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 #10 with an average rating of 7.8. Databricks holds a 9.0% mindshare in SA, compared to Broadcom’s 3.5% mindshare. Additionally, 96% of Databricks users are willing to recommend the solution, compared to 88% of Broadcom users who would recommend it.

Databricks

Read 93 Databricks reviews

19,069 Views
3,242 Comparison Views

96% willing to recommend

Spring Cloud Data Flow

Read 9 Spring Cloud Data Flow reviews

3,140 Views
1,225 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: February 2026).

Buyer's Guide

Databricks vs. Spring Cloud Data Flow

February 2026

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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 (6th), Data Science Platforms (1st), Data Management Platforms (DMP) (5th)

Spring Cloud Data Flow

Ranking in Streaming Analytics

10th

Average Rating

7.8

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

Data Integration (22nd)

Mindshare comparison

As of March 2026, in the Streaming Analytics category, the mindshare of Databricks is 9.0%, down from 14.2% compared to the previous year. The mindshare of Spring Cloud Data Flow is 3.5%, down from 4.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Mindshare Distribution
Product	Mindshare (%)
Databricks	9.0%
Spring Cloud Data Flow	3.5%
Other	87.5%

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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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

"Databricks is based on a Spark cluster and it is fast. Performance-wise, it is great."

"I like the ability to use workspaces with other colleagues because you can work together even without seeing the other team's job."

"Databricks is a robust solution for big data processing, offering flexibility and powerful features."

"The tool helps with data processing and analytics with large-scale data or big data since it is associated with managing data at a large scale."

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

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

"Can cut across the entire ecosystem of open source technology to give an extra level of getting the transformatory process of the data."

"The most significant benefit Databricks has brought to my company is the Unity Catalog."

More Databricks pros

"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 feature is real-time streaming."

"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 dashboards in Spring Cloud Dataflow are quite valuable."

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

"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."

Cons

"I have had some issues with some of the Spark clusters running on Databricks, where the Spark runtime and clusters go up and down, which is an area for improvement."

"I'm not the guy that I'm working with Databricks on a daily basis. I'm on the management team. However, my team tells me there are limitations with streaming events. The connectors work with a small set of platforms. For example, we can work with Kafka, but if we want to move to an event-driven solution from AWS, we cannot do it. We cannot connect to all the streaming analytics platforms, so we are limited in choosing the best one."

"The product should incorporate more learning aspects. It needs to have a free trial version that the team can practice."

"Databricks would benefit from enhanced metrics and tighter integration with Azure's diagnostics."

"The initial setup of Databricks could be complex."

"Databricks would have more collaborative features than it has. It should have some more customization for the jobs."

"I think setting up the whole account for one person and giving access are areas that can be difficult to manage and should be made a little easier."

"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."

More Databricks cons

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

"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."

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

"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."

"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."

Pricing and Cost Advice

"We only pay for the Azure compute behind the solution."

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

"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 pricing depends on the usage itself."

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

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

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

"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."

More Databricks pricing and cost advice

"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."

"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

19%

Computer Software Company

12%

Retailer

Manufacturing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	27
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...

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 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

Comparisons

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Compared 6% of the time

Dremio vs Databricks

Compared 4% of the time

Microsoft Power BI vs Databricks

Compared 4% of the time

Alteryx vs Databricks

Compared 3% of the time

Informatica PowerCenter vs Databricks

Compared 3% of the time

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Compared 19% of the time

Apache Kafka vs Spring Cloud Data Flow

Compared 6% of the time

TIBCO BusinessWorks vs Spring Cloud Data Flow

Compared 5% of the time

Apache Spark Streaming vs Spring Cloud Data Flow

Compared 4% of the time

IBM InfoSphere DataStage vs Spring Cloud Data Flow

Compared 4% of the time

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Product Reports

Buyer's Guide

Databricks

February 2026

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Buyer's Guide

Spring Cloud Data Flow

February 2026

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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

February 2026

Free Report: Databricks vs. Spring Cloud Data Flow

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