Apache Spark Streaming vs Google Cloud Dataflow comparison

Why PeerSpot?

Apache and Google are both solutions in the Streaming Analytics category. Apache is ranked #7 with an average rating of 7.9, while Google is ranked #12 with an average rating of 8.4. Apache holds a 4.6% mindshare in SA, compared to Google’s 3.5% mindshare. Additionally, 94% of Apache users are willing to recommend the solution, compared to 93% of Google users who would recommend it.

Apache Spark Streaming

Read 17 Apache Spark Streaming reviews

3,138 Views
3,138 Comparison Views

94% willing to recommend

Google Cloud Dataflow

Read 15 Google Cloud Dataflow reviews

3,224 Views
2,773 Comparison Views

93% willing to recommend

Apache Spark Streaming

Google Cloud Dataflow

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 17, 2024

Google Cloud Dataflow and Apache Spark Streaming compete in real-time data processing, with Dataflow having an edge due to its integration and scalability, while Spark Streaming offers flexibility and an open-source framework.

Features: Google Cloud Dataflow's integration into the Google Cloud ecosystem enhances efficient data processing and scalability. It offers a pay-as-you-go model that makes it cost-effective and relies on the open-source Apache Beam framework, providing extensive documentation. It is user-friendly, allowing programming in any language like Python. Apache Spark Streaming supports multiple languages and integrates seamlessly with other data sources, providing high-performance, low-latency real-time analytics. It offers a wide scope of batch and streaming capabilities, making it highly versatile for different data projects.

Room for Improvement: Google Cloud Dataflow could enhance its Python integration to match the seamless support offered by Spark Streaming. Additional customization options could be expanded to strengthen its adaptability in diverse environments. User feedback suggests enhancing native support beyond the Google ecosystem to broaden its usability. Apache Spark Streaming might improve its deployment simplicity, matching the ease offered by Dataflow. Community-driven documentation could be bolstered to assist newer users better. Effort should be made to simplify Spark's setup to further reduce operational overhead.

Ease of Deployment and Customer Service: Google Cloud Dataflow benefits from simple deployment within the Google Cloud Platform, backed by strong support services. Apache Spark Streaming requires more complex deployment but offers flexibility through various platforms and relies on strong community-driven documentation. Dataflow is favorable for its straightforward deployment, while Spark is appreciated for its configurability.

Pricing and ROI: Google Cloud Dataflow's pay-as-you-go pricing ensures cost-effectiveness, reducing infrastructure overhead, and offers good ROI for cloud-native applications. Apache Spark Streaming, being open-source, has lower initial setup costs but needs investment in management and infrastructure as it scales, delivering flexibility and cost advantages for existing infrastructure.

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

Buyer's Guide

Apache Spark Streaming 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 Spark Streaming

Ranking in Streaming Analytics

7th

Average Rating

7.8

Reviews Sentiment

6.4

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 Spark Streaming is 4.6%, up from 3.6% 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 Spark Streaming	4.6%
Google Cloud Dataflow	3.5%
Other	91.9%

Streaming Analytics

Featured Reviews

Khoa Dang Le

Principal AI Engineer at IMT Solutions

Have faced challenges with complex data handling and seek smoother integration for machine learning workflows

I find the fault tolerance feature beneficial because I use it for serving data from a landing area. I understand all of the structures we have for Spark SQL, Spark Streaming, and MLlib. The ability of Apache Spark Streaming to handle out-of-order data using watermarking and windowing is something we use in our pipeline. Nearly 50% of our usage is based on that because we use it for landing data, and we appreciate that we can work with it. The main benefits of Apache Spark Streaming include cost savings, time savings, and efficiency improvements about data storage. The fast storage capability is crucial because Apache Spark replaces Hadoop's MapReduce, allowing us to manage our data more efficiently.

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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.

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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

"It's the fastest solution on the market with low latency data on data transformations."

"Apache Spark Streaming's most valuable feature is near real-time analytics. The developers can build APIs easily for a code-steaming pipeline. The solutions have an ecosystem of integration with other stock services."

"The solution is better than average and some of the valuable features include efficiency and stability."

"With Apache Spark Streaming's integration with Anaconda and Miniconda with Python, I interact with databases using data frames or data sets in micro versions and create solutions based on business expectations for decision-making, logistic regression, linear regression, or machine learning which provides image or voice record and graphical data for improved accuracy."

"By integrating Apache Spark Streaming, the data freshness rate, and latency have significantly improved from 24-hour batch processing to less than one minute, facilitating faster communication to downstream systems, aiding marketing campaigns."

"The main benefits of Apache Spark Streaming include cost savings, time savings, and efficiency improvements about data storage."

"Apache Spark Streaming was straightforward in terms of maintenance. It was actively developed, and migrating from an older to a newer version was quite simple."

"The solution is very stable and reliable."

More Apache Spark Streaming pros

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

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

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

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

"It is a scalable solution."

"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."

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

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

More Google Cloud Dataflow pros

Cons

"Integrating event-level streaming capabilities could be beneficial."

"The problem is we need to use it in a certain manner. After that, we need to apply another pipeline for the machine learning processes, and that's what we work on."

"The solution itself could be easier to use."

"While it is reliable, there are some issues with Apache Spark Streaming as it is not 100% reliable."

"We would like to have the ability to do arbitrary stateful functions in Python."

"The service structure of Apache Spark Streaming can improve. There are a lot of issues with memory management and latency. There is no real-time analytics. We recommend it for the use cases where there is a five-second latency, but not for a millisecond, an IOT-based, or the detection anomaly-based. Flink as a service is much better."

"In terms of improvement, the UI could be better."

"The initial setup is quite complex."

More Apache Spark Streaming cons

"I would like to see improvements in consistency and flexibility for schema design for NoSQL data stored in wide columns."

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

"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."

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

"The deployment time could also be reduced."

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

"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."

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

More Google Cloud Dataflow cons

Pricing and Cost Advice

"Spark is an affordable solution, especially considering its open-source nature."

"I was using the open-source community version, which was self-hosted."

"On a scale from one to ten, where one is expensive, or not cost-effective, and ten is cheap, I rate the price a seven."

"People pay for Apache Spark Streaming as a service."

"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."

"The solution is not very expensive."

"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."

"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

15%

Outsourcing Company

13%

Comms Service Provider

Healthcare Company

Financial Services Firm

19%

Manufacturing Company

12%

Comms Service Provider

Construction Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	9
Midsize Enterprise	2
Large Enterprise	7

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

Questions from the Community

What needs improvement with Apache Spark Streaming?

One of the improvements we need is in Spark SQL and the machine learning library. I don't think there is too much to work on, but the issue is when we want to use machine learning, we always need t...

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What is your primary use case for Apache Spark Streaming?

We work with Apache Spark Streaming for our project because we use that as one of the landing data sources, and we work with it to ensure we can get all of the data before it goes through our data ...

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What advice do you have for others considering Apache Spark Streaming?

One thing I would share with other organizations considering Apache Spark Streaming is the necessity of having effective data storage. We want to ensure we acquire and manage our data storage effec...

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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

Comparisons

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Compared 10% of the time

Apache Pulsar vs Apache Spark Streaming

Compared 7% of the time

Apache Flink vs Apache Spark Streaming

Compared 7% of the time

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Compared 14% of the time

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Compared 11% of the time

Apache Flink vs Google Cloud Dataflow

Compared 10% of the time

Qlik Talend Cloud vs Google Cloud Dataflow

Compared 9% of the time

Apache NiFi vs Google Cloud Dataflow

Compared 6% of the time

More Google Cloud Dataflow Competitors

Product Reports

Buyer's Guide

Apache Spark Streaming

September 2026

Download Apache Spark Streaming product report

Buyer's Guide

Google Cloud Dataflow

September 2026

Download Google Cloud Dataflow product report

Also Known As

Spark Streaming

Google Dataflow

Overview

Apache Spark Streaming efficiently processes real-time data with features like micro-batching and native Python support. It's scalable and integrates with many services, ideal for reducing data latency and enabling real-time analytics across industries.

Apache Spark Streaming is a powerful tool for real-time data processing and analytics, offering support for multiple languages and robust integration capabilities. Its open-source nature, combined with features like checkpointing and watermarking, makes it a reliable choice for managing data streams with low latency. However, it faces challenges with Kubernetes deployments and requires improvements in memory management and latency. The installation process and handling of structured and unstructured data also present complexities. Despite these challenges, it's heavily utilized in building data pipelines and leveraging machine learning algorithms.

What are Apache Spark Streaming's key features?

Native Python Support: Efficient processing with Python language integration.
Micro-Batching: Handles streams in small batches for real-time processing.
Real-Time Analytics: Enables instant data insights.
Scalability: Adapts to varying data loads.
Low Latency: Processes data with minimal delays.

What benefits or ROI should users expect?

Efficiency: Streamlined real-time data processing.
Reliability: Consistent performance across tasks.
Integration: Seamless connection with other services.
Cost Optimization: Reduces processing expenses over time.

In industries like healthcare, telecommunications, and logistics, Apache Spark Streaming is implemented for real-time data processing and machine learning. It aids in predictive maintenance, anomaly detection, and fraud detection by reducing data latency with comprehensive analytics. Organizations frequently use it alongside Kafka and cloud storage solutions to enhance GIS, predictive analytics, and Customer 360 profiling.

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

UC Berkeley AMPLab, Amazon, Alibaba Taobao, Kenshoo, eBay Inc.

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

Buyer's Guide

Apache Spark Streaming vs. Google Cloud Dataflow

September 2026

Free Report: Apache Spark Streaming vs. Google Cloud Dataflow

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

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