Apache Flink vs Google Cloud Dataflow comparison

Apache and Google are both solutions in the Streaming Analytics category. Apache is ranked #3 with an average rating of 8.0, while Google is ranked #9 with an average rating of 8.4. Apache holds a 9.8% mindshare in SA, compared to Google’s 3.9% 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

4,064 Views
4,064 Comparison Views

95% willing to recommend

Google Cloud Dataflow

Read 15 Google Cloud Dataflow reviews

2,566 Views
2,181 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: April 2026).

Buyer's Guide

Apache Flink vs. Google Cloud Dataflow

April 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

3rd

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

9th

Average Rating

8.0

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of April 2026, in the Streaming Analytics category, the mindshare of Apache Flink is 9.8%, down from 13.1% compared to the previous year. The mindshare of Google Cloud Dataflow is 3.9%, down from 7.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Mindshare Distribution
Product	Mindshare (%)
Apache Flink	9.8%
Google Cloud Dataflow	3.9%
Other	86.3%

Streaming Analytics

Featured Reviews

Aswini Atibudhi

Distinguished AI Leader at Walmart Global Tech at Walmart

Enables robust real-time data processing but documentation needs refinement

Apache Flink is very powerful, but it can be challenging for beginners because it requires prior experience with similar tools and technologies, such as Kafka and batch processing. It's essential to have a clear foundation; hence, it can be tough for beginners. However, once they grasp the concepts and have examples or references, it becomes easier. Intermediate users who are integrating with Kafka or other sources may find it smoother. After setting up and understanding the concepts, it becomes quite stable and scalable, allowing for customization of jobs. Every software, including Apache Flink, has room for improvement as it evolves. One key area for enhancement is user-friendliness and the developer experience; improving documentation and API specifications is essential, as they can currently be verbose and complex. Debugging and local testing pose challenges for newcomers, particularly when learning about concepts such as time semantics and state handling. Although the APIs exist, they aren't intuitive enough. We also need to simplify operational procedures, such as developing tools and tuning Flink clusters, as these processes can be quite complex. Additionally, implementing one-click rollback for failures and improving state management during dynamic scaling while retaining the last states is vital, as the current large states pose scaling challenges.

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reviewer2812851

Senior Customer Data Platform Specialist at a marketing services firm with 1,001-5,000 employees

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.

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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 ease of usage, even for complex tasks, stands out."

"The main advantage is the turnaround time, which has been reduced drastically because of Apache Flink, and now everything is in almost real time with no waiting or lag of data in the application while machine resources are utilized much more efficiently."

"The event processing function is the most useful or the most used function. The filter function and the mapping function are also very useful because we have a lot of data to transform. For example, we store a lot of information about a person, and when we want to retrieve this person's details, we need all the details. In the map function, we can actually map all persons based on their age group. That's why the mapping function is very useful. We can really get a lot of events, and then we keep on doing what we need to do."

"Flink moved on to becoming a standard technology for location platform."

"With Flink, it provides out-of-the-box checkpointing and state management, guaranteed message processing, and it also helped us with application maintenance, deployments, and restarts."

"With Flink, it provides out-of-the-box checkpointing and state management. It helps us in that way. When Storm used to restart, sometimes we would lose messages. With Flink, it provides guaranteed message processing, which helped us. It also helped us with maintenance or restarts."

"We value this solution's intricate system because it comes with a state inside the mechanism and product, allowing us to process batch data, stream to real-time and build pipelines, and we do not need to process data from the beginning when we pause as we can continue from the same point where we stopped, helping us save time as 95% of our pipelines will now be on Amazon and we'll save money by saving time."

"Apache Flink's best feature is its data streaming tool."

More Apache Flink pros

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

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

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

"The integration within Google Cloud Platform is very good."

"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."

"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 the integration, it's very simple if you have the complete stack, which we are using, it is overall very easy to use, user-friendly, and cost-effective if you know how to use it, and the solution is very flexible for programmers, if you know how to do scripts or program in Python or any other language, it's extremely easy to use."

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

More Google Cloud Dataflow pros

Cons

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

"The solution could be more user-friendly."

"In a future release, they could improve on making the error descriptions more clear."

"Apache Flink is very powerful, but it can be challenging for beginners because it requires prior experience with similar tools and technologies, such as Kafka and batch processing."

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

"The state maintains checkpoints and they use RocksDB or S3; they are good but sometimes the performance is affected when you use RocksDB for checkpointing."

"We have a machine learning team that works with Python, but Apache Flink does not have full support for the language."

"Failure is another area where it is a bit rigid or not that flexible."

More Apache Flink cons

"Currently, not all error logs are available to users and this could make debugging failed jobs very difficult."

"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."

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

"I would like Google Cloud Dataflow to be integrated with IT data flow and other related services to make it easier to use as it is a complex tool."

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

"The technical support is very hard to reach."

"Occasionally, dealing with a huge volume of data causes failure due to array size."

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

More Google Cloud Dataflow cons

Pricing and Cost Advice

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

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

"It's an open-source solution."

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

"It's an open source."

"The solution is not very expensive."

"Google Cloud is slightly cheaper than AWS."

"The tool is cheap."

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

"The solution is cost-effective."

"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."

"Google Cloud Dataflow is a cheap solution."

"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."

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Top Industries

By visitors reading reviews

Financial Services Firm

18%

Retailer

12%

Computer Software Company

Manufacturing Company

Financial Services Firm

20%

Manufacturing Company

13%

Retailer

10%

Computer Software 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	11

Questions from the Community

What is your experience regarding pricing and costs for Apache Flink?

The solution is expensive. I rate the product’s pricing a nine out of ten, where one is cheap and ten is expensive.

See all answers

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...

See all answers

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 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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Product Reports

Buyer's Guide

Apache Flink

April 2026

Download Apache Flink product report

Buyer's Guide

Google Cloud Dataflow

April 2026

Download Google Cloud Dataflow product report

Also Known As

Flink

Google Dataflow

Overview

Apache Flink is an open-source batch and stream data processing engine. It can be used for batch, micro-batch, and real-time processing. Flink is a programming model that combines the benefits of batch processing and streaming analytics by providing a unified programming interface for both data sources, allowing users to write programs that seamlessly switch between the two modes. It can also be used for interactive queries.

Flink can be used as an alternative to MapReduce for executing iterative algorithms on large datasets in parallel. It was developed specifically for large to extremely large data sets that require complex iterative algorithms.

Flink is a fast and reliable framework developed in Java, Scala, and Python. It runs on the cluster that consists of data nodes and managers. It has a rich set of features that can be used out of the box in order to build sophisticated applications.

Flink has a robust API and is ready to be used with Hadoop, Cassandra, Hive, Impala, Kafka, MySQL/MariaDB, Neo4j, as well as any other NoSQL database.

Apache Flink Features

Distributed execution of streaming programs on clusters of computers
Support for multiple data sources and sinks: this includes Hadoop file systems, databases, and other data sources
Streaming SQL query engine with support for windowing functions
Low latency query execution in milliseconds
Runs in a distributed fashion: it can be deployed on multiple machines or nodes to increase performance and reliability of data processing pipelines.
Powerful API that supports both batch and streaming applications
Runs on clusters of commodity hardware with minimal configuration
Can be integrated with other technologies, such as Apache Spark for complex data mining

Apache Flink Benefits

Ease of use: Flink has an intuitive API and provides high-level abstractions for handling data streams. Even beginners in the field can work with the platform with ease.

Fault tolerance: Flink can automatically detect and recover from failures in the system.

Scalability: Flink scales to thousands of nodes. It can run on clusters of any size and the user does not have to worry about managing the cluster.

Reviews from Real Users

Apache Flink stands out among its competitors for a number of reasons. Two major ones are its low latency and its user-friendly interface. PeerSpot users take note of the advantages of these features in their reviews:

The head of data and analytics at a computer software company notes, “The top feature of Apache Flink is its low latency for fast, real-time data. Another great feature is the real-time indicators and alerts which make a big difference when it comes to data processing and analysis.”

Ertugrul A., manager at a computer software company, writes, “It's usable and affordable. It is user-friendly and the reporting is good.”

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

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

April 2026

Free Report: Apache Flink vs. Google Cloud Dataflow

Find out what your peers are saying about Apache Flink vs. Google Cloud Dataflow and other solutions. Updated: April 2026.

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