CICDtas

CICDtas is a term that refers to the practice of integrating Continuous Integration (CI) and Continuous Delivery/Deployment (CD) principles into the development of data analytics and data science projects. Traditionally, CI/CD pipelines have been heavily associated with software development, focusing on automating the building, testing, and deployment of applications. However, the challenges of reproducibility, versioning, and efficient deployment in data-related workflows have led to the adoption of CI/CD methodologies in this domain.

The core idea behind CICDtas is to apply the same automation and collaboration benefits to data pipelines, machine learning models, and analytical outputs. This involves automating the testing of data quality, the training and validation of machine learning models, and the deployment of these models or analytical dashboards into production environments. It aims to reduce manual errors, accelerate the delivery of insights, and ensure the reliability and consistency of data products. Key components often include version control for code, data, and models, automated testing frameworks tailored for data and ML, and automated deployment strategies for various data-centric artifacts. The ultimate goal is to create a more robust and agile data development lifecycle.