Apache Spark Streaming vs Google Cloud Dataflow comparison

Apache and Google are both solutions in the Streaming Analytics category. Apache is ranked #8 with an average rating of 7.9, while Google is ranked #10 with an average rating of 8.2. Apache holds a 3.9% mindshare in SA, compared to Google’s 4.5% 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

1,268 Views
1,268 Comparison Views

94% willing to recommend

Google Cloud Dataflow

Read 14 Google Cloud Dataflow reviews

2,228 Views
1,872 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: December 2025).

Buyer's Guide

Apache Spark Streaming vs. Google Cloud Dataflow

December 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

8th

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

10th

Average Rating

8.0

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of January 2026, in the Streaming Analytics category, the mindshare of Apache Spark Streaming is 3.9%, up from 3.2% compared to the previous year. The mindshare of Google Cloud Dataflow is 4.5%, 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	3.9%
Google Cloud Dataflow	4.5%
Other	91.6%

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.

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 platform’s most valuable feature for processing real-time data is its ability to handle continuous data streams."

"The solution is very stable and reliable."

"With Apache Spark Streaming, you can have multiple kinds of windows; depending on your use case, you can select either a tumbling window, a sliding window, or a static window to determine how much data you want to process at a single point of time."

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

"As an open-source solution, using it is basically free."

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

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

"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."

More Apache Spark Streaming 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."

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

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

"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."

"It is a scalable solution."

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

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

More Google Cloud Dataflow pros

Cons

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

"The debugging aspect could use some improvement."

"There could be an improvement in the area of the user configuration section, it should be less developer-focused and more business user-focused."

"The initial setup is quite complex."

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

"The solution itself could be easier to use."

"One improvement I would expect is real-time processing instead of micro-batch or near real-time."

"The cost and load-related optimizations are areas where the tool lacks and needs improvement."

More Apache Spark Streaming cons

"The deployment time could also be reduced."

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

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

"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."

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

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

"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."

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

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."

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

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

"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."

"Google Cloud Dataflow is a cheap solution."

"Google Cloud is slightly cheaper than AWS."

"The tool is cheap."

"The solution is not very expensive."

"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."

"The solution is cost-effective."

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Top Industries

By visitors reading reviews

Computer Software Company

22%

Financial Services Firm

21%

University

Healthcare Company

Financial Services Firm

18%

Manufacturing Company

12%

Retailer

11%

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?

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 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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Product Reports

Buyer's Guide

Apache Spark Streaming

January 2026

Download Apache Spark Streaming product report

Buyer's Guide

Google Cloud Dataflow

January 2026

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

December 2025

Free Report: Apache Spark Streaming vs. Google Cloud Dataflow

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