Databricks vs Spring Cloud Data Flow comparison

Databricks and Broadcom are both solutions in the Streaming Analytics category. Databricks is ranked #1 with an average rating of 8.4, while Broadcom is ranked #11 with an average rating of 7.8. Databricks holds a 10.0% mindshare in SA, compared to Broadcom’s 4.1% mindshare. Additionally, 96% of Databricks users are willing to recommend the solution, compared to 88% of Broadcom users who would recommend it.

Databricks

Read 92 Databricks reviews

18,651 Views
3,357 Comparison Views

96% willing to recommend

Spring Cloud Data Flow

Read 9 Spring Cloud Data Flow reviews

3,055 Views
1,283 Comparison Views

88% willing to recommend

Databricks

Spring Cloud Data Flow

Comparison Buyer's Guide

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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: December 2025).

Buyer's Guide

Databricks vs. Spring Cloud Data Flow

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:

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)

Spring Cloud Data Flow

Ranking in Streaming Analytics

11th

Average Rating

7.8

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

Data Integration (20th)

Mindshare comparison

As of January 2026, in the Streaming Analytics category, the mindshare of Databricks is 10.0%, down from 13.8% compared to the previous year. The mindshare of Spring Cloud Data Flow is 4.1%, down from 4.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Market Share Distribution
Product	Market Share (%)
Databricks	10.0%
Spring Cloud Data Flow	4.1%
Other	85.9%

Streaming Analytics

Featured Reviews

SimonRobinson

Governance And Engagement Lead

Improved data governance has enabled sensitive data tracking but cost management still needs work

I believe we could improve Databricks integration with cloud service providers. The impact of our current integration has not been particularly good, and it's becoming very expensive for us. The inefficiencies in our implementation, such as not shutting down warehouses when they're not in use or reserving the right number of credits, have led to increased costs. We made several beginner mistakes, such as not taking advantage of incremental loading and running overly complicated queries all the time. We should be using ETL tools to help us instead of doing it directly in Databricks. We need more experienced professionals to manage Databricks effectively, as it's not as forgiving as other platforms such as Snowflake. I think introducing customer repositories would facilitate easier implementation with Databricks.

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Lipika Naik

Senior Software Engineer at QBE Regional Insurance

Provides ease of integration with other cloud platforms

Spring Cloud Data Flow is a useful product if I consider how there are different providers with whom my company had to deal, and most of them offer cloud-based products. I can't explain any crucial circumstances where the product's integration capabilities were helpful, but the aforementioned details explain the scenario for which I used the solution. I was only involved with the development of the product and not with the data pipeline configuration phase. The use of Spring Cloud Data Flow greatly impacted projects' time to market since our company's intention was to actually deploy and ensure that the payment platform integrated with it, which was an easy process. The product's user interface was very intuitive. The tool was deployed in multiple environments, but I am not sure about the production. From the time I started taking up the job in my current organization, I saw that we have deployed the tool in multiple environments wherein the number of users extensively used the product in the UAT environment, which is one of the most stable environments. There were 20 different methods to test the tool. I wouldn't be able to tell you the production details of the tool as I was more part of the production deployment, but I can say that it was deployed with the intent of making it available for 10,000 users. Those who plan to use the product should enjoy the flexibility of the solution. I rate the tool a nine out of ten.

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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

"Its lightweight and fast processing are valuable."

"Databricks is a scalable solution. It is the largest advantage of the solution."

"We are completely satisfied with the ease of connecting to different sources of data or pocket files in the search"

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

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

"The processing capacity is tremendous in the database."

"Automation with Databricks is very easy when using the API."

"The capacity of use of the different types of coding is valuable. Databricks also has good performance because it is running in spark extra storage, meaning the performance and the capacity use different kinds of codes."

More Databricks pros

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

"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 most valuable feature is real-time streaming."

"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."

"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 product is very user-friendly."

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

"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."

Cons

"In the next release, I would like to see more optimization features."

"Would be helpful to have additional licensing options."

"There is room for improvement in visualization."

"The integration features could be more interesting, more involved."

"One area of improvement is the Databricks File System (DBFS), where command-line challenges arise when accessing files. Standardization of file paths on the system could help, as engineers sometimes struggle."

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

"My experience with the pricing and licensing model is that it remains relatively expensive. Though it's less expensive than AWS, we still need a more cost-effective solution."

"Databricks' technical support takes a while to respond and could be improved."

More Databricks cons

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

"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."

"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."

"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."

"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."

Pricing and Cost Advice

"My smallest project is around a hundred euros, and my most expensive is just under a thousand euros a week. That is based on terabytes of data processed each month."

"The price of Databricks is reasonable compared to other solutions."

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

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

"I am based in South Africa, where it is expensive adapting to the cloud, and then there is the price for the tool itself."

"The solution is based on a licensing model."

"There are different versions."

"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."

More Databricks pricing and cost advice

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

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

"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."

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Top Industries

By visitors reading reviews

Financial Services Firm

18%

Manufacturing Company

Computer Software Company

Healthcare Company

Financial Services Firm

21%

Computer Software Company

13%

Retailer

Government

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	1
Large Enterprise	5

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

January 2026

Download Databricks product report

Buyer's Guide

Spring Cloud Data Flow

December 2025

Download Spring Cloud Data Flow product report

Also Known As

Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash

No data available

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

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.

Broadcom

Sample Customers

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

Information Not Available

Buyer's Guide

Databricks vs. Spring Cloud Data Flow

December 2025

Free Report: Databricks vs. Spring Cloud Data Flow

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