No more typing reviews! Try our Samantha, our new voice AI agent.

Apache Spark vs Google Cloud Dataflow comparison

 

Comparison Buyer's Guide

Executive Summary

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

ROI

Sentiment score
5.6
Apache Spark provides up to 50% cost savings, boosting efficiency and reducing expenses significantly in machine learning analytics.
Sentiment score
4.7
Google Cloud Dataflow offers significant cost and time savings, proving to be an efficient investment for data architecture.
 

Customer Service

Sentiment score
6.0
Apache Spark offers vibrant community support and resources, with commercial support available through vendors like Cloudera and Hadoop.
Sentiment score
6.1
Google Cloud Dataflow's support is effective for large issues but experiences mixed feedback on response times and service consistency.
I would rate the technical support of Apache Spark an eight because when we had questions, we found solutions, and it was straightforward.
Consultant, Chief Engineer, Teamleiter at infoteam Software AG
I have received support via newsgroups or guidance on specific discussions, which is what I would expect in an open-source situation.
Data Architect at Devtech
The fact that no interaction is needed shows their great support since I don't face issues.
Data Engineer at Accenture
Google's support team is good at resolving issues, especially with large data.
Senior Data Engineer at Accruent
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.
Senior Customer Data Platform Specialist at a marketing services firm with 1,001-5,000 employees
 

Scalability Issues

Sentiment score
7.4
Apache Spark's scalability and versatility enable efficient large-scale data processing, making it a reliable choice for diverse teams.
Sentiment score
6.9
Google Cloud Dataflow excels in scalability, resource optimization, and autoscaling, effectively supporting varying data volumes across departments.
As a team lead, I'm responsible for handling five to six applications, but Google Cloud Dataflow seems to handle our use case effectively.
Senior Software Engineer at Dun & Bradstreet
Google Cloud Dataflow can handle large data processing for real-time streaming workloads as they grow, making it a good fit for our business.
Senior Data Engineer at Accruent
Google Cloud Dataflow has auto-scaling capabilities, allowing me to add different machine types based on pace and requirements.
Data Engineer at Accenture
 

Stability Issues

Sentiment score
7.4
Apache Spark is praised for its robust stability and reliability, with high user ratings despite minor configuration challenges.
Sentiment score
8.3
Google Cloud Dataflow is stable and reliable, praised for automatic scaling, despite occasional errors with complex tasks.
MapReduce needs to perform numerous disk input and output operations, while Apache Spark can use memory to store and process data.
Data Engineer at a tech company with 10,001+ employees
Without a doubt, we have had some crashes because each situation is different, and while the prototype in my environment is stable, we do not know everything at other customer sites.
Data Architect at Devtech
I have not encountered any issues with the performance of Dataflow, as it is stable and backed by Google services.
Data Engineer at Accenture
The job we built has not failed once over six to seven months.
Senior Software Engineer at Dun & Bradstreet
The automatic scaling feature helps maintain stability.
Senior Data Engineer at Accruent
 

Room For Improvement

Apache Spark needs improvements in real-time querying, user-friendliness, logging, large dataset handling, and expanded programming language support.
Improvements in error logging, support, cost, integration, scalability, and automation are needed for Google Cloud Dataflow's efficiency.
Various tools like Informatica, TIBCO, or Talend offer specific aspects, licensing can be costly;
Data Architect at Devtech
I find that there really lacks the technical depth to do any recommendations for future updates of Apache Spark.
Consultant, Chief Engineer, Teamleiter at infoteam Software AG
Outside of Google Cloud Platform, it is problematic for others to use it and may require promotion as an actual technology.
Data Engineer at Accenture
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 manually.
Senior Customer Data Platform Specialist at a marketing services firm with 1,001-5,000 employees
Dealing with a huge volume of data causes failure due to array size.
Senior Software Engineer at Dun & Bradstreet
 

Setup Cost

Apache Spark is cost-effective but can incur high infrastructure costs, especially in cloud setups like Databricks, with setup time variability.
Google Cloud Dataflow is seen as a cost-effective streaming solution, with affordability ratings varying widely among users.
It is part of a package received from Google, and they are not charging us too high.
Senior Software Engineer at Dun & Bradstreet
 

Valuable Features

Apache Spark provides scalable, in-memory data processing with flexible support for distributed computing, streaming, and machine learning integration.
Google Cloud Dataflow offers scalable, cost-effective data processing, integrating seamlessly with Google Cloud, using Apache Beam and various tools.
The most important part is that everything can be connected, and the data exchange across overseas connections is fast and reliable.
Consultant, Chief Engineer, Teamleiter at infoteam Software AG
Apache Spark is the solution, and within it, you have PySpark, which is the API for Apache Spark to write and run Python code.
Data Engineer at a tech company with 10,001+ employees
The solution is beneficial in that it provides a base-level long-held understanding of the framework that is not variant day by day, which is very helpful in my prototyping activity as an architect trying to assess Apache Spark, Great Expectations, and Vault-based solutions versus those proposed by clients like TIBCO or Informatica.
Data Architect at Devtech
It supports multiple programming languages such as Java and Python, enabling flexibility without the need to learn something new.
Data Engineer at Accenture
The integration within Google Cloud Platform is very good.
Senior Software Engineer at Dun & Bradstreet
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.
Senior Customer Data Platform Specialist at a marketing services firm with 1,001-5,000 employees
 

Categories and Ranking

Apache Spark
Average Rating
8.4
Reviews Sentiment
6.9
Number of Reviews
69
Ranking in other categories
Hadoop (1st), Compute Service (6th), Java Frameworks (2nd)
Google Cloud Dataflow
Average Rating
8.0
Reviews Sentiment
6.8
Number of Reviews
15
Ranking in other categories
Streaming Analytics (11th)
 

Mindshare comparison

Apache Spark and Google Cloud Dataflow aren’t in the same category and serve different purposes. Apache Spark is designed for Hadoop and holds a mindshare of 13.6%, down 17.6% compared to last year.
Google Cloud Dataflow, on the other hand, focuses on Streaming Analytics, holds 3.7% mindshare, down 7.1% since last year.
Hadoop Mindshare Distribution
ProductMindshare (%)
Apache Spark13.6%
Cloudera Distribution for Hadoop14.8%
HPE Data Fabric10.5%
Other61.1%
Hadoop
Streaming Analytics Mindshare Distribution
ProductMindshare (%)
Google Cloud Dataflow3.7%
Apache Flink8.9%
Databricks8.1%
Other79.3%
Streaming Analytics
 

Featured Reviews

Devindra Weerasooriya - PeerSpot reviewer
Data Architect at Devtech
Provides a consistent framework for building data integration and access solutions with reliable performance
The in-memory computation feature is certainly helpful for my processing tasks. It is helpful because while using structures that could be held in memory rather than stored during the period of computation, I go for the in-memory option, though there are limitations related to holding it in memory that need to be addressed, but I have a preference for in-memory computation. The solution is beneficial in that it provides a base-level long-held understanding of the framework that is not variant day by day, which is very helpful in my prototyping activity as an architect trying to assess Apache Spark, Great Expectations, and Vault-based solutions versus those proposed by clients like TIBCO or Informatica.
reviewer2812851 - PeerSpot reviewer
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.
report
Use our free recommendation engine to learn which Hadoop solutions are best for your needs.
893,221 professionals have used our research since 2012.
 

Top Industries

By visitors reading reviews
Financial Services Firm
23%
Comms Service Provider
7%
Manufacturing Company
7%
Computer Software Company
6%
Financial Services Firm
20%
Manufacturing Company
13%
Retailer
10%
Insurance Company
5%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business28
Midsize Enterprise16
Large Enterprise32
By reviewers
Company SizeCount
Small Business3
Midsize Enterprise2
Large Enterprise11
 

Questions from the Community

What is your experience regarding pricing and costs for Apache Spark?
Apache Spark is open-source, so it doesn't incur any charges.
What needs improvement with Apache Spark?
I find that there really lacks the technical depth to do any recommendations for future updates of Apache Spark. I used it for two years for our prototype work and testing things, but because I had...
What is your primary use case for Apache Spark?
I attempted to use Apache Spark in one of our customer projects, but after the initial test, our customer moved to another technology and another database system. I do not have any final remarks on...
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...
 

Also Known As

No data available
Google Dataflow
 

Overview

 

Sample Customers

NASA JPL, UC Berkeley AMPLab, Amazon, eBay, Yahoo!, UC Santa Cruz, TripAdvisor, Taboola, Agile Lab, Art.com, Baidu, Alibaba Taobao, EURECOM, Hitachi Solutions
Absolutdata, Backflip Studios, Bluecore, Claritics, Crystalloids, Energyworx, GenieConnect, Leanplum, Nomanini, Redbus, Streak, TabTale
Find out what your peers are saying about Apache, Cloudera, Amazon Web Services (AWS) and others in Hadoop. Updated: April 2026.
893,221 professionals have used our research since 2012.