Analytics engineer is the latest role that combines the technical skills of a data engineer with the business knowledge of a data analyst. They are typically coding in SQL, building dbt data models, and automating data pipelines. You could say they own the steps between data ingestion and orchestration. Whether you are a seasoned analytics engineer or new to the field, it’s important to continually learn new things and improve the work you’ve already done.
If you’ve got an agile team interested in shipping fast without breaking things, this post is for you. In this piece, I’m going to explain how we at Rainforest QA approach automated testing in a continuous integration / continuous delivery (CI/CD) pipeline, with a focus on end-to-end (e2e) functional testing. The aim of our testing and other DevOps methodologies is to maintain a healthy balance between speed and product quality.
This Eckerson Group report gives you a good understanding of how the Unravel platform addresses multiple categories of data observability—application/pipeline performance, cluster/platform performance, data quality, and, most significant, FinOps cost governance—with automation and AI-driven recommendations.
