Fivetran vs. Google Cloud Dataflow

Fivetran's business model justifies the use-case where we require data from a single source basically a lot of data but if the requirement is not on the heavier side, Fivetran comes to costly operation when compared to its peers. Otherwise, I'll recommend Fivetran for stability and update and seamless service provider.

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.

• Easily connects to source data using delivered connectors
• Transforms data into standard models and schemas
• Has very good documentation to help quickly setup connectors

Incentivized

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

• More detailed logging
• More flexible choices for time range over which records are synced
• More options for masking and excluding sensitive data

Incentivized

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

Just need to input connection info.

Incentivized

It runs pretty well and gets our data from point A to point cluster quickly enough. Honestly, it's not something I think about unless it breaks and that's pretty rare.

Incentivized

We never seriously considered using anything else. Our data engineers had used Fivetran extensively in previous roles so when it came time to make a decision, there wasn't much of a process. They gladly signed the contract with Fivetran pretty quickly.

Incentivized

Google Cloud Dataproc Cloud Datafusion

• Development cost have reduced for each connector
• The pay-per-use model is still not out their which requires lot of overhead cost

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