Apache Flink vs Google Cloud Dataflow comparison

Apache and Google are both solutions in the Streaming Analytics category. Apache is ranked #6 with an average rating of 7.8, while Google is ranked #7 with an average rating of 8.0. Apache holds a 13.2% mindshare in SA, compared to Google’s 7.4% mindshare. Additionally, 94% of Apache users are willing to recommend the solution, compared to 92% of Google users who would recommend it.

Apache Flink

Read 16 Apache Flink reviews

5,090 Views
3,740 Comparison Views

94% willing to recommend

Google Cloud Dataflow

Read 12 Google Cloud Dataflow reviews

3,122 Views
2,513 Comparison Views

92% 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: March 2025).

Buyer's Guide

Apache Flink vs. Google Cloud Dataflow

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 Flink

Ranking in Streaming Analytics

6th

Average Rating

7.6

Reviews Sentiment

6.9

Number of Reviews

Ranking in other categories

No ranking in other categories

Google Cloud Dataflow

Ranking in Streaming Analytics

7th

Average Rating

7.8

Reviews Sentiment

7.3

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of April 2025, in the Streaming Analytics category, the mindshare of Apache Flink is 13.2%, up from 9.5% compared to the previous year. The mindshare of Google Cloud Dataflow is 7.4%, up from 7.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics

Featured Reviews

Ilya Afanasyev

Senior Software Development Engineer at Yahoo!

A great solution with an intricate system and allows for batch data processing

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

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

"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."

"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."

"The documentation is very good."

"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."

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

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

"Allows us to process batch data, stream to real-time and build pipelines."

"This is truly a real-time solution."

More Apache Flink pros

"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 friendly, and cost-effective if you know how to use it. 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."

"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 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."

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

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

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

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

More Google Cloud Dataflow pros

Cons

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

"The solution could be more user-friendly."

"Apache Flink's documentation should be available in more languages."

"In terms of stability with Flink, it is something that you have to deal with every time. Stability is the number one problem that we have seen with Flink, and it really depends on the kind of problem that you're trying to solve."

"There is a learning curve. It takes time to learn."

"One way to improve Flink would be to enhance integration between different ecosystems. For example, there could be more integration with other big data vendors and platforms similar in scope to how Apache Flink works with Cloudera. Apache Flink is a part of the same ecosystem as Cloudera, and for batch processing it's actually very useful but for real-time processing there could be more development with regards to the big data capabilities amongst the various ecosystems out there."

"PyFlink is not as fully featured as Python itself, so there are some limitations to what you can do with it."

"Apache Flink should improve its data capability and data migration."

More Apache Flink cons

"The deployment time could also be reduced."

"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."

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

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

"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."

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

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."

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

"It's an open source."

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

"It's an open-source solution."

"The solution is cost-effective."

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

"Google Cloud is slightly cheaper than AWS."

"The tool is cheap."

"Google Cloud Dataflow is a cheap solution."

"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 not very expensive."

"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

23%

Computer Software Company

16%

Manufacturing Company

Retailer

Financial Services Firm

18%

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 Flink?

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. ...

See all answers

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?

There are more libraries that are missing and also maybe more capabilities for machine learning. It could have a friendly user interface for pipeline configuration, deployment, and monitoring.

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?

Google Cloud Dataflow costs are primarily driven by compute resources (worker type and count) and data volume. However, other factors like pipeline complexity also contribute significantly to the t...

See all answers

What needs improvement with Google Cloud Dataflow?

Apache Beam represents a powerful data processing solution that deserves wider recognition in the broader tech community. This technology offers remarkable capabilities for handling data at scale, ...

See all answers

Comparisons

Spring Cloud Data Flow vs Apache Flink

Compared 25% of the time

Databricks vs Apache Flink

Compared 21% of the time

Azure Stream Analytics vs Apache Flink

Compared 13% of the time

Amazon Kinesis vs Apache Flink

Compared 10% of the time

Amazon MSK vs Apache Flink

Compared 5% of the time

More Apache Flink Competitors

Databricks vs Google Cloud Dataflow

Compared 42% 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 6% 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 Flink

March 2025

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Buyer's Guide

Streaming Analytics

March 2025

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

March 2025

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: March 2025.

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