Apache Flink vs Google Cloud Dataflow comparison

Apache and Google are both solutions in the Streaming Analytics category. Apache is ranked #5 with an average rating of 7.6, while Google is ranked #8 with an average rating of 7.9. Apache holds a 12.1% mindshare in SA, compared to Google’s 8.4% mindshare. Additionally, 94% of Apache users are willing to recommend the solution, compared to 90% of Google users who would recommend it.

Apache Flink

Read 16 Apache Flink reviews

6,723 Views
4,716 Comparison Views

94% willing to recommend

Google Cloud Dataflow

Read 10 Google Cloud Dataflow reviews

3,811 Views
3,133 Comparison Views

90% willing to recommend

Apache Flink

Google Cloud Dataflow

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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: December 2024).

Buyer's Guide

Apache Flink vs. Google Cloud Dataflow

December 2024

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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:

ROI

No sentiment score available

Sentiment score

7.2

Startups using Google Cloud Dataflow report cost savings and up to 70% time efficiency, though full ROI is still unclear.

No quotes available

For more quotes and insights, download the Apache Flink report

No quotes available

For more quotes and insights, download the Google Cloud Dataflow report

Customer Service

Sentiment score

6.3

Users primarily depend on Apache Flink's community support and resources, occasionally opting for paid services for critical issues.

No sentiment score available

No quotes available

For more quotes and insights, download the Apache Flink report

No quotes available

For more quotes and insights, download the Google Cloud Dataflow report

Stability Issues

No sentiment score available

Sentiment score

8.2

Google Cloud Dataflow is generally viewed as stable, receiving high ratings for reliability from most users.

No quotes available

For more quotes and insights, download the Apache Flink report

No quotes available

For more quotes and insights, download the Google Cloud Dataflow report

Categories and Ranking

Apache Flink

Ranking in Streaming Analytics

5th

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

8th

Average Rating

7.8

Reviews Sentiment

7.3

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of December 2024, in the Streaming Analytics category, the mindshare of Apache Flink is 12.1%, up from 10.8% compared to the previous year. The mindshare of Google Cloud Dataflow is 8.4%, up from 6.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics

Featured Reviews

Sunil Morya

Consultant at Tata Consultancy

Easy to deploy and manage; lacking simple integration with Amazon products

The issue we had with Flink was that when you had to refer the schema into the input data stream, it had to be done directly into code. The XLS format where the schema is stored, had to be stored in Python. If the schema changes, you have to redeploy Flink because the basic tasks and jobs are already running. That's one disadvantage. Another was a restriction with Amazon's CloudFormation templates which don't allow for direct deployment in the private subnet. You have to deploy into the public subnet and then from the Amazon console, specify a different private subnet that requires a lot of settings. In general, the integration with Amazon products was not good and was very time-consuming. I'd like to think that has changed.

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Tamer Talal

Satellite System Engineer at NARSS

A tool useful for data transmission and data storage that needs to improve its authentication area

The authentication part of the product is an area of concern where improvements are required. For some common users, the solution's authentication part is difficult to use. The scalability of the product is an area of concern where improvements are required. In the future, the product should be made available at a cheaper rate.

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Top Industries

By visitors reading reviews

Financial Services Firm

23%

Computer Software Company

17%

Manufacturing Company

Healthcare Company

Financial Services Firm

17%

Retailer

12%

Computer Software Company

11%

Manufacturing 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. ...

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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.

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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.

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What do you like most about Google Cloud Dataflow?

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

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What is your experience regarding pricing and costs for Google Cloud Dataflow?

The solution is not very expensive.

See all answers

What needs improvement with Google Cloud Dataflow?

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Comparisons

Spring Cloud Data Flow vs Apache Flink

Compared 25% of the time

Databricks vs Apache Flink

Compared 22% of the time

Azure Stream Analytics vs Apache Flink

Compared 14% of the time

Amazon Kinesis vs Apache Flink

Compared 11% of the time

Apache Pulsar vs Apache Flink

Compared 4% of the time

More Apache Flink Competitors

Databricks vs Google Cloud Dataflow

Compared 42% of the time

Apache NiFi vs Google Cloud Dataflow

Compared 11% of the time

Amazon MSK vs Google Cloud Dataflow

Compared 11% of the time

Spring Cloud Data Flow vs Google Cloud Dataflow

Compared 8% 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

December 2024

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Buyer's Guide

Streaming Analytics

December 2024

Download Google Cloud Dataflow product report

Also Known As

Flink

Google Dataflow

Learn More

Apache

Google

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.”

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.

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

December 2024

Free Report: Apache Flink vs. Google Cloud Dataflow

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