Try our new research platform with insights from 80,000+ expert users

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
6.1
Apache Spark enhances machine learning, cutting operational costs by up to 50%, with efficiency reliant on resources and expertise.
Sentiment score
5.6
Google Cloud Dataflow was appreciated for cost savings and time efficiency, though some considered its impact not fully assessable yet.
 

Customer Service

Sentiment score
5.9
Apache Spark support feedback varies, with mixed reviews on community forums, vendor support, and documentation adequacy.
Sentiment score
6.6
Google Cloud Dataflow support varies, with users praising technical resolution but highlighting inconsistent response times and accessibility.
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
Google's support team is good at resolving issues, especially with large data.
Senior Data Engineer at Accruent
The fact that no interaction is needed shows their great support since I don't face issues.
Data Engineer at Accenture
Whenever we have issues, we can consult with Google.
Senior Software Engineer at Dun & Bradstreet
 

Scalability Issues

Sentiment score
7.5
Apache Spark excels in scalability, efficiently handling large data workloads with ease, though it requires skilled infrastructure management.
Sentiment score
7.3
Google Cloud Dataflow excels in scalability and efficiency, making it ideal for real-time data processing and dynamic needs.
Google Cloud Dataflow has auto-scaling capabilities, allowing me to add different machine types based on pace and requirements.
Data Engineer at Accenture
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
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
 

Stability Issues

Sentiment score
7.4
Apache Spark is generally stable, trusted by companies; newer versions enhance reliability, though memory issues may arise without proper configuration.
Sentiment score
8.3
Google Cloud Dataflow is stable, reliably handles tasks, and benefits from automatic scaling, with minor issues on complex tasks.
Apache Spark resolves many problems in the MapReduce solution and Hadoop, such as the inability to run effective Python or machine learning algorithms.
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 requires improvements in scalability, usability, documentation, memory efficiency, real-time processing, and broader language support for better performance.
Google Cloud Dataflow needs better Kafka integration, improved error logs, reduced startup time, and enhanced Python SDK features.
Various tools like Informatica, TIBCO, or Talend offer specific aspects, licensing can be costly;
Data Architect at Devtech
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 would like to see improvements in consistency and flexibility for schema design for NoSQL data stored in wide columns.
Senior Data Engineer at Accruent
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 may incur expenses from hardware, cloud resources, or commercial support, impacting deployment costs.
Google Cloud Dataflow is praised for cost-effectiveness and scalability, offering competitive pricing influenced by pipeline complexity and company size.
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 offers fast in-memory processing, scalable analytics, MLlib for machine learning, SQL support, and seamless integration with languages.
Google Cloud Dataflow offers seamless integration, multi-language support, scalability, and serverless data handling for efficient batch and streaming processes.
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
Google Cloud Dataflow's features for event stream processing allow us to gain various insights like detecting real-time alerts.
Senior Data Engineer at Accruent
 

Categories and Ranking

Apache Spark
Average Rating
8.4
Reviews Sentiment
6.9
Number of Reviews
68
Ranking in other categories
Hadoop (1st), Compute Service (5th), Java Frameworks (2nd)
Google Cloud Dataflow
Average Rating
8.0
Reviews Sentiment
7.1
Number of Reviews
14
Ranking in other categories
Streaming Analytics (10th)
 

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 13.9%, down 18.2% compared to last year.
Google Cloud Dataflow, on the other hand, focuses on Streaming Analytics, holds 4.5% mindshare, down 7.8% since last year.
Hadoop Market Share Distribution
ProductMarket Share (%)
Apache Spark13.9%
Cloudera Distribution for Hadoop15.1%
HPE Data Fabric14.9%
Other56.1%
Hadoop
Streaming Analytics Market Share Distribution
ProductMarket Share (%)
Google Cloud Dataflow4.5%
Apache Flink12.3%
Databricks10.0%
Other73.2%
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.
Jana Polianskaja - PeerSpot reviewer
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.
report
Use our free recommendation engine to learn which Hadoop solutions are best for your needs.
879,927 professionals have used our research since 2012.
 

Top Industries

By visitors reading reviews
Financial Services Firm
25%
Computer Software Company
11%
Manufacturing Company
7%
Comms Service Provider
6%
Financial Services Firm
19%
Manufacturing Company
13%
Retailer
11%
Computer Software Company
8%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business28
Midsize Enterprise15
Large Enterprise32
By reviewers
Company SizeCount
Small Business3
Midsize Enterprise2
Large Enterprise10
 

Questions from the Community

What do you like most about Apache Spark?
We use Spark to process data from different data sources.
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?
Areas for improvement are obviously ease of use considerations, though there are limitations in doing that, so while various tools like Informatica, TIBCO, or Talend offer specific aspects, licensi...
What do you like most about Google Cloud Dataflow?
The product's installation process is easy...The tool's maintenance part is somewhat easy.
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?
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...
 

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: January 2026.
879,927 professionals have used our research since 2012.