Databricks vs Google Cloud Dataflow comparison

Databricks and Google are both solutions in the Streaming Analytics category. Databricks is ranked #1 with an average rating of 8.3, while Google is ranked #9 with an average rating of 8.2. Databricks holds a 9.5% mindshare in SA, compared to Google’s 4.2% mindshare. Additionally, 96% of Databricks users are willing to recommend the solution, compared to 93% of Google users who would recommend it.

Databricks

Read 93 Databricks reviews

18,865 Views
3,396 Comparison Views

96% willing to recommend

Google Cloud Dataflow

Read 14 Google Cloud Dataflow reviews

2,197 Views
1,867 Comparison Views

93% willing to recommend

Databricks

Google Cloud Dataflow

Comparison Buyer's Guide

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Executive SummaryUpdated on Jan 18, 2026

Databricks and Google Cloud Dataflow compete in the cloud-based data management and analytics category. Databricks appears to have the upper hand due to its robust machine learning integration capabilities and flexible deployment options, while Google Cloud Dataflow is better integrated with the Google Cloud ecosystem and offers cost-effective solutions.

Features: Databricks handles large-scale analytics efficiently with advanced machine learning integration, supports multiple languages like Python, R, and Scala, and provides collaboration through notebooks and Delta Lake format. Google Cloud Dataflow offers strong integration within the Google ecosystem, leverages Apache Beam for both batch and streaming processing, and provides flexibility in programming language support.

Room for Improvement: Databricks should enhance visualization capabilities, improve integration features, and simplify model scoring and monitoring. Additionally, improvements in predictive analysis libraries and clearer error messages are necessary. Google Cloud Dataflow could benefit from better error logging, reduced startup time for jobs, and improved technical support, as well as enhanced integration with IT data flow and a more accessible setup process.

Ease of Deployment and Customer Service: Databricks supports versatile deployment on multiple cloud platforms like Azure and AWS, offering flexibility and favorably rated response times, though technical support could be improved. Google Cloud Dataflow excels in Google Cloud Platform integration, noted for its clear documentation, though some users have faced occasional support challenges.

Pricing and ROI: Databricks is perceived as expensive, particularly for small and mid-sized clusters, but delivers good ROI due to high efficiency and scalability. Google Cloud Dataflow is considered a cost-effective alternative, offering flexible pricing based on compute resources and usage patterns, making it a notable advantage in user reviews.

To learn more, read our detailed Databricks vs. Google Cloud Dataflow Report (Updated: December 2025).

Buyer's Guide

Databricks vs. Google Cloud Dataflow

December 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:

ROI

Sentiment score

6.5

Users experience mixed returns with Databricks, noting cost efficiency and scalability but facing challenges with measuring monetary gains.

Sentiment score

5.6

Google Cloud Dataflow was appreciated for cost savings and time efficiency, though some considered its impact not fully assessable yet.

This reduction in both time and money resulted in real-time impact and significant cost savings.

Satyam Wagh

Consultant at Nice Software Solutions

For a lot of different tasks, including machine learning, it is a nice solution.

Parag Bhosale

Senior Data Engineer at a logistics company with 51-200 employees

When it comes to big data processing, I prefer Databricks over other solutions.

IshwarSukheja

Head CEO at bizmetric

For more quotes and insights, download the Databricks report

No quotes available

For more quotes and insights, download the Google Cloud Dataflow report

Customer Service

Sentiment score

6.9

Databricks customer service is praised for prompt, professional support, though some report delays; documentation helps many users.

Sentiment score

6.6

Google Cloud Dataflow support varies, with users praising technical resolution but highlighting inconsistent response times and accessibility.

Whenever we reach out, they respond promptly.

Parag Bhosale

Senior Data Engineer at a logistics company with 51-200 employees

As of now, we are raising issues and they are providing solutions without any problems.

Prabhakar Bonam

Data Platform Architect at KELLANOVA

I rate the technical support as fine because they have levels of technical support available, especially partners who get really good support from Databricks on new features.

Lax Kas

Data Engineer at CRAFT Tech

For more quotes and insights, download the Databricks report

The fact that no interaction is needed shows their great support since I don't face issues.

Jana Polianskaja

Data Engineer at Accenture

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

Preethi Reddy

Senior Data Engineer at Accruent

Whenever we have issues, we can consult with Google.

Sunil Miragi

Senior Software Engineer at Dun & Bradstreet

For more quotes and insights, download the Google Cloud Dataflow report

Scalability Issues

Sentiment score

7.4

Databricks is praised for its scalability, elasticity, and auto-scaling, providing high performance and flexibility across industries.

Sentiment score

7.3

Google Cloud Dataflow excels in scalability and efficiency, making it ideal for real-time data processing and dynamic needs.

The sky's the limit with Databricks.

SimonRobinson

Governance And Engagement Lead

The patches have sometimes caused issues leading to our jobs being paused for about six hours.

Parag Bhosale

Senior Data Engineer at a logistics company with 51-200 employees

Databricks is an easily scalable platform.

Prabhakar Bonam

Data Platform Architect at KELLANOVA

For more quotes and insights, download the Databricks report

Google Cloud Dataflow has auto-scaling capabilities, allowing me to add different machine types based on pace and requirements.

Jana Polianskaja

Data Engineer at Accenture

As a team lead, I'm responsible for handling five to six applications, but Google Cloud Dataflow seems to handle our use case effectively.

Sunil Miragi

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.

Preethi Reddy

Senior Data Engineer at Accruent

For more quotes and insights, download the Google Cloud Dataflow report

Stability Issues

Sentiment score

7.6

Databricks is highly rated for reliability and efficiency, with minor issues quickly resolved, boasting strong user stability scores.

Sentiment score

8.3

Google Cloud Dataflow is stable, reliably handles tasks, and benefits from automatic scaling, with minor issues on complex tasks.

They release patches that sometimes break our code.

Parag Bhosale

Senior Data Engineer at a logistics company with 51-200 employees

Although it is too early to definitively state the platform's stability, we have not encountered any issues so far.

Prabhakar Bonam

Data Platform Architect at KELLANOVA

Databricks is definitely a very stable product and reliable.

AvivCohen

Data Engineer at a tech vendor with 1,001-5,000 employees

For more quotes and insights, download the Databricks report

I have not encountered any issues with the performance of Dataflow, as it is stable and backed by Google services.

Jana Polianskaja

Data Engineer at Accenture

The job we built has not failed once over six to seven months.

Sunil Miragi

Senior Software Engineer at Dun & Bradstreet

The automatic scaling feature helps maintain stability.

Preethi Reddy

Senior Data Engineer at Accruent

For more quotes and insights, download the Google Cloud Dataflow report

Room For Improvement

Databricks needs better visualization, integration, clearer errors, UI enhancements, wider platform support, and improved documentation and usability.

Google Cloud Dataflow needs better Kafka integration, improved error logs, reduced startup time, and enhanced Python SDK features.

Adjusting features like worker nodes and node utilization during cluster creation could mitigate these failures.

ShubhamSharma7

Data Engineer at a engineering company with 1,001-5,000 employees

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.

Parag Bhosale

Senior Data Engineer at a logistics company with 51-200 employees

We use MLflow for managing MLOps, however, further improvement would be beneficial, especially for large language models and related tools.

Rama Subba Reddy Thavva

Solution Architect at Mercedes-Benz AG

For more quotes and insights, download the Databricks report

Outside of Google Cloud Platform, it is problematic for others to use it and may require promotion as an actual technology.

Jana Polianskaja

Data Engineer at Accenture

Dealing with a huge volume of data causes failure due to array size.

Sunil Miragi

Senior Software Engineer at Dun & Bradstreet

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

Preethi Reddy

Senior Data Engineer at Accruent

For more quotes and insights, download the Google Cloud Dataflow report

Setup Cost

Databricks' pricing varies widely based on usage and data volume, making it cost-effective yet potentially expensive for large-scale use.

Google Cloud Dataflow is praised for cost-effectiveness and scalability, offering competitive pricing influenced by pipeline complexity and company size.

It is not a cheap solution.

Prabhakar Bonam

Data Platform Architect at KELLANOVA

I believe that in terms of credits for Databricks, we're spending between £15,000 and £20,000 a month.

SimonRobinson

Governance And Engagement Lead

For more quotes and insights, download the Databricks report

It is part of a package received from Google, and they are not charging us too high.

Sunil Miragi

Senior Software Engineer at Dun & Bradstreet

For more quotes and insights, download the Google Cloud Dataflow report

Valuable Features

Databricks excels in ease of use, scalability, integration, and data governance, enhancing productivity and collaboration for data engineering.

Google Cloud Dataflow offers seamless integration, multi-language support, scalability, and serverless data handling for efficient batch and streaming processes.

Databricks' capability to process data in parallel enhances data processing speed.

ShubhamSharma7

Data Engineer at a engineering company with 1,001-5,000 employees

The platform allows us to leverage cloud advantages effectively, enhancing our AI and ML projects.

Prabhakar Bonam

Data Platform Architect at KELLANOVA

The Unity Catalog is for data governance, and the Delta Lake is to build the lakehouse.

Lax Kas

Data Engineer at CRAFT Tech

For more quotes and insights, download the Databricks report

It supports multiple programming languages such as Java and Python, enabling flexibility without the need to learn something new.

Jana Polianskaja

Data Engineer at Accenture

The integration within Google Cloud Platform is very good.

Sunil Miragi

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.

Preethi Reddy

Senior Data Engineer at Accruent

For more quotes and insights, download the Google Cloud Dataflow report

Categories and Ranking

Databricks

Ranking in Streaming Analytics

1st

Average Rating

8.2

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

Cloud Data Warehouse (9th), Data Science Platforms (1st), Data Management Platforms (DMP) (5th)

Google Cloud Dataflow

Ranking in Streaming Analytics

9th

Average Rating

8.0

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of February 2026, in the Streaming Analytics category, the mindshare of Databricks is 9.5%, down from 14.1% compared to the previous year. The mindshare of Google Cloud Dataflow is 4.2%, down from 7.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Market Share Distribution
Product	Market Share (%)
Databricks	9.5%
Google Cloud Dataflow	4.2%
Other	86.3%

Streaming Analytics

Featured Reviews

Satyam Wagh

Consultant at Nice Software Solutions

Unified data workflows have cut ticket processing times and are driving faster business insights

Databricks already provides monthly updates and continuously works on delivering new features while enhancing existing ones. However, the platform could become easier to use. While instruction-led workshops are available, offering more free instructional workshops would allow a wider audience to access and learn about Databricks. Additionally, providing use cases would help beginners gain more knowledge and hands-on experience. Regarding my experience, I was initially unfamiliar with the platform and had to conduct research and learn through various videos. I did find some instruction-led classes, but several of those required payment. The platform should provide more free resources to enable a broader audience to access and learn about Databricks. The platform itself is user-friendly and easy to use without complex issues, so I believe it does not need improvement in its core functionality. Rather, supporting aspects can be enhanced.

Read full review

Jana Polianskaja

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.

Read full review

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Top Industries

By visitors reading reviews

Financial Services Firm

18%

Manufacturing Company

Computer Software Company

Healthcare Company

Financial Services Firm

18%

Manufacturing Company

12%

Retailer

11%

Computer Software Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	26
Midsize Enterprise	12
Large Enterprise	56

By reviewers
Company Size	Count
Small Business	3
Midsize Enterprise	2
Large Enterprise	10

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 ...

See all answers

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...

See all answers

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...

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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?

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...

See all answers

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Compared 16% of the time

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Compared 8% of the time

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Compared 7% of the time

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Compared 6% of the time

Amazon MSK vs Google Cloud Dataflow

Compared 6% of the time

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Product Reports

Buyer's Guide

Databricks

January 2026

Download Databricks product report

Buyer's Guide

Google Cloud Dataflow

January 2026

Download Google Cloud Dataflow product report

Also Known As

Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash

Google Dataflow

Overview

Databricks offers a scalable, versatile platform that integrates seamlessly with Spark and multiple languages, supporting data engineering, machine learning, and analytics in a unified environment.

Databricks stands out for its scalability, ease of use, and powerful integration with Spark, multiple languages, and leading cloud services like Azure and AWS. It provides tools such as the Notebook for collaboration, Delta Lake for efficient data management, and Unity Catalog for data governance. While enhancing data engineering and machine learning workflows, it faces challenges in visualization and third-party integration, with pricing and user interface navigation being common concerns. Despite needing improvements in connectivity and documentation, it remains popular for tasks like real-time processing and data pipeline management.

What features make Databricks unique?

Notebook: Enables collaborative work among team members.
Delta Lake: Optimizes data management operations.
Unity Catalog: Provides governance over data assets.
Cloud Integration: Seamlessly connects with major cloud platforms.

What benefits can users expect from Databricks?

Versatility: Supports diverse applications in data science and engineering.
Performance: Delivers efficient handling of large-scale analytics tasks.
Collaboration: Enhances teamwork in data projects.
Unified Environment: Centralizes machine learning and analytics activities.

In the tech industry, Databricks empowers teams to perform comprehensive data analytics, enabling them to conduct extensive ETL operations, run predictive modeling, and prepare data for SparkML. In retail, it supports real-time data processing and batch streaming, aiding in better decision-making. Enterprises across sectors leverage its capabilities for creating secure APIs and managing data lakes effectively.

Databricks

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

Elsevier, MyFitnessPal, Sharethrough, Automatic Labs, Celtra, Radius Intelligence, Yesware

Absolutdata, Backflip Studios, Bluecore, Claritics, Crystalloids, Energyworx, GenieConnect, Leanplum, Nomanini, Redbus, Streak, TabTale

Buyer's Guide

Databricks vs. Google Cloud Dataflow

December 2025

Free Report: Databricks vs. Google Cloud Dataflow

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