Apache Spark Streaming vs Google Cloud Dataflow comparison

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 #9 with an average rating of 8.1. Apache holds a 3.6% mindshare in SA, compared to Google’s 5.1% 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

1,291 Views
1,291 Comparison Views

94% willing to recommend

Google Cloud Dataflow

Read 14 Google Cloud Dataflow reviews

2,451 Views
2,034 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 2025).

Buyer's Guide

Apache Spark Streaming vs. Google Cloud Dataflow

September 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 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

9th

Average Rating

8.0

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of October 2025, in the Streaming Analytics category, the mindshare of Apache Spark Streaming is 3.6%, up from 3.4% compared to the previous year. The mindshare of Google Cloud Dataflow is 5.1%, down from 7.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Market Share Distribution
Product	Market Share (%)
Apache Spark Streaming	3.6%
Google Cloud Dataflow	5.1%
Other	91.3%

Streaming Analytics

Featured Reviews

Himansu Jena

Sr Project Manager at Raj Subhatech

Efficient real-time data management and analysis with advanced features

There are various ways we can improve Apache Spark Streaming through best practices. The initial part requires attention to batch interval tuning, which helps small intervals in micro batches based on latency requirements and helps prevent back pressure. We can use data formats such as Parquet or ORC for storage that needs faster reads and leveraging feature predicate push-down optimizations. We can implement serialization which helps with any Kyro in terms of .NET or Java. We have boxing and unboxing serialization for XML and JSON for converting key-pair values stored in browser. We can also implement caching mechanisms for storing and recomputing multiple operations. We can use specified joins which help with smaller databases, and distributed joins can minimize users. We can implement project optimization memory for CPU efficiency, known as Tungsten. Additionally, load balancing, checkpointing, and schema evaluation are areas to consider based on performance and bottlenecks. We can use Bugzilla tools for tracking and Splunk to monitor the performance of process systems, utilization, and performance based on data frames or data sets.

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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.

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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."

"Spark Streaming is critical, quite stable, full-featured, and scalable."

"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."

"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."

"Apache Spark Streaming has features like checkpointing and Streaming API that are useful."

"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."

"I appreciate Apache Spark Streaming's micro-batching capabilities; the watermarking functionality and related features are quite good."

More Apache Spark Streaming pros

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

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

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

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

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

"It is a scalable solution."

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

More Google Cloud Dataflow pros

Cons

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

"When dealing with various data types including COBOL, Excel, JSON, video, audio, and MPG files, challenges can arise with incomplete or missing values."

"It was resource-intensive, even for small-scale applications."

"The downside is when you have this the other way around in the columns, it becomes really hard to use."

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

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

"The debugging aspect could use some improvement."

"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."

More Apache Spark Streaming cons

"The deployment time could also be reduced."

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

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

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

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

"The technical support has slight room for improvement."

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

More Google Cloud Dataflow cons

Pricing and Cost Advice

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

"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."

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

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

"The tool is cheap."

"The solution is not very expensive."

"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."

"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."

"Google Cloud is slightly cheaper than AWS."

"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

Computer Software Company

23%

Financial Services Firm

20%

Healthcare Company

University

Financial Services Firm

17%

Manufacturing Company

12%

Retailer

10%

Computer Software 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	10

Questions from the Community

What do you like most about Apache Spark Streaming?

Apache Spark Streaming is versatile. You can use it for competitive intelligence, gathering data from competitors, or for internal tasks like monitoring workflows.

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What needs improvement with Apache Spark Streaming?

I believe the downsides of Apache Spark Streaming are that it primarily supports structured data. Currently, in my organization, we require thousands of transcripts that need to be handled during l...

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What is your primary use case for Apache Spark Streaming?

My use cases for Apache Spark Streaming were during my academics. During that time, I used Apache Spark Streaming to transmit data live from one source to another.

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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.

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?

It can be improved in several ways. The system could function in an automated fashion and provide suggestions based on past transactions to achieve better scalability. Implementing AI-based suggest...

See all answers

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Compared 7% of the time

Amazon MSK vs Google Cloud Dataflow

Compared 5% of the time

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Product Reports

Buyer's Guide

Apache Spark Streaming

October 2025

Download Apache Spark Streaming product report

Buyer's Guide

Google Cloud Dataflow

September 2025

Download Google Cloud Dataflow product report

Also Known As

Spark Streaming

Google Dataflow

Overview

Spark Streaming makes it easy to build scalable fault-tolerant streaming applications.

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

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 2025

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 2025.

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