Databricks vs Spring Cloud Data Flow comparison

Databricks and VMware are both solutions in the Streaming Analytics category. Databricks is ranked #1 with an average rating of 8.3, while VMware is ranked #9 with an average rating of 7.8. Databricks holds a 13.5% mindshare in SA, compared to VMware’s 4.7% mindshare. Additionally, 96% of Databricks users are willing to recommend the solution, compared to 88% of VMware users who would recommend it.

Databricks

Read 91 Databricks reviews

23,965 Views
4,314 Comparison Views

96% willing to recommend

Spring Cloud Data Flow

Read 9 Spring Cloud Data Flow reviews

3,216 Views
1,544 Comparison Views

88% willing to recommend

Databricks

Spring Cloud Data Flow

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Executive SummaryUpdated on Dec 17, 2024

Databricks and Spring Cloud Data Flow operate in the data processing and analytics domain. Databricks has a competitive edge due to its robust analytics capabilities and fast data processing integration with Python and Spark.

Features: Databricks supports large-scale analytics with built-in optimization recommendations for enhanced query performance and speed. The integration with Python and Spark provides fast data processing and machine learning capabilities. Its flexible use of programming languages and collaborative notebooks offer significant versatility. Spring Cloud Data Flow excels in microservices orchestration, dependency injection, and composability, ideal for flexible lightweight processing tasks.

Room for Improvement: Databricks could improve its visualization capabilities, enhance integration with tools like Power BI, and expand machine learning libraries. Users have noted its high cost and desire for more accessible technical documentation. Spring Cloud Data Flow could benefit from a better user interface, additional language support, and greater community engagement, along with improved documentation and dashboard features.

Ease of Deployment and Customer Service: Databricks supports deployments in public and hybrid clouds, praised for its responsive technical support, though communication may occasionally falter due to intermediary providers. Spring Cloud Data Flow is generally deployed on-premises or in private clouds, with clear documentation but less robust community support due to its open-source nature.

Pricing and ROI: Databricks operates on a pay-per-use model, often deemed expensive yet justified by its comprehensive features, with ROI experiences varying by cloud usage and scale. Spring Cloud Data Flow, as an open-source option, offers cost benefits, though official support incurs fees, aligning with its community-driven framework and offering good value in community editions.

To learn more, read our detailed Databricks vs. Spring Cloud Data Flow Report (Updated: July 2025).

Buyer's Guide

Databricks vs. Spring Cloud Data Flow

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:

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 (8th), Data Science Platforms (1st)

Spring Cloud Data Flow

Ranking in Streaming Analytics

9th

Average Rating

7.8

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

Data Integration (20th)

Mindshare comparison

As of August 2025, in the Streaming Analytics category, the mindshare of Databricks is 13.5%, up from 11.8% compared to the previous year. The mindshare of Spring Cloud Data Flow is 4.7%, up from 4.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.

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.

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NitinGoyal

Engineering Lead at Naukri.com

Has a plug-and-play model and provides good robustness and scalability

The solution's community support could be improved. I don't know why the Spring Cloud Data Flow community is not very strong. Community support is very limited whenever you face any problem or are stuck somewhere. I'm not sure whether it has improved in the last six months because this pipeline was set up almost two years ago. I struggled with that a lot. For example, there was limited support whenever I got an exception and sought help from Stack Overflow or different forums. Interacting with Kubernetes needs a few certificates. You need to define all the certificates within your application. With the help of those certificates, your Java application or Spring Cloud Data Flow can interact with Kubernetes. I faced a lot of hurdles while placing those certificates. Despite following the official documentation to define all the replicas, readiness, and liveliness probes within the Spring Cloud Data Flow application, it was not working. So, I had to troubleshoot while digging in and debugging the internals of Spring Cloud Data Flow at that time. It was just a configuration mismatch, and I was doing nothing weird. There was a small spelling difference between how Spring Cloud Data Flow was expecting it and how I passed it. I was just following the official documentation.

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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 solution is very simple and stable."

"We have the ability to scale, collaborate and do machine learning."

"Databricks has a scalable Spark cluster creation process. The creators of Databricks are also the creators of Spark, and they are the industry leaders in terms of performance."

"I would rate them ten out of ten."

"Databricks has improved my organization by allowing us to transform data from sources to a different format and feed that to the analytics, business intelligence, and reporting teams. This tool makes it easy to do those kinds of things."

"The technical support is good."

"The most valuable feature of Databricks is the notebook, data factory, and ease of use."

"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."

More Databricks pros

"The dashboards in Spring Cloud Dataflow are quite valuable."

"The most valuable feature is real-time streaming."

"The solution's most valuable feature is that it allows us to use different batch data sources, retrieve the data, and then do the data processing, after which we can convert and store it in the target."

"The ease of deployment on Kubernetes, the seamless integration for orchestration of various pipelines, and the visual dashboard that simplifies operations even for non-specialists such as quality analysts."

"The most valuable features of Spring Cloud Data Flow are the simple programming model, integration, dependency Injection, and ability to do any injection. Additionally, auto-configuration is another important feature because we don't have to configure the database and or set up the boilerplate in the database in every project. The composability is good, we can create small workloads and compose them in any way we like."

"The product is very user-friendly."

"There are a lot of options in Spring Cloud. It's flexible in terms of how we can use it. It's a full infrastructure."

"The best thing I like about Spring Cloud Data Flow is its plug-and-play model."

Cons

"Generative AI is catching up in areas like data governance and enterprise flavor. Hence, these are places where Databricks has to be faster."

"The stability of the clusters or the instances of Databricks would be better if it was a much more stable environment. We've had issues with crashes."

"I would love an integration in my desktop IDE. For now, I have to code on their webpage."

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

"The product should provide more advanced features in future releases."

"The initial setup of Databricks could be complex."

"Performance could be improved."

"Would be helpful to have additional licensing options."

More Databricks cons

"There were instances of deployment pipelines getting stuck, and the dashboard not always accurately showing the application status, requiring manual intervention such as rerunning applications or refreshing the dashboard."

"The solution's community support could be improved."

"Spring Cloud Data Flow is not an easy-to-use tool, so improvements are required."

"Some of the features, like the monitoring tools, are not very mature and are still evolving."

"On the tool's online discussion forums, you may get stuck with an issue, making it an area where improvements are required."

"Spring Cloud Data Flow could improve the user interface. We can drag and drop in the application for the configuration and settings, and deploy it right from the UI, without having to run a CI/CD pipeline. However, that does not work with Kubernetes, it only works when we are working with jars as the Spring Cloud Data Flow applications."

"I would improve the dashboard features as they are not very user-friendly."

"The configurations could be better. Some configurations are a little bit time-consuming in terms of trying to understand using the Spring Cloud documentation."

Pricing and Cost Advice

"Databricks are not costly when compared with other solutions' prices."

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

"Databricks is a very expensive solution. Pricing is an area that could definitely be improved. They could provide a lower end compute and probably reduce the price."

"The basic version of this solution is now open-source, so there are no license costs involved. However, there is a charge for any advanced functionality and this can be quite expensive."

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

"The licensing costs of Databricks depend on how many licenses we need, depending on which Databricks provides a lot of discounts."

"We implement this solution on behalf of our customers who have their own Azure subscription and they pay for Databricks themselves. The pricing is more expensive if you have large volumes of data."

"It is an expensive tool. The licensing model is a pay-as-you-go one."

More Databricks pricing and cost advice

"If you want support from Spring Cloud Data Flow there is a fee. The Spring Framework is open-source and this is a free solution."

"This is an open-source product that can be used free of charge."

"The solution provides value for money, and we are currently using its community edition."

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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

27%

Computer Software Company

16%

Retailer

Manufacturing Company

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 ...

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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...

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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 needs improvement with Spring Cloud Data Flow?

There were instances of deployment pipelines getting stuck, and the dashboard not always accurately showing the application status, requiring manual intervention such as rerunning applications or r...

See all answers

What is your primary use case for Spring Cloud Data Flow?

We had a project for content management, which involved multiple applications each handling content ingestion, transformation, enrichment, and storage for different customers independently. We want...

See all answers

What advice do you have for others considering Spring Cloud Data Flow?

I would definitely recommend Spring Cloud Data Flow. It requires minimal additional effort or time to understand how it works, and even non-specialists can use it effectively with its friendly docu...

See all answers

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Product Reports

Buyer's Guide

Databricks

August 2025

Download Databricks product report

Buyer's Guide

Spring Cloud Data Flow

July 2025

Download Spring Cloud Data Flow product report

Also Known As

Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash

No data available

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

Spring Cloud Data Flow is a toolkit for building data integration and real-time data processing pipelines.
Pipelines consist of Spring Boot apps, built using the Spring Cloud Stream or Spring Cloud Task microservice frameworks. This makes Spring Cloud Data Flow suitable for a range of data processing use cases, from import/export to event streaming and predictive analytics. Use Spring Cloud Data Flow to connect your Enterprise to the Internet of Anything—mobile devices, sensors, wearables, automobiles, and more.

VMware

Sample Customers

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

Information Not Available

Buyer's Guide

Databricks vs. Spring Cloud Data Flow

July 2025

Free Report: Databricks vs. Spring Cloud Data Flow

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