Confluent vs. Google Cloud Dataflow

If you have a need to stream data, real time or segmented structured data then Confluent is a great platform to do so with. You won't run into packet transfer size limitations that other platforms have. Flexibility in on-prem, cloud, and managed cloud offerings makes it very flexible no matter how you choose to implement.

Incentivized

Based on my experience, streaming / real time / machine learning / AI type of processing and batch processing which needs less transformation are very well suited. Work load that needs complex transformation / multiple hops gets very complicated to implement. New feature like Dataflow SQL option will come in handy for sql heavy users.

• Products work great.
• Training is available.
• Customer support is good.

Incentivized

• Streaming, Real time work load
• Batch processing
• Auto scaling
• flexible pricing

• Cloud based Azure platform features for Confluent lacks behind AWS And GCP

Incentivized

• inbuild template options can be expanded
• more data connector options
• easy of use

The support from the Confluent platform is great and satisfying. We have been working with Confluent for more than a year now. They sent out resident architects to help us set up Confluent cluster on our cloud and help us troubleshoot problems we have encountered. Overall, it has been a great experience working with the Confluent Platform.

Incentivized

For our use case it was very important that the technology we were working with fit into our Azure architecture, and met our data processing size requirements to stream data within certain SLAs. Confluent more than met our performance requirements and compared to the others scale options and cost to run it was more than financially viable as a platform solution to our global operations.

Incentivized

Google Cloud Dataproc Cloud Datafusion

• It enables us to develop event driven application.
• It increases our ability to handle streaming data.
• It reduces latency of communication.

Incentivized

• cost saving from managing our own data center for ETL servers
• consumption based pricing
• with auto scaling feature, we were able to expand components to support work load