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.
Suppose an e-commerce website has top-notch functional features and runs on the best possible technology of today. It also allows users to browse multiple products and complete their shopping experience. But the interface is confusing, lacks proper structure, and has an inconsistent layout. Would you navigate that site? We think not! The visual layout takes precedence in attracting traffic despite functional aspects being critical to a site’s success.
Software testing is an essential part of software development, as it helps to ensure that the software meets the requirements and is fit for its intended purpose. A test plan is a document that outlines the strategy, objectives, resources, and schedule of a software testing process. Creating a thorough and effective test plan is critical to the success of software testing. It can help identify potential problems or issues that may arise during the process.
Many changes keep happening in software daily, especially when it is big, like Netflix, Amazon, Facebook, etc. Such software contains a lot of modules, and there are different teams working on them that might never know each other’s names. But they still affect the same software even if they do in different ways. Along with working on features and enhancements, some people work on bugs and regularly rectify them to integrate the new code.