Google Managed data processing service
Rating: 8 out of 10
Use Cases and Deployment Scope
In our company we are using Google Cloud Dataflow to create data pipe lines for data transformation and ingestion use cases before loading data into database. Flexibility to create our own flex templates for any special case handling. Capability to fit streaming and batch data loads are some benefits. We have some real time loads, which Dataflow helps alot.
Pros
- Streaming, Real time work load
- Batch processing
- Auto scaling
- flexible pricing
Cons
- inbuild template options can be expanded
- more data connector options
- easy of use
Likelihood to Recommend
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.
SP
Sathish Palanivel(He/Him)
Cloud Architect in Information Technology at Cognizant (10,001+ employees)
Vetted Review
2 years of experience