May 2019 Summaries

The text provides a detailed guide on building an analytics dashboard using Vue, Cube.js, and Laue, with insight into the integration of the open-source Cube framework, which supports high-performance data analytics. The tutorial outlines the setup of a Cube backend using a Postgres database deployed on Heroku, and demonstrates how to create a Vue application using vue-cli or Vue UI. The process includes incorporating Bootstrap for styling, setting up a Cube instance with backend URL and API token, and utilizing the Cube Vue Client for data queries. The guide also explains the creation of components, such as a QueryBuilder and Chart, to display data, including user counts and charts rendered with the Laue library. The step-by-step instructions culminate in a fully functional dashboard, with links provided for further resources and the live version of the dashboard.

In the realm of Quality Engineering and Test Automation, the lack of visibility is often a significant issue, as it is crucial for all team members, not just Quality Analysts, to be aware of the test automation process. Visibility tools like Story Walls and Burn Down Charts are common in Agile practices, but the visibility of test automation specifically is lacking, which can hinder responsibility and perceived value. To address this, a Test Automation Dashboard was developed using MySQL and Cube.js, providing essential metrics that indicate the health of a product and aid in decision-making. This dashboard is framework agnostic, allowing integration with various test automation frameworks as long as they can update the MySQL database. The dashboard aims to answer key questions about test coverage and execution results, and its setup process involves using Node.js, npm, and MySQL to display the results on a local server.

The text provides a detailed guide on building an analytics dashboard using Cube Backend and Redash, offering a solution for those who prefer not to code their own dashboards. It highlights the benefits of using Cube, such as simplifying query updates and managing large data volumes efficiently through pre-aggregations, which improve both performance and cost-effectiveness. The guide explains the setup process, including using a PostgreSQL database and hosting on Heroku, and covers creating a Redash dashboard by setting up a JSON Data Source, forming queries, and adding visualizations like bar charts. It also describes how to implement filters using Redash's Parameters feature, enabling dynamic queries and dashboard-level filter values for enhanced interactivity. The resulting live dashboard can be published, queried, or displayed with auto-update capabilities.

You can build an analytical application with Cube.js, which solves problems like analytic SQL generation, query results caching and execution orchestration, data pre-aggregation, security, and API for query results fetch. For Angular developers, Cube.js has a binding that provides flexibility to work with any charting library, such as ng2-charts. To build an analytical dashboard using Angular, you need to set up a Cube.js backend, define a data model, deploy the backend to Heroku, create a chart component with ng2-charts, and finally build a simple dashboard in your app.component. The Cube.js Angular Client is used to load data from the backend, and Bootstrap provides styling for the dashboard.