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 8.4, while Google is ranked #8 with an average rating of 8.1. Databricks holds a 13.1% mindshare in SA, compared to Google’s 5.5% mindshare. Additionally, 96% of Databricks users are willing to recommend the solution, compared to 93% of Google users who would recommend it.

Databricks

Read 91 Databricks reviews

22,527 Views
4,055 Comparison Views

96% willing to recommend

Google Cloud Dataflow

Read 14 Google Cloud Dataflow reviews

2,682 Views
2,226 Comparison Views

93% willing to recommend

Databricks

Google Cloud Dataflow

Comparison Buyer's Guide

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Executive SummaryUpdated on Jul 6, 2025

Databricks and Google Cloud Dataflow are competitive platforms in data analytics. Databricks holds an edge due to its advanced analytics and machine learning capabilities, although its higher cost is a factor, whereas Google Cloud Dataflow presents a cost-effective option with significant connectivity and scalability.

Features: Databricks offers a unified workspace with extensive support for various programming languages, large-scale data processing, and machine learning. It provides built-in optimization recommendations and seamless cloud service integration, enhancing collaboration. Google Cloud Dataflow stands out with its real-time streaming abilities, strong programming flexibility, and cost-effectiveness. It leverages Apache Beam's open-source framework for enhanced connectivity.

Room for Improvement: Databricks could improve by expanding library support for machine learning, refining error messaging, and increasing cost transparency. Deeper visualization tool integration and broader cloud platform integration would enhance its offering. Google Cloud Dataflow needs improved error logging, faster job startup times, and more seamless integration with additional services to boost user experience.

Ease of Deployment and Customer Service: Databricks is praised for its easy deployment on public and private clouds, featuring a user-friendly interface and strong documentation. While Databricks support is favorable, response times vary. Google Cloud Dataflow benefits from Google’s extensive documentation, though it can be complex for non-technical users to set up and navigate.

Pricing and ROI: Databricks, though priced higher, delivers considerable ROI, especially for enterprises transitioning from older systems due to its flexibility and performance. Google Cloud Dataflow offers a more affordable solution, particularly for smaller workloads, yet improvements in pricing flexibility and predictability could enhance its appeal.

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

Buyer's Guide

Databricks vs. Google Cloud Dataflow

July 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

Databricks efficiently lowers costs with cloud services, though ROI varies by sector and integration, particularly with Azure.

Sentiment score

5.6

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

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

7.2

Databricks support is praised for prompt, professional service, comprehensive resources, and effective communication, enhancing overall user satisfaction.

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, enabling easy adaptation to large data and user loads with efficient resource management.

Sentiment score

7.3

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

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

I would rate the scalability of this solution as very high, about nine out of ten.

Lax Kas

Data Engineer at CRAFT Tech

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

7.7

Databricks is stable and robust, with minor issues, handling large data volumes and earning high stability ratings.

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 Cellebrite

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 requires visualization improvements, pricing clarity, user-friendliness, expanded integrations, and simplification for non-technical users to enhance 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

Enterprise buyers view Databricks as moderately pricey, with high setup costs, though discounts and licensing flexibility are available.

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

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 scalability, integration, and user-friendly features, making it ideal for data processing and AI across industries.

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)

Google Cloud Dataflow

Ranking in Streaming Analytics

8th

Average Rating

8.0

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of September 2025, in the Streaming Analytics category, the mindshare of Databricks is 13.1%, up from 12.5% compared to the previous year. The mindshare of Google Cloud Dataflow is 5.5%, down from 7.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Market Share Distribution
Product	Market Share (%)
Databricks	13.1%
Google Cloud Dataflow	5.5%
Other	81.4%

Streaming Analytics

Featured Reviews

ShubhamSharma7

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

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.

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

17%

Computer Software Company

10%

Manufacturing Company

Healthcare Company

Financial Services Firm

17%

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	25
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 ...

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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

Comparisons

Dataiku vs Databricks

Compared 9% of the time

Microsoft Power BI vs Databricks

Compared 9% of the time

Informatica PowerCenter vs Databricks

Compared 7% of the time

Dremio vs Databricks

Compared 6% of the time

Amazon SageMaker vs Databricks

Compared 3% of the time

More Databricks Competitors

Apache Flink vs Google Cloud Dataflow

Compared 21% of the time

Apache NiFi vs Google Cloud Dataflow

Compared 11% of the time

Spring Cloud Data Flow vs Google Cloud Dataflow

Compared 7% of the time

Amazon MSK vs Google Cloud Dataflow

Compared 7% of the time

AWS Lambda vs Google Cloud Dataflow

Compared 4% of the time

More Google Cloud Dataflow Competitors

Product Reports

Buyer's Guide

Databricks

September 2025

Download Databricks product report

Buyer's Guide

Google Cloud Dataflow

September 2025

Download Google Cloud Dataflow product report

Also Known As

Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash

Google Dataflow

Overview

Databricks is utilized for advanced analytics, big data processing, machine learning models, ETL operations, data engineering, streaming analytics, and integrating multiple data sources.

Organizations leverage Databricks for predictive analysis, data pipelines, data science, and unifying data architectures. It is also used for consulting projects, financial reporting, and creating APIs. Industries like insurance, retail, manufacturing, and pharmaceuticals use Databricks for data management and analytics due to its user-friendly interface, built-in machine learning libraries, support for multiple programming languages, scalability, and fast processing.

What are the key features of Databricks?

User-friendly interface: Simplifies operations and usability.
Built-in machine learning libraries: Facilitates machine learning tasks.
Support for multiple programming languages: Enhances flexibility.
Scalability: Efficiently handles growing data needs.
Fast processing: Improves performance and speed.
Automated optimization: Reduces manual efforts.
Data visualization: Provides insightful visuals.
Collaborative features: Enhances teamwork.
Delta Lake performance: Boosts data management.
Seamless cluster management: Simplifies system operations.

What are the benefits or ROI to look for in Databricks reviews?

Efficiency in handling large datasets: Ensures smooth processing.
Interactive workspace environment: Improves user collaboration.
Integration with platforms: Provides connectivity benefits.
Performance optimization: Enhances overall system performance.
Support for data governance and security: Ensures data integrity and protection.

Databricks is implemented in insurance for risk analysis and claims processing; in retail for customer analytics and inventory management; in manufacturing for predictive maintenance and supply chain optimization; and in pharmaceuticals for drug discovery and patient data analysis. Users value its scalability, machine learning support, collaboration tools, and Delta Lake performance but seek improvements in visualization, pricing, and integration with BI tools.

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

July 2025

Free Report: Databricks vs. Google Cloud Dataflow

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