Senior Software Engineer at QBE Regional Insurance
Real User
Top 20
2024-03-26T06:50:25Z
Mar 26, 2024
Spring Cloud Data Flow is a useful product if I consider how there are different providers with whom my company had to deal, and most of them offer cloud-based products. I can't explain any crucial circumstances where the product's integration capabilities were helpful, but the aforementioned details explain the scenario for which I used the solution. I was only involved with the development of the product and not with the data pipeline configuration phase. The use of Spring Cloud Data Flow greatly impacted projects' time to market since our company's intention was to actually deploy and ensure that the payment platform integrated with it, which was an easy process. The product's user interface was very intuitive. The tool was deployed in multiple environments, but I am not sure about the production. From the time I started taking up the job in my current organization, I saw that we have deployed the tool in multiple environments wherein the number of users extensively used the product in the UAT environment, which is one of the most stable environments. There were 20 different methods to test the tool. I wouldn't be able to tell you the production details of the tool as I was more part of the production deployment, but I can say that it was deployed with the intent of making it available for 10,000 users. Those who plan to use the product should enjoy the flexibility of the solution. I rate the tool a nine out of ten.
The solution requires little maintenance. My advice to others is for them to follow the documentation. The solution is very well-designed and they deliver on their promises. I rate Spring Cloud Data Flow a seven out of ten.
While the deployment is on-premises, the data center is not on-premises. It's in a different geographical location, however, it was the client's own data center. We deployed there, and we installed the CDF server, then the Skipper server, and everything else including all the microservices. We used the PCF Cloud Foundry platform and for the bank, we deployed in Kubernetes. Spring Cloud Data Flow server is pretty standard to implement. The year before it was a new project, however, now it is already implemented in many, many projects. I think developers should start using it if they are not using it yet. In the future, there could be some more improvements in the area of the data pipeline ETF process. That said, I'm happy with the Spring Cloud Data Flow server right now. Our biggest takeaway has been to design the pipeline depending on the customer's needs. We cannot just think about everything as a developer. Sometimes we need to think about what the customer needs instead. Everything needs to be based on customer flow. That helps us design a proper data pipeline. The task mechanism is also helpful if we can run some tasks instead of keeping the application live 24 hours. Overall, I'd rate the solution nine out of ten. It's a really good solution and a lot cheaper than a lot of infrastructure provided by big companies like Google or Amazon.
Senior Platform Associate L2 at a tech services company with 10,001+ employees
Real User
2020-10-19T09:33:41Z
Oct 19, 2020
We used this product with Kubernetes, which had been recently introduced and we liked it. It was very good, compared to Maven. We did try it with Maven; however, the server took 15 or 16 minutes to start. This is when we switched to Kubernetes and it was very good. They provide a lot of different configurations and environment types. We use Kafka on Kubernetes, as well. The configured was proved by SCDF. I would rate this solution a seven out of ten.
I would rate this product (or set of technologies) a solid eight out of 10. The things that would keep me from giving it a full 10 are the fact that the graphic user interface portion of the toolset still needs some polishing and performs somewhat slowly. However, I have not had an opportunity to run this tool set on higher performing machines, and have been limited to simply running it within a set of virtual machines on my own workstation.
What is data integration? Data integration is the process of combining data that resides in multiple sources into one unified set. This is done for analytical uses as well as for operational uses.
Spring Cloud Data Flow is a useful product if I consider how there are different providers with whom my company had to deal, and most of them offer cloud-based products. I can't explain any crucial circumstances where the product's integration capabilities were helpful, but the aforementioned details explain the scenario for which I used the solution. I was only involved with the development of the product and not with the data pipeline configuration phase. The use of Spring Cloud Data Flow greatly impacted projects' time to market since our company's intention was to actually deploy and ensure that the payment platform integrated with it, which was an easy process. The product's user interface was very intuitive. The tool was deployed in multiple environments, but I am not sure about the production. From the time I started taking up the job in my current organization, I saw that we have deployed the tool in multiple environments wherein the number of users extensively used the product in the UAT environment, which is one of the most stable environments. There were 20 different methods to test the tool. I wouldn't be able to tell you the production details of the tool as I was more part of the production deployment, but I can say that it was deployed with the intent of making it available for 10,000 users. Those who plan to use the product should enjoy the flexibility of the solution. I rate the tool a nine out of ten.
The solution requires little maintenance. My advice to others is for them to follow the documentation. The solution is very well-designed and they deliver on their promises. I rate Spring Cloud Data Flow a seven out of ten.
While the deployment is on-premises, the data center is not on-premises. It's in a different geographical location, however, it was the client's own data center. We deployed there, and we installed the CDF server, then the Skipper server, and everything else including all the microservices. We used the PCF Cloud Foundry platform and for the bank, we deployed in Kubernetes. Spring Cloud Data Flow server is pretty standard to implement. The year before it was a new project, however, now it is already implemented in many, many projects. I think developers should start using it if they are not using it yet. In the future, there could be some more improvements in the area of the data pipeline ETF process. That said, I'm happy with the Spring Cloud Data Flow server right now. Our biggest takeaway has been to design the pipeline depending on the customer's needs. We cannot just think about everything as a developer. Sometimes we need to think about what the customer needs instead. Everything needs to be based on customer flow. That helps us design a proper data pipeline. The task mechanism is also helpful if we can run some tasks instead of keeping the application live 24 hours. Overall, I'd rate the solution nine out of ten. It's a really good solution and a lot cheaper than a lot of infrastructure provided by big companies like Google or Amazon.
We used this product with Kubernetes, which had been recently introduced and we liked it. It was very good, compared to Maven. We did try it with Maven; however, the server took 15 or 16 minutes to start. This is when we switched to Kubernetes and it was very good. They provide a lot of different configurations and environment types. We use Kafka on Kubernetes, as well. The configured was proved by SCDF. I would rate this solution a seven out of ten.
I would rate this product (or set of technologies) a solid eight out of 10. The things that would keep me from giving it a full 10 are the fact that the graphic user interface portion of the toolset still needs some polishing and performs somewhat slowly. However, I have not had an opportunity to run this tool set on higher performing machines, and have been limited to simply running it within a set of virtual machines on my own workstation.