April 2020 Summaries

Companies seeking to derive value from their extensive data lakes often require modern tools for processing and visualization. Preset emphasizes its commitment to open-source solutions by highlighting the integration of Apache Superset with Dremio, facilitated by a DBAPI-compatible Python driver and a SQLAlchemy dialect. This integration allows users to leverage the analytical capabilities of Superset with Dremio's data processing features, offering a pathway to more advanced data analytics. For those looking to streamline the setup process, Preset offers an alpha version of a hosted Superset solution that can be easily connected with Dremio, enabling users to quickly harness the value of their data.

The post describes a step-by-step approach to deploying a customizable GitHub Engagement Dashboard using open-source tools like a simple data pipeline and other assets in a Superset environment. It explains how to extract data from the GitHub API, transform it into meaningful insights using Jupyter notebooks, and load it into a database for visualization in Superset. The process involves handling challenges such as rate limiting and nested REST calls, with solutions like caching and incremental data loading. It also covers importing assets into Superset, customizing dashboards, and creating reusable metrics to enhance data analysis. The author emphasizes the contribution to the open-source community, offers tips for optimizing Superset features, and invites feedback to improve the project, highlighting the support available through an alpha program at Preset for hosted Superset experiences.