Apache Spark Streaming vs Google Cloud Dataflow comparison

Apache Spark Streaming vs. Google Cloud Dataflow

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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

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 July 2026, in the Streaming Analytics category, the mindshare of Apache Spark Streaming is 4.7%, up from 2.6% compared to the previous year. The mindshare of Google Cloud Dataflow is 3.5%, down from 6.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Mindshare Distribution
Product	Mindshare (%)
Apache Spark Streaming	4.7%
Google Cloud Dataflow	3.5%
Other	91.8%

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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reviewer2812851

Senior Customer Data Platform Specialist at a marketing services firm with 1,001-5,000 employees

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

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

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

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

"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."

"The solution is very stable and reliable."

"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."

"It is the most scalable tool that I have seen before."

"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."

More Apache Spark Streaming pros

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

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

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

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

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

"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."

"Migrating our batch processing jobs to Google Cloud Dataflow led to a reduction in cost by 70%."

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

More Google Cloud Dataflow pros

Cons

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

"We don't have enough experience to be judgmental about its flaws."

"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."

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

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

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

"Monitoring is an area where they could definitely improve Apache Spark Streaming. When you have a streaming application, it generates numerous logs. After some time, the logs become meaningless because they're quite large and impossible to open."

"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

"Promoting the technology more broadly would help increase its adoption."

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

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

"The deployment time could also be reduced."

"The technical support has slight room for improvement."

"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."

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

"The technical support is very hard to reach."

More Google Cloud Dataflow cons

Pricing and Cost Advice

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

"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."

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

"The tool is cheap."

"Google Cloud is slightly cheaper than AWS."

"Google Cloud Dataflow is a cheap solution."

"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."

"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."

"The solution is not very expensive."

"The solution is cost-effective."

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Top Industries

By visitors reading reviews

Financial Services Firm

18%

Computer Software Company

Comms Service Provider

Outsourcing Company

Financial Services Firm

19%

Manufacturing Company

12%

Retailer

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	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...

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 ...

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...

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.

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...

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...

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Product Reports

Apache Spark Streaming

Download Apache Spark Streaming product report

Google Cloud Dataflow

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

Apache Spark Streaming vs. Google Cloud Dataflow