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 #8 with an average rating of 7.9. Databricks holds a 14.6% mindshare in SA, compared to Google’s 8.4% mindshare. Additionally, 96% of Databricks users are willing to recommend the solution, compared to 90% of Google users who would recommend it.

Databricks

Read 85 Databricks reviews

6,962 Views
4,678 Comparison Views

96% willing to recommend

Google Cloud Dataflow

Read 10 Google Cloud Dataflow reviews

3,614 Views
3,001 Comparison Views

90% willing to recommend

Databricks

Google Cloud Dataflow

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 26, 2024

Databricks and Google Cloud Dataflow are powerful data processing platforms in the big data and analytics space. Databricks appears to have an edge in machine learning and collaborative features, while Google Cloud Dataflow excels in cost-effectiveness and flexibility for various programming languages.

Features: Databricks offers a Spark-based architecture that excels in machine learning with features like Delta Lake and MLflow integration. It supports multiple programming languages, enhancing its suitability for big data analytics and interactive querying. Its collaborative workspace and scalability enhance performance in complex data tasks. Google Cloud Dataflow, based on Apache Beam, integrates strongly with other Google Cloud services, providing cost-effectiveness, scalability, and robust streaming and data pipeline capabilities. It also supports various languages, offering flexibility for programmers.

Room for Improvement: Databricks users have noted the need to expand its visualization capabilities, improve integration with BI tools, and enhance predictive analytics and machine learning libraries. Google Cloud Dataflow could improve its error logging and debugging features, expand its Python SDK functionalities, and optimize scalability and authentication processes. Both platforms face pricing concerns, with Databricks needing more cost-effective options and Google Cloud Dataflow urged to further improve cost optimizations.

Ease of Deployment and Customer Service: Databricks provides flexibility with deployment options across public, private, and hybrid cloud environments, although this can add complexity. Customer service feedback is mixed, with some accolades for responsiveness but also calls for quicker response times. Google Cloud Dataflow focuses on optimizing public cloud deployments, potentially simplifying setup. It generally enjoys a reputation for reliability, supported by strong documentation and support services.

Pricing and ROI: Databricks is often seen as costly with its flexible pay-as-you-go pricing model, challenging for cost estimation despite providing value for batch processing and high workloads. Users appreciate the time and effort savings contributing to ROI. Google Cloud Dataflow is considered more cost-effective with competitive pricing appealing to organizations seeking economical options. Both platforms deliver significant value, though perceptions of cost and ROI vary depending on specific use cases.

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

Buyer's Guide

Databricks vs. Google Cloud Dataflow

January 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:

Categories and Ranking

Databricks

Ranking in Streaming Analytics

1st

Average Rating

8.2

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

Data Science Platforms (1st)

Google Cloud Dataflow

Ranking in Streaming Analytics

8th

Average Rating

7.8

Reviews Sentiment

7.3

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of January 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 8.4%, up from 7.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics

Featured Reviews

Parag Bhosale

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

Integrating engineering and learning, but cost challenges arise with cluster management

We often use a single cluster to ingest Databricks, which Databricks doesn't recommend. They suggest using a no-cluster solution like job clusters. This can be overwhelming for us because we started smaller. 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. We need to stay in sync with the DVR versions, and migrations can pose challenges. For example, issues arose when we moved a cluster from a previous version to the latest one. We could use their job clusters, however, that increases costs, which is challenging for us as a startup. Maintaining this infrastructure can be a headache.

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Tamer Talal

Satellite System Engineer at NARSS

A tool useful for data transmission and data storage that needs to improve its authentication area

The authentication part of the product is an area of concern where improvements are required. For some common users, the solution's authentication part is difficult to use. The scalability of the product is an area of concern where improvements are required. In the future, the product should be made available at a cheaper rate.

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Quotes from Members

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

Pros

"The technical support is good."

"The most valuable feature is the Spark cluster which is very fast for heavy loads, big data processing and Pi Spark."

"This solution offers a lake house data concept that we have found exciting. We are able to have a large amount of data in a data lake and can manage all relational activities."

"The most valuable feature of Databricks is the integration of the data warehouse and data lake, and the development of the lake house. Additionally, it integrates well with Spark for processing data in production."

"Ability to work collaboratively without having to worry about the infrastructure."

"The processing capacity is tremendous in the database."

"It's very simple to use Databricks Apache Spark."

"It's great technology."

More Databricks pros

"I don't need a server running all the time while using the tool. It is also easy to setup. The product offers a pay-as-you-go service."

"The product's installation process is easy...The tool's maintenance part is somewhat easy."

"The service is relatively cheap compared to other batch-processing engines."

"The best feature of Google Cloud Dataflow is its practical connectedness."

"Google Cloud Dataflow is useful for streaming and data pipelines."

"The most valuable features of Google Cloud Dataflow are scalability and connectivity."

"The most valuable features of Google Cloud Dataflow are the integration, it's very simple if you have the complete stack, which we are using. It is overall very easy to use, user-friendly friendly, and cost-effective if you know how to use it. The solution is very flexible for programmers, if you know how to do scripts or program in Python or any other language, it's extremely easy to use."

"The solution allows us to program in any language we desire."

More Google Cloud Dataflow pros

Cons

"Pricing is one of the things that could be improved."

"While Databricks is generally a robust solution, I have noticed a limitation with debugging in the Delta Live Table, which could be improved."

"Databricks requires writing code in Python or SQL, so if you're a good programmer then you can use Databricks."

"I would like more integration with SQL for using data in different workspaces."

"Performance could be improved."

"There is room for improvement in visualization."

"Scalability is an area with certain shortcomings. The solution's scalability needs improvement."

"Databricks doesn't offer the use of Python scripts by itself and is not connected to GitHub repositories or anything similar. This is something that is missing. if they could integrate with Git tools it would be an advantage."

More Databricks cons

"They should do a market survey and then make improvements."

"Google Cloud Dataflow should include a little cost optimization."

"When I deploy the product in local errors, a lot of errors pop up which are not always caught. The solution's error logging is bad. It can take a lot of time to debug the errors. It needs to have better logs."

"There are certain challenges regarding the Google Cloud Composer which can be improved."

"The solution's setup process could be more accessible."

"I would like Google Cloud Dataflow to be integrated with IT data flow and other related services to make it easier to use as it is a complex tool."

"The authentication part of the product is an area of concern where improvements are required."

"The deployment time could also be reduced."

More Google Cloud Dataflow cons

Pricing and Cost Advice

"Licensing on site I would counsel against, as on-site hardware issues tend to really delay and slow down delivery."

"I'm not involved in the financing, but I can say that the solution seemed reasonably priced compared to the competitors. Similar products are usually in the same price range. With five being affordable and one being expensive, I would rate Databricks a four out of five."

"Databricks uses a price-per-use model, where you can use as much compute as you need."

"Databricks' cost could be improved."

"We pay as we go, so there isn't a fixed price. It's charged by the unit. I don't have any details detail about how they measure this, but it should be a mix between processing and quantity of data handled. We run a simulation based on our use cases, which gives us an estimate. We've been monitoring this, and the costs have met our expectations."

"The cost is around $600,000 for 50 users."

"The solution is a good value for batch processing and huge workloads."

"I do not exactly know the costs, but one of our clients pays between $100 USD and $200 USD monthly."

More Databricks pricing and cost advice

"On a scale from one to ten, where one is cheap, and ten is expensive, I rate Google Cloud Dataflow's pricing a four out of ten."

"Google Cloud Dataflow is a cheap solution."

"The price of the solution depends on many factors, such as how they pay for tools in the company and its size."

"On a scale from one to ten, where one is cheap, and ten is expensive, I rate the solution's pricing a seven to eight out of ten."

"The tool is cheap."

"The solution is not very expensive."

"Google Cloud is slightly cheaper than AWS."

"The solution is cost-effective."

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Top Industries

By visitors reading reviews

Financial Services Firm

17%

Computer Software Company

11%

Manufacturing Company

Healthcare Company

Financial Services Firm

17%

Retailer

13%

Manufacturing Company

12%

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 ...

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?

The solution is not very expensive.

See all answers

What needs improvement with Google Cloud Dataflow?

See all answers

Comparisons

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Compared 11% of the time

Informatica PowerCenter vs Databricks

Compared 8% of the time

Dremio vs Databricks

Compared 8% of the time

Amazon SageMaker vs Databricks

Compared 8% of the time

Azure Stream Analytics vs Databricks

Compared 4% of the time

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Apache NiFi vs Google Cloud Dataflow

Compared 12% of the time

Amazon MSK vs Google Cloud Dataflow

Compared 11% of the time

Apache Flink vs Google Cloud Dataflow

Compared 9% of the time

Spring Cloud Data Flow vs Google Cloud Dataflow

Compared 9% of the time

Amazon Kinesis vs Google Cloud Dataflow

Compared 5% of the time

More Google Cloud Dataflow Competitors

Product Reports

Buyer's Guide

Databricks

January 2025

Download Databricks product report

Buyer's Guide

Streaming Analytics

January 2025

Download Google Cloud Dataflow product report

Also Known As

Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash

Google Dataflow

Learn More

Databricks

Google

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.

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.

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

January 2025

Free Report: Databricks vs. Google Cloud Dataflow

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