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Apache Spark Streaming vs Google Cloud Dataflow comparison

Apache and Google are both solutions in the Streaming Analytics category. Apache is ranked #10 with an average rating of 8.2, while Google is ranked #7 with an average rating of 8.1. Apache holds a 2.7% mindshare in SA, compared to Google’s 6.8% mindshare. Additionally, 91% of Apache users are willing to recommend the solution, compared to 92% of Google users who would recommend it.
Apache Spark Streaming
Read 11 Apache Spark Streaming reviews
  • 1,466 Views
  • 737 Comparison Views
91% willing to recommend
Google Cloud Dataflow
Read 13 Google Cloud Dataflow reviews
  • 2,758 Views
  • 1,111 Comparison Views
92% willing to recommend
 

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.

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To learn more, read our detailed Apache Spark Streaming vs. Google Cloud Dataflow Report (Updated: June 2025).
Buyer's Guide
Apache Spark Streaming vs. Google Cloud Dataflow
June 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
10th
Average Rating
8.0
Reviews Sentiment
7.4
Number of Reviews
11
Ranking in other categories
No ranking in other categories
Google Cloud Dataflow
Ranking in Streaming Analytics
7th
Average Rating
8.0
Reviews Sentiment
7.3
Number of Reviews
13
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of June 2025, in the Streaming Analytics category, the mindshare of Apache Spark Streaming is 2.7%, down from 3.8% compared to the previous year. The mindshare of Google Cloud Dataflow is 6.8%, down from 7.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Streaming Analytics
 

Featured Reviews

Oscar Estorach - PeerSpot reviewer
Versatile and flexible when dealing with large-scale data streams
What I like about Spark is its versatility in supporting multiple languages and that makes it my preferred choice for building scalable and efficient systems, whether it is hooking databases with web applications or handling large-scale data transformations. Apache Spark Streaming is versatile. You can use it for competitive intelligence, gathering data from competitors, or for internal tasks like monitoring workflows. It works well in the cloud, and you can structure data using Databricks or Spark, providing flexibility for different projects. Spark Streaming's flexibility shines when dealing with large-scale data streams. It caters to different needs, offering real-time insights for tasks like online sales analytics. The ability to prioritize data streams is valuable, especially for monitoring competitor prices online.
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Jana Polianskaja - PeerSpot reviewer
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

"Spark Streaming is critical, quite stable, full-featured, and scalable."
"It's the fastest solution on the market with low latency data on data transformations."
"Apache Spark Streaming has features like checkpointing and Streaming API that are useful."
"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."
"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 platform’s most valuable feature for processing real-time data is its ability to handle continuous data streams."
"Apache Spark Streaming is versatile. You can use it for competitive intelligence, gathering data from competitors, or for internal tasks like monitoring workflows."
More Apache Spark Streaming pros
"The product's installation process is easy...The tool's maintenance part is somewhat easy."
"It is a scalable solution."
"The support team is good and it's easy to use."
"Google Cloud Dataflow is useful for streaming and data pipelines."
"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 service is relatively cheap compared to other batch-processing engines."
"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."
More Google Cloud Dataflow pros
 

Cons

"The debugging aspect could use some improvement."
"We don't have enough experience to be judgmental about its flaws."
"We would like to have the ability to do arbitrary stateful functions in Python."
"It was resource-intensive, even for small-scale applications."
"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 solution itself could be easier to use."
"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."
More Apache Spark Streaming cons
"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."
"They should do a market survey and then make improvements."
"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."
"The technical support has slight room for improvement."
"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."
"Promoting the technology more broadly would help increase its adoption."
More Google Cloud Dataflow cons
 

Pricing and Cost Advice

"People pay for Apache Spark Streaming as a service."
"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."
"The tool is cheap."
"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."
"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 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 solution is not very expensive."
"The solution is cost-effective."
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Top Industries

By visitors reading reviews
Financial Services Firm
26%
Computer Software Company
21%
Manufacturing Company
5%
University
5%
Financial Services Firm
18%
Manufacturing Company
12%
Retailer
11%
Computer Software Company
10%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

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.
See all answers
What needs improvement with Apache Spark Streaming?
We don't have enough experience to be judgmental about its flaws, as we've only used stable features like batch micro-batch. Integration poses no problem; however, I don't use some features and can...
See all answers
What is your primary use case for Apache Spark Streaming?
We use Spark Streaming in a micro-batch region. It's not a full real-time system, but it offers high performance and low latency.
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?
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 am not sure, as we built only one job, and it is running on a daily basis. Everything else is managed using BigQuery schedulers and Talend. However, occasionally, dealing with a huge volume of da...
See all answers
 

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Product Reports

Buyer's Guide
Streaming Analytics
June 2025
Apache Spark Streaming reportDownload Apache Spark Streaming product report
Buyer's Guide
Google Cloud Dataflow
June 2025
Google Cloud Dataflow reportDownload 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
June 2025
Free Report: Apache Spark Streaming vs. Google Cloud Dataflow
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