Aiven Platform vs Google Cloud Dataflow comparison

"What I like best about the tool is that the process for the services is faster compared to other methods. It's easier to use because Aiven for Apache Kafka handles the maintenance, so we have less to manage. We only use Kafka to manage its connectivity."

"One of the most valuable features of Aiven Platform is that it handles the upgrades for us seamlessly, saving us time that would be spent on routine upgrades."

"The integration within Google Cloud Platform is very good."

"It is a scalable solution."

"Google's support team is good at resolving issues, especially with large data."

"Google Cloud Dataflow is useful for streaming and data pipelines."

"I don't need a server running all the time while using the tool. It is also easy to setup. The product offers a pay-as-you-go service."

"The most valuable features of Google Cloud Dataflow are scalability and connectivity."

"It allows me to test solutions locally using runners like Direct Runner without having to start a Dataflow job, which can be costly."

"The solution allows us to program in any language we desire."

"I would really like to see Aiven Platform add a user interface for database backups, as this would eliminate the need for a third-party solution."

"One challenge we face is when we want to update the version, for example, from 3.6 to 3.7. It will spawn new nodes, and then there's rebalancing and syncing from other brokers. There's high CPU usage during this process, so the solution can't be used for a while, causing some downtime in our services. To tackle this challenge, we schedule maintenance updates during low-traffic periods when there's less risk and fewer users use the services."

"I would like to see improvements in consistency and flexibility for schema design for NoSQL data stored in wide columns."

"Promoting the technology more broadly would help increase its adoption."

"The technical support has slight room for improvement."

"When I deploy the product in local errors, a lot of errors pop up which are not always caught. The solution's error logging is bad. It can take a lot of time to debug the errors. It needs to have better logs."

"The deployment time could also be reduced."

"The solution's setup process could be more accessible."

"They should do a market survey and then make improvements."

"Occasionally, dealing with a huge volume of data causes failure due to array size."

"Google Cloud Dataflow is a cheap solution."

"The tool is cheap."

"On a scale from one to ten, where one is cheap, and ten is expensive, I rate Google Cloud Dataflow's pricing a four out of ten."

"The solution is cost-effective."

"The price of the solution depends on many factors, such as how they pay for tools in the company and its size."

"The solution is not very expensive."

"Google Cloud is slightly cheaper than AWS."

"On a scale from one to ten, where one is cheap, and ten is expensive, I rate the solution's pricing a seven to eight out of ten."