Cython vs. NumPy vs. Python RPA vs. Spring Cloud Data Flow Comparison Chart

Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today. NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. NumPy’s high level syntax makes it accessible and productive for programmers from any background or experience level. NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use. With this power comes simplicity: a solution in NumPy is often clear and elegant.

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.