Azure Data Factory vs. Google Cloud Dataflow

In a data pipeline, you will be able to add different kinds of activities for example connect from your on-premise SFTP and move CSV files to storage accounts. As well data factory has its own data flow if you are an ETL developer who experimented with maybe you have worked with SSIS, thus, you will start quickly with this new feature of the data factory.

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.

• Creating ETL and ELT workflows as well as orchestrating and monitoring pipelines without writing any code.
• Hybrid data integration is easily and agilely possible through this software.
• It has lot of various useful components

Incentivized

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

• Learning curve for pipeline creation interface.
• Alerting isn't necessarily built in. Had to work around this to meet team needs.
• With GIT enabled, some features can only be done via git, while some need to be done via the portal.

Incentivized

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

So far product has performed as expected. We were noticing some performance issues, but they were largely Synapse related. This has led to a shift from Synapse to Databricks. Overall this has delayed our analytic platform. Once databricks becomes fully operational, Azure Data Factory will be critical to our environment and future success.

Incentivized

We have not had need to engage with Microsoft much on Azure Data Factory, but they have been responsive and helpful when needed. This being said, we have not had a major emergency or outage requiring their intervention. The score of seven is a representation that they have done well for now, but have not proved out their support for a significant issue

Incentivized

Azure Data Factory fits well into our overall systems architecture where we already utilize largely Azure services and also Microsoft based products in the on-premises environment. I think cost structure is also very competitive with Azure Data Factory. Most services provide a visual interface for designing ETL workflows, but our team found Azure Data Factory's interface more intuitive.

Incentivized

Google Cloud Dataproc Cloud Datafusion

• Limiting the amount of data moving up and down from the cloud for cloud-native applications.
• Overall simple to use interface which is actually easier for a first time ETL developer than SSIS.

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