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 8.3, while Broadcom is ranked #17 with an average rating of 7.5. Databricks holds a 7.8% mindshare in SA, compared to Broadcom’s 2.6% mindshare. Additionally, 96% of Databricks users are willing to recommend the solution, compared to 88% of Broadcom users who would recommend it.

Databricks

Read 94 Databricks reviews

23,376 Views
4,675 Comparison Views

96% willing to recommend

Spring Cloud Data Flow

Read 9 Spring Cloud Data Flow reviews

4,093 Views
1,392 Comparison Views

88% willing to recommend

Databricks

Spring Cloud Data Flow

Comparison Buyer's Guide

Download the report

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: June 2026).

Buyer's Guide

Databricks vs. Spring Cloud Data Flow

June 2026

Download the complete report

Helped 906,829 peers since 2012

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 (4th), Data Science Platforms (1st), Data Management Platforms (DMP) (5th)

Spring Cloud Data Flow

Ranking in Streaming Analytics

17th

Average Rating

7.8

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

Data Integration (31st)

Mindshare comparison

As of July 2026, in the Streaming Analytics category, the mindshare of Databricks is 7.8%, down from 14.1% compared to the previous year. The mindshare of Spring Cloud Data Flow is 2.6%, 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	7.8%
Spring Cloud Data Flow	2.6%
Other	89.6%

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.

Read full review

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.

Read full review

Quotes from Members

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

Pros

"This solution offers a lake house data concept that we have found exciting, as we are able to have a large amount of data in a data lake and can manage all relational activities, with all asset complaints properties available to ensure the quality of all data."

"One of the newest features brought into this solution provides you with a way to solve, deploy, and train models using the platform itself, or it can connect to your Azure Machine Learning in order to train, deploy, and productionalize some of the machine learning models."

"Databricks' most valuable feature is the data transformation through PySpark."

"I think the features I like the most are the scalability of the solution as well as its ability to share."

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

"The Delta Lake data type has been the most useful part of this solution. Delta Lake is an opensource data type and it was implemented and invented by Databricks."

"By going through this solution, we were able to complete the processing of the data in half an hour."

"The solution is very easy to use."

More Databricks pros

"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 dashboards in Spring Cloud Dataflow are quite valuable."

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

"Overall, Spring Cloud Data Flow is a really good solution and a lot cheaper than a lot of infrastructure provided by big companies like Google or Amazon."

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

"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."

"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."

More Spring Cloud Data Flow pros

Cons

"The initial setup of Databricks could be complex."

"I believe that this product could be improved by becoming more user-friendly."

"Databricks could improve in some of its functionality."

"I present a lot of projects in various forums and seminars and there aren't a lot of credits and trial options with Databricks. Even if we want to explore, we're not able to and that's a challenge."

"If I want to create a Databricks account, I need to have a prior cloud account such as an AWS account or an Azure account. Only then can I create a Databricks account on the cloud. However, if they can make it so that I can still try Databricks even if I don't have a cloud account on AWS and Azure, it would be great. That is, it would be nice if it were possible to create a pseudo account and be provided with a free trial. It is very essential to creating a workforce on Databricks. For example, students or corporate staff can then explore and learn Databricks."

"The solution could improve by providing better automation capabilities. For example, working together with more of a DevOps approach, such as continuous integration."

"Some of the error messages that we receive are too vague, saying things like "unknown exception", and these should be improved to make it easier for developers to debug problems."

"Costs can quickly add up if you don't plan for it."

More Databricks cons

"The documentation on offer is not that good."

"The visual user interface could use some help; it needs improvement."

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

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

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

"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."

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

More Spring Cloud Data Flow cons

Pricing and Cost Advice

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

"The solution requires a subscription."

"I would rate the tool’s pricing an eight out of ten."

"Price-wise, I would rate Databricks a three out of five."

"The pricing depends on the usage itself."

"I would rate Databricks' pricing seven out of ten."

"There are different versions."

"The licensing costs of Databricks is a tiered licensing regime, so it is flexible."

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."

See which vendors are best for you

Use our free recommendation engine to learn which Streaming Analytics solutions are best for your needs.

See recommendations

906,829 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

17%

Manufacturing Company

10%

Computer Software Company

Healthcare Company

Financial Services Firm

17%

Computer Software Company

11%

Retailer

Outsourcing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	27
Midsize Enterprise	12
Large Enterprise	57

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 ...

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...

See all answers

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

Dataiku vs Databricks

Compared 4% of the time

Alteryx vs Databricks

Compared 4% of the time

Dremio vs Databricks

Compared 3% of the time

H2O.ai vs Databricks

Compared 3% of the time

Palantir Foundry vs Databricks

Compared 3% of the time

More Databricks Competitors

Apache Flink vs Spring Cloud Data Flow

Compared 10% of the time

Cloudera DataFlow vs Spring Cloud Data Flow

Compared 5% of the time

TIBCO Spotfire vs Spring Cloud Data Flow

Compared 5% of the time

TIBCO BusinessWorks vs Spring Cloud Data Flow

Compared 4% of the time

WSO2 Enterprise Integrator vs Spring Cloud Data Flow

Compared 4% of the time

More Spring Cloud Data Flow Competitors

Product Reports

Buyer's Guide

Databricks

July 2026

Download Databricks product report

Buyer's Guide

Spring Cloud Data Flow

July 2026

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

June 2026

Free Report: Databricks vs. Spring Cloud Data Flow

Find out what your peers are saying about Databricks vs. Spring Cloud Data Flow and other solutions. Updated: June 2026.

DOWNLOAD NOW

906,829 professionals have used our research since 2012.

See our Databricks vs. Spring Cloud Data Flow report.

See our list of best Streaming Analytics vendors.

We monitor all Streaming Analytics reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.