Azure Stream Analytics vs. Confluent vs. Google Cloud Dataflow

Data enrichment is effectively done in stream analytics also checking the values with different functionality like windowing and group by clause is effectively working.

Incentivized

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

It is best in cases where you have batch as well as streaming data. Also in some cases where you have batch data right now and in future you will get streaming data. In those cases Dataflow is very good. Also in cases where most of your infra is on GCP. It might not be good when you already are on AWS or Azure. And also you want in-depth control over security and management. Then you can directly use Apache beam over Dataflow.

Incentivized

• Routing of data from multiple inputs to multiple output
• You create your own user define function.
• Intermediate query is working very effectively.

Incentivized

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

Incentivized

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

• Code competency is not that much effective
• Ml models can't be integrated with stream analytics

Incentivized

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

Incentivized

• More templates for Bigquery and App Engine. There is only limited options for templates so the things we use can limit.
• I would like native connectors for Excel (XLSX) to reduce the need for custom wrappers in financial pipelines.
• Debugging Google Cloud Dataflow using only logs in Cloud Logging can be overwhelming sometimes, and it’s not always obvious which specific element in the flow caused a failure. IT uses a lot of time.

Incentivized

It really saved a lot of time and it's flexibility really can give you infra which is future-proof for most of the use cases may it be streaming or batch data. And with this you can avoid use of resource-heavy big data offerings.

Incentivized

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

Azure Stream Analytics is easy to implement and also to integrate compare to other services like iot analytics

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

• Very nice roi while using it.
• Multiple integration is the best functionality

Incentivized

• 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