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 8.2, while VMware is ranked #10 with an average rating of 7.6. Databricks holds a 14.0% mindshare in SA, compared to VMware’s 5.0% mindshare. Additionally, 96% of Databricks users are willing to recommend the solution, compared to 87% of VMware users who would recommend it.

Databricks

Read 82 Databricks reviews

7,736 Views
5,084 Comparison Views

96% willing to recommend

Spring Cloud Data Flow

Read 8 Spring Cloud Data Flow reviews

2,743 Views
2,051 Comparison Views

87% willing to recommend

Databricks

Spring Cloud Data Flow

Comparison Buyer's Guide

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Executive Summary

We performed a comparison between Databricks and Spring Cloud Data Flow based on real PeerSpot user reviews.

Find out in this report how the two Streaming Analytics solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed Databricks vs. Spring Cloud Data Flow Report (Updated: October 2024).

Buyer's Guide

Databricks vs. Spring Cloud Data Flow

October 2024

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Helped 814,763 peers since 2012

Categories and Ranking

Databricks

Ranking in Streaming Analytics

1st

Average Rating

8.2

Number of Reviews

Ranking in other categories

Data Science Platforms (1st)

Spring Cloud Data Flow

Ranking in Streaming Analytics

10th

Average Rating

7.8

Number of Reviews

Ranking in other categories

Data Integration (22nd)

Mindshare comparison

As of November 2024, in the Streaming Analytics category, the mindshare of Databricks is 14.0%, up from 9.6% compared to the previous year. The mindshare of Spring Cloud Data Flow is 5.0%, up from 4.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics

Featured Reviews

Dunstan Matekenya

Data Scientist at a financial services firm with 10,001+ employees

Jul 10, 2024

Process large-scale data sets and integrates with Apache Spark with notebook environment

I primarily use Databricks to process large-scale data sets with Apache Spark. My main use case is processing large data sets, such as 600 GB or 800 GB Databricks integrates natively with Apache Spark, which I use as a processing engine for large-scale datasets. This native integration is one of…

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NitinGoyal

Engineering Lead at Naukri.com

Aug 15, 2024

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 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."

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

"The integration with Python and the notebooks really helps."

"Databricks' Lakehouse architecture has been most useful for us. The data governance has been absolutely efficient in between other kinds of solutions."

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

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

"I like how easy it is to share your notebook with others. You can give people permission to read or edit. I think that's a great feature. You can also pull in code from GitHub pretty easily. I didn't use it that often, but I think that's a cool feature."

"It's easy to increase performance as required."

More Databricks pros

"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 most valuable feature is real-time streaming."

"The product is very user-friendly."

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

"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 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."

Cons

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

"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."

"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."

"The biggest problem associated with the product is that it is quite pricey."

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

"Databricks can improve by making the documentation better."

"Doesn't provide a lot of credits or trial options."

"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."

"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."

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

"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."

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

Pricing and Cost Advice

"There are different versions."

"The solution requires a subscription."

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

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

"The solution is based on a licensing model."

"We have only incurred the cost of our AWS cloud services. This is because during this period, Databricks provided us with an extended evaluation period, and we have not spent much money yet. We are just starting to incur costs this month, I will know more later on the full cost perspective."

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

"The cost for Databricks depends on the use case. I work on it as a consultant, so I'm using the client's Databricks, so it depends on how big the client is."

More Databricks pricing and cost advice

"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."

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

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Top Industries

By visitors reading reviews

Financial Services Firm

16%

Computer Software Company

12%

Manufacturing Company

Healthcare Company

Financial Services Firm

29%

Computer Software Company

16%

Manufacturing Company

Retailer

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?

I would improve the dashboard features as they are not very user-friendly. Another area for improvement is the documentation, as it is not very precise. There are limited resources available on Spr...

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

I am a developer using Spring Cloud Dataflow. We primarily use it to convert our applications from monolithic to microservices. The solution is used for scheduling tasks in a specific order and ens...

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

My advice would be to thoroughly review the documentation and understand if Spring Cloud Dataflow is the right solution for your application. For applications with only one or two microservices, it...

See all answers

Comparisons

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Compared 11% of the time

Informatica PowerCenter vs Databricks

Compared 8% of the time

Amazon SageMaker vs Databricks

Compared 8% of the time

Dremio vs Databricks

Compared 8% of the time

Microsoft Power BI vs Databricks

Compared 6% of the time

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Apache Flink vs Spring Cloud Data Flow

Compared 33% of the time

TIBCO BusinessWorks vs Spring Cloud Data Flow

Compared 10% of the time

Google Cloud Dataflow vs Spring Cloud Data Flow

Compared 9% of the time

Apache Spark Streaming vs Spring Cloud Data Flow

Compared 7% of the time

Informatica Intelligent Data Management Cloud (IDMC) vs Spring Cloud Data Flow

Compared 3% of the time

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Product Reports

Buyer's Guide

Databricks

November 2024

Download Databricks product report

Buyer's Guide

Spring Cloud Data Flow

October 2024

Download Spring Cloud Data Flow product report

Also Known As

Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash

No data available

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Databricks

VMware

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.

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.

Sample Customers

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

Information Not Available

Buyer's Guide

Databricks vs. Spring Cloud Data Flow

October 2024

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: October 2024.

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