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Databricks vs Spring Cloud Data Flow comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 17, 2024

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
91
Ranking in other categories
Cloud Data Warehouse (8th), Data Science Platforms (1st)
Spring Cloud Data Flow
Ranking in Streaming Analytics
9th
Average Rating
7.8
Reviews Sentiment
6.8
Number of Reviews
9
Ranking in other categories
Data Integration (21st)
 

Mindshare comparison

As of July 2025, in the Streaming Analytics category, the mindshare of Databricks is 14.2%, up from 11.3% compared to the previous year. The mindshare of Spring Cloud Data Flow is 4.8%, up from 4.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Streaming Analytics
 

Featured Reviews

ShubhamSharma7 - PeerSpot reviewer
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.
NitinGoyal - PeerSpot reviewer
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.

Quotes from Members

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

Pros

"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."
"The initial setup phase of Databricks was good."
"The simplicity of development is the most valuable feature."
"Specifically for data science and data analytics purposes, it can handle large amounts of data in less time. I can compare it with Teradata. If a job takes five hours with Teradata databases, Databricks can complete it in around three to three and a half hours."
"Its lightweight and fast processing are valuable."
"I like that Databricks is a unified platform that lets you do streaming and batch processing in the same place. You can do analytics, too. They have added something called Databricks SQL Analytics, allowing users to connect to the data lake to perform analytics. Databricks also will enable you to share your data securely. It integrates with your reporting system as well."
"Having one solution for everything, from data engineering to machine learning, is beneficial since everything comes under one hood."
"A very valuable feature is the data processing, and the solution is specifically good at using the Spark ecosystem."
"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 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 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."
"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 product is very user-friendly."
"The best thing I like about Spring Cloud Data Flow is its plug-and-play model."
 

Cons

"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 integration of data could be a bit better."
"The pricing of Databricks could be cheaper."
"Implementation of Databricks is still very code heavy."
"The ability to customize our own pipelines would enhance the product, similar to what's possible using ML files in Microsoft Azure DevOps."
"Instead of relying on a massive instance, the solution should offer micro partition levels. They're working on it, however, they need to implement it to help the solution run more effectively."
"Doesn't provide a lot of credits or trial options."
"Pricing is one of the things that could be improved."
"On the tool's online discussion forums, you may get stuck with an issue, making it an area where 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."
"Spring Cloud Data Flow is not an easy-to-use tool, so 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."
"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."
"The solution's community support could be improved."
"Some of the features, like the monitoring tools, are not very mature and are still evolving."
"I would improve the dashboard features as they are not very user-friendly."
 

Pricing and Cost Advice

"The product pricing is moderate."
"The solution is affordable."
"I rate the price of Databricks as eight out of ten."
"Price-wise, I would rate Databricks a three out of five."
"The price of Databricks is reasonable compared to other solutions."
"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."
"The solution is based on a licensing model."
"Licensing on site I would counsel against, as on-site hardware issues tend to really delay and slow down delivery."
"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
17%
Computer Software Company
10%
Manufacturing Company
9%
Healthcare Company
6%
Financial Services Firm
26%
Computer Software Company
17%
Retailer
7%
Manufacturing Company
6%
 

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 ...
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...
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...
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...
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...
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...
 

Also Known As

Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash
No data available
 

Overview

 

Sample Customers

Elsevier, MyFitnessPal, Sharethrough, Automatic Labs, Celtra, Radius Intelligence, Yesware
Information Not Available
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