Here’s the difference between Airflow and Google Cloud Dataflow. The comparison is based on pricing, deployment, business model, and other important factors.
Apache Airflow is a workflow automation and scheduling system that can be used to author and manage data pipelines. Airflow uses workflows made of directed acyclic graphs (DAGs) of tasks.
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.
Overview | ||
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Categories | Workflow Orchestration | Data Streaming |
Stage | Early Stage | Late Stage |
Target Segment | Enterprise, Mid size | Enterprise, Mid size |
Deployment | SaaS | SaaS |
Business Model | Open Source | Commercial |
Pricing | Not Available | Freemium |
Location | US | US |
Companies using it | ||
Contact info |