March 2021 Summaries

COTA is a company that combines oncology expertise with advanced technology and analytics to organize real-world medical treatment data for cancer research and care. They use various solutions, including Real World Analytics (RWA), to support oncologists' clinical decision-making and researchers' development of new drugs and therapies. COTA has access to millions of electronic oncology patient records and uses data analytics to improve their technology. They have adopted the Cube platform to reduce development time and integrate easily with data from various sources. The Cube team is collaborative, and COTA's use of Cube.js has improved their products and performance. However, they still see areas for improvement in Cube.js, such as more out-of-the-box Angular support, and plan to migrate to containerized Docker deployments.

This tutorial explains how to integrate a pivot table into a React application using AG Grid and Cube, focusing on creating an efficient analytical API and using AG Grid's features to display data. Pivot tables are described as tools that summarize large datasets into aggregated, meaningful views using measures and dimensions. AG Grid is highlighted for its extensive features suitable for enterprise applications, available in both community and enterprise versions, with pivoting capabilities in the latter. Cube is introduced as an open-source analytical API platform that creates APIs over databases, facilitating high-level data manipulation without direct SQL queries, and includes a caching layer for efficient data retrieval. The guide walks through setting up a Cube project, connecting it to a Postgres database, generating a data schema, and creating a query to fill the pivot table. It then details steps to incorporate AG Grid into a React app, including installing necessary packages, creating a pivot table component, and configuring data transformations and aggregation functions. The tutorial emphasizes AG Grid's documentation and encourages experimentation with its features, while also inviting engagement and feedback on the tutorial's content.

We've created an openly accessible analytical app with Cube, which can be accessed by anyone without authentication checks being enabled. We then added authentication using signed and encrypted JSON Web Tokens (JWTs) to secure the API. Next, we implemented authorization with JWT claims to control access to data based on users' roles. Finally, we integrated the web application with Auth0 as an external authentication provider, which identifies all users and generates JWTs for them, ensuring only logged-in users can access the app and perform queries to Cube. With these measures in place, our app now implements IAAA (identification, authentication, authorization, accountability), making it a secure multi-tenant analytics platform.

This tutorial explores how to build an analytical application on top of Google BigQuery, a serverless data warehouse. It uses public datasets from BigQuery to visualize the impact of the COVID-19 pandemic on people's lives for every country in the world. The application is built using Cube, an open-source analytical API platform that provides a semantic layer, caching, and high-level identifiers to work with data. The tutorial covers creating an API over BigQuery, defining a data schema, exploring the data, building an analytical app, and understanding how the app retrieves data from the Cube API.