For software development to succeed in Switzerland, that is to justify the relatively high development cost, it is essential to offer unique advantages in terms of timeliness and quality assurance. At Zurich University of Applied Sciences, we are proud to have contributed a number of tools for quality assessment and linting especially for cloudware – among others, the first Docker Compose checker, the first multi-Dockerfile linter, and the first advanced Helm and SAM consistency scans.
As we also teach Python programming to first-year engineering students, we consider it important to encourage the frequent use of linting tools. This blog post introduces such a service, naturally doubling as informal case study on how to deliver SaaS linting functionality without much effort through serverless technologies.
As presented in a prior post, Singer.io is a modern, open-source ETL (Extract, Transform and Load) framework for integrating data from various sources, including online datasets and services, with a focus on being simple and light-weight. The basics of the framework were explored in our last post on the topic, so we will refer you to that if you are unfamiliar.
This post is about our process for deploying Singer to the cloud, more specifically, to the Cloud Foundry open source cloud application platform. This was done in the context of researching the maturity of data transformation tools in a cloud-native environment. We will explore the options for deploying Singer taps and targets to a cloud provider and discuss our implementation and deployment process in detail.
Singer.io is an open-source JSON-based data shifting (ETL: extract, transform, load) framework, designed to bring simplicity when moving data between a source and a destination service on the Internet. In this post, we present the framework as entry point into the world of SaaS-level data exchange and some associated research questions.