Compare - Google Cloud Dataflow VS Continual
Here’s the difference between Google Cloud Dataflow and Continual. The comparison is based on pricing, deployment, business model, and other important factors.
About Google Cloud Dataflow
Google Cloud Dataflow is a cloud-based data processing service for both batch and real-time data streaming applications. It enables developers to set up processing pipelines for integrating, preparing and analyzing large data sets, such as those found in Web analytics or big data analytics applications. The Cloud Dataflow software expands on earlier Google parallel processing projects, including MapReduce, which originated at the company. Cloud Dataflow is designed to bring to entire analytics pipelines the style of fast parallel execution that MapReduce brought to a single type of computational sort for batch processing jobs.
About Continual
Continual is the missing AI layer for the modern data stack. Get continually improving predictions – from customer churn to inventory forecasts – directly in your data warehouse. No complex engineering required.
Comparison Table
| Overview | ||
|---|---|---|
| Categories | Data Streaming | Feature Store |
| Stage | Late Stage | Early Stage |
| Target Segment | Enterprise, Mid size | Mid size, Enterprise |
| Deployment | SaaS | SaaS |
| Business Model | Commercial | Commercial |
| Pricing | Freemium | Contact Sales |
| Location | US | San Francisco, California |
| Companies using it | ||
| Contact info |
Add to compare
