![]() ![]() gitlab-ci.yml file in a repo to define the CI/CD settings to invoke based on the triggers you define. Pipelines are a structured topographical way to configure continuous integration, delivery, and deployment in GitLab. This post breaks down our initial, modest solution for building a CI/CD deployment pipeline that leverages Cloud Composer Airflow and GitLab. GitLab - for source code management and to target multiple data environments for testing and quality assurance.Cloud Composer - for the orchestration of our data pipelines ETL jobs and scheduled tasks.Airflow embodies the concept of Directed Acyclic Graphs (DAGs), which are written in Python, to declare sequential task configurations that carry out our workflow. Airflow - to manage data services through GCP.These are pretty standard DevOps requirements, and to achieve them, our team implemented a Continuous Integration Continuous Deployment (CI/CD) approach for our data applications in Google Cloud Platform (GCP). To ensure the quality of incoming features, the team sought to create a pipeline that automatically validated those features, build them to verify their interoperability with existing features and GitLab, and alert the respective owners of any failures in the pipeline. ![]() This means we need to build better, more configurable and more collaborative tooling that prevents code collisions and enforces software engineering best practices. The Ripple Data Engineering team is expanding, which means higher frequency changes to our data pipeline source code. ![]()
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