Dataflow

#437in Platform as a Service

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What is Dataflow?

Build, run & deploy data pipelines, ML models and Streamlit apps without DevOps. Jupyter, Airflow & VS Code in one managed Python platform. Free trial.

Dataflow is a tool in the Platform as a Service category of a tech stack.

Key Features

Managed Dependencies, Development Ready Workspace, Shared Foundation, Deploy to Production, Cloud-Agnostic

Dataflow Pros & Cons

Pros of Dataflow

No pros listed yet.

Cons of Dataflow

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Dataflow Alternatives & Comparisons

What are some alternatives to Dataflow?

Heroku

Heroku is a cloud application platform – a new way of building and deploying web apps. Heroku lets app developers spend 100% of their time on their application code, not managing servers, deployment, ongoing operations, or scaling.

Google App Engine

Google has a reputation for highly reliable, high performance infrastructure. With App Engine you can take advantage of the 10 years of knowledge Google has in running massively scalable, performance driven systems. App Engine applications are easy to build, easy to maintain, and easy to scale as your traffic and data storage needs grow.

Apache Camel

An open source Java framework that focuses on making integration easier and more accessible to developers.

Jupyter

The Jupyter Notebook is a web-based interactive computing platform. The notebook combines live code, equations, narrative text, visualizations, interactive dashboards and other media.