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 12.5% mindshare in SA, compared to Broadcom’s 4.6% mindshare. Additionally, 96% of Databricks users are willing to recommend the solution, compared to 88% of Broadcom users who would recommend it.

Databricks

Read 91 Databricks reviews

20,775 Views
3,740 Comparison Views

96% willing to recommend

Spring Cloud Data Flow

Read 9 Spring Cloud Data Flow reviews

3,001 Views
1,410 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: September 2025).

Buyer's Guide

Databricks vs. Spring Cloud Data Flow

September 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)

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 (21st)

Mindshare comparison

As of October 2025, in the Streaming Analytics category, the mindshare of Databricks is 12.5%, down from 12.8% compared to the previous year. The mindshare of Spring Cloud Data Flow is 4.6%, up from 4.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Market Share Distribution
Product	Market Share (%)
Databricks	12.5%
Spring Cloud Data Flow	4.6%
Other	82.9%

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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Kaleeswaran Karuppusamy

Cloud Application Architect at Lumen Technologies

Helps retrieve data and with data processing but is not so easy to use

There are no stability issues in the tool. Compared to Apache Sling, Spring Cloud Data Flow is not easy to use. People prefer Apache Sling when dealing with their use cases. I don't know whether Spring Cloud Data Flow will be demanded a lot in the market because there are a lot of other options available, especially open-source tools like Apache Sling, which is getting a lot of attention from people. A lot of people have started using Apache Sling, so I don't know how much more visible Spring Cloud Data Flow will be in the future.

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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

"When we have a huge volume of data that we want to process with speed, velocity, and volume, we go through Databricks."

"Having one solution for everything, from data engineering to machine learning, is beneficial since everything comes under one hood."

"The initial setup is pretty easy."

"Databricks offers various courses that I can use, whether it's PySpark, Scala, or R."

"The notebooks and the ability to share them with collaborators are valuable, as multiple developers can use a single cluster."

"We like that this solution can handle a wide variety and velocity of data engineering, either in batch mode or real-time."

"Databricks integrates well with other solutions."

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

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 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 product is very user-friendly."

"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 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 dashboards in Spring Cloud Dataflow are quite valuable."

Cons

"The initial setup of Databricks could be complex."

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

"It should have more compatible and more advanced visualization and machine learning libraries."

"Databricks' performance when serving the data to an analytics tool isn't as good as Snowflake's."

"The pricing of Databricks could be cheaper."

"We'd like a more visual dashboard for analysis It needs better UI."

"The integration of data could be a bit better."

"The integration and query capabilities can be improved."

More Databricks cons

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

"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."

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

"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."

Pricing and Cost Advice

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

"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."

"The solution requires a subscription."

"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 billing of Databricks can be difficult and should improve."

"The solution uses a pay-per-use model with an annual subscription fee or package. Typically this solution is used on a cloud platform, such as Azure or AWS, but more people are choosing Azure because the price is more reasonable."

"The price is okay. It's competitive."

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

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%

Computer Software Company

Manufacturing Company

Healthcare Company

Financial Services Firm

23%

Computer Software Company

15%

Retailer

Manufacturing Company

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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Compared 9% of the time

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Compared 6% of the time

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Compared 5% of the time

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Product Reports

Buyer's Guide

Databricks

October 2025

Download Databricks product report

Buyer's Guide

Spring Cloud Data Flow

September 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

September 2025

Free Report: Databricks vs. Spring Cloud Data Flow

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