Conda vs. Homebrew vs. IronPython vs. Spring Cloud Data Flow Comparison Chart

The missing package manager for macOS (or Linux). The script explains what it will do and then pauses before it does it. Homebrew installs the stuff you need that Apple (or your Linux system) didn’t. Homebrew installs packages to their own directory and then symlinks their files into /usr/local (on macOS Intel). Homebrew won’t install files outside its prefix and you can place a Homebrew installation wherever you like. Trivially create your own Homebrew packages. It’s all Git and Ruby underneath, so hack away with the knowledge that you can easily revert your modifications and merge upstream updates. Homebrew formulae are simple Ruby scripts. Homebrew complements macOS (or your Linux system). Install your RubyGems with gem and their dependencies with brew. Homebrew Cask installs macOS apps, fonts and plugins and other non-open source software. Making a cask is as simple as creating a formula.

Microservice-based streaming and batch data processing for Cloud Foundry and Kubernetes. Spring Cloud Data Flow provides tools to create complex topologies for streaming and batch data pipelines. The data pipelines consist of Spring Boot apps, built using the Spring Cloud Stream or Spring Cloud Task microservice frameworks. Spring Cloud Data Flow supports a range of data processing use cases, from ETL to import/export, event streaming, and predictive analytics. The Spring Cloud Data Flow server uses Spring Cloud Deployer, to deploy data pipelines made of Spring Cloud Stream or Spring Cloud Task applications onto modern platforms such as Cloud Foundry and Kubernetes. A selection of pre-built stream and task/batch starter apps for various data integration and processing scenarios facilitate learning and experimentation. Custom stream and task applications, targeting different middleware or data services, can be built using the familiar Spring Boot style programming model.