Try our new research platform with insights from 80,000+ expert users

SAP Data Hub vs Spring Cloud Data Flow comparison

 

Comparison Buyer's Guide

Executive Summary

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

SAP Data Hub
Average Rating
7.6
Reviews Sentiment
6.2
Number of Reviews
3
Ranking in other categories
Data Governance (26th), Metadata Management (8th)
Spring Cloud Data Flow
Average Rating
7.8
Reviews Sentiment
6.8
Number of Reviews
9
Ranking in other categories
Data Integration (22nd), Streaming Analytics (9th)
 

Mindshare comparison

SAP Data Hub and Spring Cloud Data Flow aren’t in the same category and serve different purposes. SAP Data Hub is designed for Data Governance and holds a mindshare of 1.2%, down 1.4% compared to last year.
Spring Cloud Data Flow, on the other hand, focuses on Data Integration, holds 1.1% mindshare, up 0.8% since last year.
Data Governance
Data Integration
 

Featured Reviews

VM
The solution is seamless, but the database sometimes leads to confusion
We used to have multiple different kinds of databases, which internally, had different compliance levels. Retention management is very different now. If the policy is live and the claim has been completed, I couldn't archive the claim. I needed to keep a reference integrity of that claim and understand which policy paid out the claim. With this solution, the policy came in six months ago and qualified for archiving. The claim had been paid and in every environment, the claim had been closed, including the reporting system, the claims system, etc. With the payment set gateway, I can just go and archive. But, we had a hard time during this process. I rate the overall solution a seven out of ten.
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

"SAP is one of the most seamless ERPs that have integrated SAP archiving within Excel. I have not seen this with any other database."
"Its connection to on-premise products is the most valuable. We mostly use the on-premise connection, which is seamless. This is what we prefer in this solution over other solutions. We are using it the most for the orchestration where the data is coming from different categories. Its other features are very much similar to what they are giving us in open source. Their push-down approach is the most advantageous, where they push most of the processing on to the same data source. This means that they have a serverless kind of thing, and they don't process the data inside a product such as Data Hub. They process the data from where the data is coming out. If it is coming from HANA, to capture the data or process it for analytics, orchestration, or management, they go to the HANA database and give it out. They don't process it on Data Hub. This push-down approach increases the processing speed a little bit because the data is processed where it is sitting. That's the best part and an advantage. I have used another product where they used to capture the data first and then they used to process it and give it. In Data Hub, it is in reverse. They process it first and give it, and then they put their own manipulations. They lead in terms of business functions. No other solution has business functions already implemented to perform business analysis. They have a lot of prebuilt business functions for machine learning and orchestration, which we can use directly to get an analysis out from the existing data. Most of the data is sitting as enterprise data there. That's a major advantage that they have."
"The most valuable feature is the S/4HANA 1909 On-Premise"
"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 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 dashboards in Spring Cloud Dataflow are quite valuable."
"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 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

"Nowadays there are some inconsistencies in data bases, however, they upgrade and release the versions to market."
"In 2018, connecting it to outside sources, such as IoT products or IoT-enabled big data Hadoop, was a little complex. It was not smooth at the beginning. It was unstable. It took a lot of time for the initial data load. Sometimes, the connection broke, and we had to restart the process, which was a major issue, but they might have improved it now. It is very smooth with SAP HANA on-premise system, SAP Cloud Platform, and SAP Analytics Cloud. It could be because these are their own products, and they know how to integrate them. With Hadoop, they might have used open-source technologies, and that's why it was breaking at that time. They are providing less embedded integration because they want us to use their other products. For example, they don't want to go and remove SAP Analytics Cloud and put everything in Data Hub. They want us to use SAP Analytics Cloud somewhere else and not inside the Data Hub. On the integration part, it lacks real-time analytics, and it is slow. They should embed the SAP Analytics Cloud inside Data Hub or support some kind of analysis. They do provide some analysis, but it is not extensive. They are moreover open source. So, we need a lot of developers or data scientists to go in and implement Python algorithms. It would be better if they can provide their own existing algorithms and give some connections and drop-down menus to go and just configure those. It will make things really quick by increasing the embedded integrations. It will also improve the process efficiency and processing power. Its performance needs improvement. It is a little slow. It is not the best in the market, and there are other products that are much better than this. In terms of technology and performance, it is a little slow as compared to Microsoft and other data orchestration products. I haven't used other products, but I have read about those products, their settings, and the milliseconds that they do. In Azure Purview, they say that they can copy, manage, or transform the data within milliseconds. They say that they can transform 100 gigabytes of data within three to five seconds, which is something SAP cannot do. It generally takes a lot of time to process that much amount of data. However, I have never tested out Azure."
"The company has everything offshore."
"I would improve the dashboard features as they are not very user-friendly."
"On the tool's online discussion forums, you may get stuck with an issue, making it an area where improvements are required."
"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 configurations could be better. Some configurations are a little bit time-consuming in terms of trying to understand using the Spring Cloud documentation."
"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 is not an easy-to-use tool, so improvements are required."
"The solution's community support could be improved."
"Some of the features, like the monitoring tools, are not very mature and are still evolving."
 

Pricing and Cost Advice

"The Cloud is very expensive, but SAP HANA previous service is okay."
"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."
report
Use our free recommendation engine to learn which Data Governance solutions are best for your needs.
837,501 professionals have used our research since 2012.
 

Top Industries

By visitors reading reviews
Manufacturing Company
14%
Computer Software Company
14%
Financial Services Firm
14%
Government
10%
Financial Services Firm
25%
Computer Software Company
18%
Manufacturing Company
7%
Retailer
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
 

Questions from the Community

What do you like most about SAP Data Hub?
SAP is one of the most seamless ERPs that have integrated SAP archiving within Excel. I have not seen this with any other database.
What needs improvement with SAP Data Hub?
We moved from Oracle. If you're aware of your monitoring system, the RPU market, and the managed system, you should move to HANA, which is an innovative database built by SAP itself. However, this ...
What is your primary use case for SAP Data Hub?
I technically handle the database, like cycle management projects. When transaction data comes in, we see it based on the retention periods. We have to move the data to some secure storage rather t...
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...
 

Overview

 

Sample Customers

Kaeser Kompressoren, HARTMANN
Information Not Available
Find out what your peers are saying about Microsoft, Informatica, Collibra and others in Data Governance. Updated: January 2025.
837,501 professionals have used our research since 2012.