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."

"I would rate the overall solution a ten out of ten."

"The most valuable features of Google Cloud Dataflow are the integration, it's very simple if you have the complete stack, which we are using. It is overall very easy to use, user-friendly friendly, and cost-effective if you know how to use it. The solution is very flexible for programmers, if you know how to do scripts or program in Python or any other language, it's extremely easy to use."

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

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

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

"The best feature of Google Cloud Dataflow is its practical connectedness."

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

"The service is relatively cheap compared to other batch-processing engines."

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

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

"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 technical support has slight room for improvement."

"I would like Google Cloud Dataflow to be integrated with IT data flow and other related services to make it easier to use as it is a complex tool."

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

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

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

"There are certain challenges regarding the Google Cloud Composer which can be improved."

"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."

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

"Google Cloud is slightly cheaper than AWS."

"The solution is cost-effective."

"Google Cloud Dataflow is a cheap solution."

"The tool is cheap."

"The solution is not very expensive."