that. Cost is a major issue. I would rate the stability of the product as an eight. For Apache Flink, the final point can be rated an eight. I can recommend Apache Flink to other users for streaming support, and I am recommending it. I would rate this review an eight overall.

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Mohammed Aaqibuddin

Senior Cdp Specialist at DigitasLBi

Unified user personas have improved data workflows and support detailed monitoring and logging

Google Cloud has many streams and products. In Google Cloud, everything is translated in the backend, so we do not have to use services such as Apache Beam. When you want to use Google Cloud Functions, you write the code, and the backend talks to all the libraries or Apache, so we do not need to be concerned about those. We just need to use our functions that translate and have many tools and services readily available. Google Cloud Dataflow has made it very easy for detailed monitoring and logging features for pipeline performance assessment. For example, if I am using Google Cloud Functions, I can easily see what changes I have done and trace it properly. I can see what is happening with this script, how many users are affected, whether the script is working, what is failing, and how we can rectify issues with proper monitoring.

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

"Another feature is how Flink handles its radiuses. It has something called the checkpointing concept. You're dealing with billions and billions of requests, so your system is going to fail in large storage systems. Flink handles this by using the concept of checkpointing and savepointing, where they write the aggregated state into some separate storage. So in case of failure, you can basically recall from that state and come back."

"This is truly a real-time solution."

"Easy to deploy and manage."

"It is user-friendly and the reporting is good."

"It provides us the flexibility to deploy it on any cluster without being constrained by cloud-based limitations."

"The product helps us to create both simple and complex data processing tasks. Over time, it has facilitated integration and navigation across multiple data sources tailored to each client's needs. We use Apache Flink to control our clients' installations."

"We are very happy with the product, and we have been able to achieve all of the use cases that we are expected to deliver for our customers."

"Apache Flink offers a range of powerful configurations and experiences for development teams. Its strength lies in its development experience and capabilities."

More Apache Flink pros

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

"The solution allows us to program in any language we desire."

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

"It is a scalable solution."

"Google Cloud Dataflow has made it very easy for detailed monitoring and logging features for pipeline performance assessment."

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

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

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

More Google Cloud Dataflow pros

Cons

"There are more libraries that are missing and also maybe more capabilities for machine learning."

"Apache should provide more examples and sample code related to streaming to help me better adapt and utilize the tool."

"The TimeWindow feature is a bit tricky. The timing of the content and the windowing is a bit changed in 1.11. They have introduced watermarks. A watermark is basically associating every data with a timestamp. The timestamp could be anything, and we can provide the timestamp. So, whenever I receive a tweet, I can actually assign a timestamp, like what time did I get that tweet. The watermark helps us to uniquely identify the data. Watermarks are tricky if you use multiple events in the pipeline. For example, you have three resources from different locations, and you want to combine all those inputs and also perform some kind of logic. When you have more than one input screen and you want to collect all the information together, you have to apply TimeWindow all. That means that all the events from the upstream or from the up sources should be in that TimeWindow, and they were coming back. Internally, it is a batch of events that may be getting collected every five minutes or whatever timing is given. Sometimes, the use case for TimeWindow is a bit tricky. It depends on the application as well as on how people have given this TimeWindow. This kind of documentation is not updated. Even the test case documentation is a bit wrong. It doesn't work. Flink has updated the version of Apache Flink, but they have not updated the testing documentation. Therefore, I have to manually understand it. We have also been exploring failure handling. I was looking into changelogs for which they have posted the future plans and what are they going to deliver. We have two concerns regarding this, which have been noted down. I hope in the future that they will provide this functionality. Integration of Apache Flink with other metric services or failure handling data tools needs some kind of update or its in-depth knowledge is required in the documentation. We have a use case where we want to actually analyze or get analytics about how much data we process and how many failures we have. For that, we need to use Tomcat, which is an analytics tool for implementing counters. We can manage reports in the analyzer. This kind of integration is pretty much straightforward. They say that people must be well familiar with all the things before using this type of integration. They have given this complete file, which you can update, but it took some time. There is a learning curve with it, which consumed a lot of time. It is evolving to a newer version, but the documentation is not demonstrating that update. The documentation is not well incorporated. Hopefully, these things will get resolved now that they are implementing it. Failure is another area where it is a bit rigid or not that flexible. We never use this for scaling because complexity is very high in case of a failure. Processing and providing the scaled data back to Apache Flink is a bit challenging. They have this concept of offsetting, which could be simplified."

"The solution could be more user-friendly."

"I am using the Python API and I have found the solution to be underdeveloped compared to others. There needs to be better integration with notebooks to allow for more practical development."

"Flink has become a lot more stable but the machine learning library is still not very flexible."

"There is room for improvement in the initial setup process."

"The technical support from Apache is not good; support needs to be improved. I would rate them from one to ten as not good."

More Apache Flink cons

"The technical support has slight room for improvement."

"The technical support is very hard to reach."

"The system could function in an automated fashion and provide suggestions based on past transactions to achieve better scalability."

"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."

"When I deploy the product in local errors, a lot of errors pop up which are not always caught. The solution's error logging is bad. It can take a lot of time to debug the errors. It needs to have better logs."

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

"Compared to other support systems, such as those in Braze, Tealium, Google, and others like Adobe, Google Cloud takes more time because it is a bigger company."

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

More Google Cloud Dataflow cons

Pricing and Cost Advice

"Apache Flink is open source so we pay no licensing for the use of the software."

"This is an open-source platform that can be used free of charge."

"It's an open-source solution."

"The solution is open-source, which is free."

"It's an open source."

"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 solution is cost-effective."

"Google Cloud is slightly cheaper than AWS."

"The solution is not very expensive."

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

"The tool is cheap."

"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."

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Top Industries

By visitors reading reviews

Financial Services Firm

16%

Retailer

14%

Computer Software Company

Manufacturing Company

Financial Services Firm

17%

Manufacturing Company

13%

Comms Service Provider

Construction Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	5
Midsize Enterprise	3
Large Enterprise	12

By reviewers
Company Size	Count
Small Business	3
Midsize Enterprise	2
Large Enterprise	12

Questions from the Community

What needs improvement with Apache Flink?

Apache could improve Apache Flink by providing more functionality, as they need to fully support data integration. The connectors are still very few for Apache Flink. There is a lack of functionali...

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What is your primary use case for Apache Flink?

I am working with Apache Flink, which is the tool we use for data integration. Apache Flink is for data, and we are working on the data integration project, not big data, using Apache Flink and Apa...

See all answers

What advice do you have for others considering Apache Flink?

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 feel there could be something that they can introduce, such as when we have data in the tables, a feature that creates a unique persona of the user automatically, so we do not have to do that man...

See all answers

What is your primary use case for Google Cloud Dataflow?

The primary use case for Google Cloud Dataflow is when a brand has a lot of data and wants to store it in their warehouse. They can use BigQuery to store their data or use big data solutions to sto...

See all answers

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Compared 14% of the time

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Compared 10% of the time

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Compared 9% of the time

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Compared 6% of the time

IBM Streams vs Google Cloud Dataflow

Compared 5% of the time

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Product Reports

Buyer's Guide

Apache Flink

October 2026

Download Apache Flink product report

Buyer's Guide

Google Cloud Dataflow

October 2026

Download Google Cloud Dataflow product report

Also Known As

Flink

Google Dataflow

Overview

Apache Flink is a powerful open-source framework for stateful computations over data streams, designed for both real-time and batch processing. It efficiently handles massive volumes of data with low-latency responses, offering versatility for complex event processing scenarios.

Apache Flink excels in processing high-throughput data streams, enabling seamless state management across distributed applications. Users appreciate its robust features like stateful transformations and checkpointing, simplifying deployment in diverse environments. Though powerful, it poses challenges for beginners due to its complexity and limited documentation, requiring some prior experience to master. Its flexible integration with systems like Kafka and support for Kubernetes on AWS makes it suitable for demanding environments where quick, real-time analysis is essential.

What are the key features of Apache Flink?

Stateful Transformations: Allows complex stateful operations on data streams with precise handling.
Low Latency: Ensures real-time data processing with minimal delays.
Checkpointing: Provides efficient and reliable checkpointing for fault tolerance.
Kafka Integration: Easy integration with Kafka for seamless data ingestion and processing.
API Support: Provides robust APIs for diverse data processing needs.
Flexible Deployment: Offers options for deploying on-premise or in cloud environments.

What benefits should users look for?

Versatility: Supports both batch and stream processing in a unified model.
Community Support: Backed by an active community that continuously enhances its features.
Ease of Use: Simplifies the coding process compared to similar frameworks like Apache Storm.
Real-Time Analytics: Facilitates immediate insights and data-driven decision-making.

Organizations leverage Apache Flink primarily for real-time data processing in sectors such as retail, transportation, and telecommunications. By deploying on AWS with Kubernetes, companies can utilize it for data cleaning, generating customer insights, and providing swift real-time updates. It effectively manages millions of events per second, serving use cases like cab aggregations, map-making, and outlier detection in telecom networks, enabling seamless integration of streaming data with existing pipelines.

Apache

Google Cloud Dataflow provides scalable batch and streaming data processing with Apache Beam integration, supporting Python and Java. It's designed for efficient data transformations, analytics, and machine learning, featuring cost-effective serverless operations.

Google Cloud Dataflow is a robust tool for handling large-scale data processing tasks with flexibility in processing batch and streaming workloads. It integrates seamlessly with other Google Cloud services like Pub/Sub for real-time messaging and BigQuery for advanced analytics. The platform supports a wide array of data transformation and preparation needs, making it suitable for complex data workflows and machine learning applications. Despite its advantages, users have noted challenges such as incomplete error logs, longer job startup times, and some limitations in the Python SDK.

What are the key features of Google Cloud Dataflow?

Apache Beam Integration: Allows for advanced data processing capabilities with extensive library support.
Flexible Language Support: Works seamlessly with Python and Java for diverse application requirements.
Scalable Processing: Manages both batch and streaming data efficiently to meet varying data loads.
Cost-Effective Model: Operates on a pay-as-you-go basis, optimizing resource expenditure.
Monitoring Tools: Provides comprehensive assessments to enhance pipeline performance.

What benefits do users experience with Google Cloud Dataflow?

Real-Time Analytics: Facilitates timely data insights essential for fast decision-making.
Integrated Ecosystem: Simplifies orchestration with services like Cloud Composer, enhancing workflow connectivity.
Data Transformation: Enhances machine learning models preparation with robust data cleansing capabilities.

Industries, especially in retail and eCommerce, implement Google Cloud Dataflow for effective batch job execution, data transformation, and event stream processing. It aids in constructing distributed data pipelines for handling extensive analytics tasks, supporting effective large-scale data-driven decisions.

Google

Sample Customers

LogRhythm, Inc., Inter-American Development Bank, Scientific Technologies Corporation, LotLinx, Inc., Benevity, Inc.

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

Buyer's Guide

Apache Flink vs. Google Cloud Dataflow

September 2026

Free Report: Apache Flink vs. Google Cloud Dataflow

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