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

Databricks vs Google Cloud Dataflow comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Mar 30, 2025

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
6.6
Organizations experience mixed returns from Databricks, with benefits from cost savings and efficiency, but challenges in initial migration.
Sentiment score
7.2
Many startups find Google Cloud Dataflow's ROI unclear, yet it offers significant time savings of around 70 percent.
When it comes to big data processing, I prefer Databricks over other solutions.
For a lot of different tasks, including machine learning, it is a nice solution.
 

Customer Service

Sentiment score
7.2
Databricks customer service is generally effective with prompt responses, though some report issues mainly with third-party support channels.
Sentiment score
7.9
Google Cloud Dataflow provides strong service and updates, but accessing technical support is slow and can be challenging.
Whenever we reach out, they respond promptly.
The fact that no interaction is needed shows their great support since I don't face issues.
Whenever we have issues, we can consult with Google.
 

Scalability Issues

Sentiment score
7.4
Databricks is praised for efficient scalability and cloud compatibility, allowing easy resource adjustment across diverse projects and industries.
Sentiment score
7.2
Google Cloud Dataflow excels in scalability, with flexible autoscaling and custom options, though some users suggest improvements.
The patches have sometimes caused issues leading to our jobs being paused for about six hours.
Google Cloud Dataflow has auto-scaling capabilities, allowing me to add different machine types based on pace and requirements.
As a team lead, I'm responsible for handling five to six applications, but Google Cloud Dataflow seems to handle our use case effectively.
 

Stability Issues

Sentiment score
7.7
Databricks is stable and efficient for large data, with minor issues during updates and occasional connectivity challenges.
Sentiment score
8.3
Google Cloud Dataflow is highly stable and reliable, consistently receiving high user ratings for its flawless performance.
They release patches that sometimes break our code.
Cluster failure is one of the biggest weaknesses I notice in our Databricks.
The job we built has not failed once over six to seven months.
I have not encountered any issues with the performance of Dataflow, as it is stable and backed by Google services.
 

Room For Improvement

Databricks users desire improved UI, enhanced data visualization, better integration, clearer error messages, robust support, and comprehensive documentation.
Google Cloud Dataflow needs improved Kafka integration, error logging, and community support, with easier setup and better Python SDK features.
It would be beneficial to have utilities where code snippets are readily available.
Adjusting features like worker nodes and node utilization during cluster creation could mitigate these failures.
We prefer using a small to mid-sized cluster for many jobs to keep costs low, but this sometimes doesn't support our operations properly.
Outside of Google Cloud Platform, it is problematic for others to use it and may require promotion as an actual technology.
Dealing with a huge volume of data causes failure due to array size.
 

Setup Cost

Databricks pricing depends on usage, with flexibility in licensing, and can vary in competitiveness compared to other solutions.
Google Cloud Dataflow is favored for cost-effectiveness, cheaper than AWS, with users rating pricing between two and seven.
It is part of a package received from Google, and they are not charging us too high.
 

Valuable Features

Databricks provides a unified platform for data engineering, machine learning, seamless cloud integration, and robust data management capabilities.
Google Cloud Dataflow offers seamless integration, cost-effectiveness, and scalability with robust support for batch and streaming processes.
Databricks' capability to process data in parallel enhances data processing speed.
The notebooks and the ability to share them with collaborators are valuable, as multiple developers can use a single cluster.
It supports multiple programming languages such as Java and Python, enabling flexibility without the need to learn something new.
The integration within Google Cloud Platform is very good.
 

Categories and Ranking

Databricks
Ranking in Streaming Analytics
1st
Average Rating
8.2
Reviews Sentiment
7.0
Number of Reviews
88
Ranking in other categories
Cloud Data Warehouse (7th), Data Science Platforms (1st)
Google Cloud Dataflow
Ranking in Streaming Analytics
7th
Average Rating
7.8
Reviews Sentiment
7.3
Number of Reviews
12
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of April 2025, in the Streaming Analytics category, the mindshare of Databricks is 14.6%, up from 10.1% compared to the previous year. The mindshare of Google Cloud Dataflow is 7.4%, up from 7.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Streaming Analytics
 

Featured Reviews

ShubhamSharma7 - PeerSpot reviewer
Capability to integrate diverse coding languages in a single notebook greatly enhances workflow
Databricks offers various courses that I can use, whether it's PySpark, Scala, or R. I can leverage all these courses in a single notebook, which is beneficial for clients as they can access various tools in one place whenever needed. This is quite significant. I usually work with PySpark based on client requirements. After coding, I feed the Databricks notebooks into the ADF pipeline for updates. Databricks' capability to process data in parallel enhances data processing speed. Furthermore, I can connect our Databricks notebook directly with Power BI and other visualization tools like Qlik. Once we develop code, it allows us to transform raw data into visualizations for clients using analysis diagrams, which is very helpful.
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.
report
Use our free recommendation engine to learn which Streaming Analytics solutions are best for your needs.
844,944 professionals have used our research since 2012.
 

Top Industries

By visitors reading reviews
Financial Services Firm
17%
Computer Software Company
11%
Manufacturing Company
9%
Healthcare Company
6%
Financial Services Firm
17%
Manufacturing Company
12%
Retailer
11%
Computer Software Company
11%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

Which do you prefer - Databricks or Azure Machine Learning Studio?
Databricks gives you the option of working with several different languages, such as SQL, R, Scala, Apache Spark, or Python. It offers many different cluster choices and excellent integration with ...
How would you compare Databricks vs Amazon SageMaker?
We researched AWS SageMaker, but in the end, we chose Databricks. Databricks is a Unified Analytics Platform designed to accelerate innovation projects. It is based on Spark so it is very fast. It...
Which would you choose - Databricks or Azure Stream Analytics?
Databricks is an easy-to-set-up and versatile tool for data management, analysis, and business analytics. For analytics teams that have to interpret data to further the business goals of their orga...
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?
Google Cloud Dataflow costs are primarily driven by compute resources (worker type and count) and data volume. However, other factors like pipeline complexity also contribute significantly to the t...
What needs improvement with Google Cloud Dataflow?
Apache Beam represents a powerful data processing solution that deserves wider recognition in the broader tech community. This technology offers remarkable capabilities for handling data at scale, ...
 

Also Known As

Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash
Google Dataflow
 

Overview

 

Sample Customers

Elsevier, MyFitnessPal, Sharethrough, Automatic Labs, Celtra, Radius Intelligence, Yesware
Absolutdata, Backflip Studios, Bluecore, Claritics, Crystalloids, Energyworx, GenieConnect, Leanplum, Nomanini, Redbus, Streak, TabTale
Find out what your peers are saying about Databricks vs. Google Cloud Dataflow and other solutions. Updated: March 2025.
844,944 professionals have used our research since 2012.