Here’s the difference between AWS S3 and Google Cloud Dataflow. The comparison is based on pricing, deployment, business model, and other important factors.
Amazon Simple Storage Service (Amazon S3) is an object storage service that offers industry-leading scalability, data availability, security, and performance. This means customers of all sizes and industries can use it to store and protect any amount of data for a range of use cases, such as data lakes, websites, mobile applications, backup and restore, archive, enterprise applications, IoT devices, and big data analytics. Amazon S3 provides easy-to-use management features so you can organize your data and configure finely-tuned access controls to meet your specific business, organizational, and compliance requirements. Amazon S3 is designed for 99.999999999% (11 9's) of durability, and stores data for millions of applications for companies all around the world.
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 | Data Lakes | Data Streaming |
Stage | Late Stage | Late Stage |
Target Segment | Enterprise, Mid size | Enterprise, Mid size |
Deployment | SaaS | SaaS |
Business Model | Commercial | Commercial |
Pricing | Freemium | Freemium |
Location | US | US |
Companies using it | ||
Contact info |