Azure Data Factory vs Spring Cloud Data Flow comparison

Microsoft and VMware are both solutions in the Data Integration category. Microsoft is ranked #1 with an average rating of 8.1, while VMware is ranked #23 with an average rating of 7.7. Microsoft holds a 11.0% mindshare in DI, compared to VMware’s 1.1% mindshare. Additionally, 92% of Microsoft users are willing to recommend the solution, compared to 88% of VMware users who would recommend it.

Azure Data Factory

Read 86 Azure Data Factory reviews

21,615 Views
17,232 Comparison Views

92% willing to recommend

Spring Cloud Data Flow

Read 9 Spring Cloud Data Flow reviews

1,725 Views
1,296 Comparison Views

88% willing to recommend

Azure Data Factory

Spring Cloud Data Flow

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Executive SummaryUpdated on Dec 19, 2024

Spring Cloud Data Flow and Azure Data Factory are prominent products in the data processing arena. Azure Data Factory appears to have an edge with its diverse integrations and scalability options, whereas Spring Cloud Data Flow provides flexibility and support for Java-based complex workflows.

Features: Spring Cloud Data Flow supports microservice architectures and provides comprehensive data-processing capabilities, making it ideal for Java-based environments. It offers integration with Kubernetes and Docker, adding flexibility for various deployment scenarios. Azure Data Factory has a robust range of data connectors and integrates seamlessly with other Azure services. It provides a powerful user interface for creating ETL pipelines and supports real-time data processing workflows.

Room for Improvement: Spring Cloud Data Flow could enhance its ease of use for non-developers, improve integration with non-Java environments, and optimize its graphical interface for pipeline management. Azure Data Factory would benefit from improved documentation, expanded offline capabilities, and additional real-time processing features to enhance its usability outside the Azure ecosystem.

Ease of Deployment and Customer Service: Spring Cloud Data Flow offers deployment flexibility through Kubernetes and Docker, with strong community and enterprise support available. Azure Data Factory focuses on a cloud-first approach with global availability, ensuring a simpler deployment process and scalability. It benefits from Azure's comprehensive customer service infrastructure, often resulting in faster setup times.

Pricing and ROI: Spring Cloud Data Flow provides open-source access that can reduce costs, though enterprise support incurs additional fees. Azure Data Factory operates on a pay-as-you-go pricing model, aligning with scalability needs, offering potentially better ROI for larger deployments. While Spring Cloud Data Flow may lower initial costs, Azure Data Factory provides a predictable cost model for scaling environments.

To learn more, read our detailed Azure Data Factory vs. Spring Cloud Data Flow Report (Updated: December 2024).

Buyer's Guide

Azure Data Factory vs. Spring Cloud Data Flow

December 2024

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Categories and Ranking

Azure Data Factory

Ranking in Data Integration

1st

Average Rating

8.0

Reviews Sentiment

6.9

Number of Reviews

Ranking in other categories

Cloud Data Warehouse (3rd)

Spring Cloud Data Flow

Ranking in Data Integration

23rd

Average Rating

7.8

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

Streaming Analytics (9th)

Mindshare comparison

As of December 2024, in the Data Integration category, the mindshare of Azure Data Factory is 11.0%, down from 13.3% compared to the previous year. The mindshare of Spring Cloud Data Flow is 1.1%, up from 1.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Integration

Featured Reviews

Thulani David Mngadi

Data architect at Old Mutual

Data flow feature is valuable for data transformation tasks

The workflow automation features in GitLab, particularly its low code/no code approach, are highly beneficial for accelerating development speed. This feature allows for quick creation of pipelines and offers customization options for integration needs, making it versatile for various use cases. GitLab supports a wide range of connectors, catering to a majority of integration needs. Azure Data Factory's virtual enterprise and monitoring capabilities, the visual interface of GitLab makes it user-friendly and easy to teach, facilitating adoption within teams. While the monitoring capabilities are sufficient out of the box, they may not be as comprehensive as dedicated enterprise monitoring tools. GitLab's monitoring features are manageable for production use, with the option to integrate log analytics or create custom dashboards if needed. The data flow feature in Azure Data Factory within GitLab is valuable for data transformation tasks, especially for those who may not have expertise in writing complex code. It simplifies the process of data manipulation and is particularly useful for individuals unfamiliar with Spark coding. While there could be improvements for more flexibility, overall, the data flow feature effectively accomplishes its purpose within GitLab's ecosystem.

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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

"The solution is okay."

"This solution has provided us with an easier, and more efficient way to carry out data migration tasks."

"The feature I found most helpful in Azure Data Factory is the pipeline feature, including being able to connect to different sources. Azure Data Factory also has built-in security, which is another valuable feature."

"The data mapping and the ability to systematically derive data are nice features. It worked really well for the solution we had. It is visual, and it did the transformation as we wanted."

"The solution can scale very easily."

"The security of the agent that is installed on-premises is very good."

"The most valuable feature is the copy activity."

"Data Factory allows you to pull data from multiple systems, transform it according to your business needs, and load it into a data warehouse or data lake."

More Azure Data Factory pros

"The product is very user-friendly."

"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 most valuable feature is real-time streaming."

"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 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 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."

Cons

"Areas for improvement in Azure Data Factory include connectivity and integration. When you use integration runtime, whenever there's a failure, the backup process in Azure Data Factory takes time, so this is another area for improvement."

"There is no built-in function for automatically adding notifications concerning the progress or outline of a pipeline run."

"Azure Data Factory could benefit from improvements in its monitoring capabilities to provide a more robust feature set. Enhancing the ease of deployment to higher environments within Azure DevOps would be beneficial, as the current process often requires extensive scripting and pipeline development. It is also known for the flexibility of the data flow feature, particularly in supporting more dynamic data-driven architectures. These enhancements would contribute to a more seamless and efficient workflow within GitLab."

"There is room for improvement primarily in its streaming capabilities. For structured streaming and machine learning model implementation within an ETL process, it lags behind tools like Informatica."

"The number of standard adaptors could be extended further."

"The speed and performance need to be improved."

"Currently, smaller businesses face a disadvantage in terms of pricing, and reducing costs could address this issue."

"The pricing scheme is very complex and difficult to understand."

More Azure Data Factory cons

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

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

"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."

"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."

"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."

Pricing and Cost Advice

"Pricing is comparable, it's somewhere in the middle."

"Azure products generally offer competitive pricing, suitable for diverse budget considerations."

"I would not say that this product is overly expensive."

"The solution is cheap."

"The pricing is a bit on the higher end."

"My company is on a monthly subscription for Azure Data Factory, but it's more of a pay-as-you-go model where your monthly invoice depends on how many resources you use. On a scale of one to five, pricing for Azure Data Factory is a four. It's just the usage fees my company pays monthly."

"I rate the product price as six on a scale of one to ten, where one is low price and ten is high price."

"The pricing is pay-as-you-go or reserve instance. Of the two options, reserve instance is much cheaper."

More Azure Data Factory 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

13%

Computer Software Company

12%

Manufacturing Company

Healthcare Company

Financial Services Firm

27%

Computer Software Company

18%

Manufacturing Company

Retailer

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

How do you select the right cloud ETL tool?

AWS Glue and Azure Data factory for ELT best performance cloud services.

See all answers

How does Azure Data Factory compare with Informatica PowerCenter?

Azure Data Factory is flexible, modular, and works well. In terms of cost, it is not too pricey. It offers the stability and reliability I am looking for, good scalability, and is easy to set up an...

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How does Azure Data Factory compare with Informatica Cloud Data Integration?

Azure Data Factory is a solid product offering many transformation functions; It has pre-load and post-load transformations, allowing users to apply transformations either in code by using Power Q...

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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

Informatica PowerCenter vs Azure Data Factory

Compared 13% of the time

Snowflake vs Azure Data Factory

Compared 10% of the time

Informatica Intelligent Data Management Cloud (IDMC) vs Azure Data Factory

Compared 8% of the time

Alteryx Designer vs Azure Data Factory

Compared 7% of the time

IBM InfoSphere DataStage vs Azure Data Factory

Compared 4% of the time

More Azure Data Factory Competitors

Apache Flink vs Spring Cloud Data Flow

Compared 35% of the time

TIBCO BusinessWorks vs Spring Cloud Data Flow

Compared 11% 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

MuleSoft Anypoint Platform vs Spring Cloud Data Flow

Compared 4% of the time

More Spring Cloud Data Flow Competitors

Product Reports

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Azure Data Factory

December 2024

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Buyer's Guide

Spring Cloud Data Flow

November 2024

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Microsoft

VMware

Overview

Azure Data Factory efficiently manages and integrates data from various sources, enabling seamless movement and transformation across platforms. Its valuable features include seamless integration with Azure services, handling large data volumes, flexible transformation, user-friendly interface, extensive connectors, and scalability. Users have experienced improved team performance, workflow simplification, enhanced collaboration, streamlined processes, and boosted productivity.

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

1. Adobe 2. BMW 3. Coca-Cola 4. General Electric 5. Johnson & Johnson 6. LinkedIn 7. Mastercard 8. Nestle 9. Pfizer 10. Samsung 11. Siemens 12. Toyota 13. Unilever 14. Verizon 15. Walmart 16. Accenture 17. American Express 18. AT&T 19. Bank of America 20. Cisco 21. Deloitte 22. ExxonMobil 23. Ford 24. General Motors 25. IBM 26. JPMorgan Chase 27. Microsoft (Azure Data Factory is developed by Microsoft) 28. Oracle 29. Procter & Gamble 30. Salesforce 31. Shell 32. Visa

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Buyer's Guide

Azure Data Factory vs. Spring Cloud Data Flow

December 2024

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