Apache Superset 101 Session

Introduction

• Session led by Evan Rousakis, PMC member and Developer Advocate at Preset.
• The session covers the basics of Apache Superset, a Business Intelligence (BI) tool, and its community.
• Available on YouTube for future reference.
• Interaction via Slack link provided.

What is Superset?

• Open Source BI Product: Part of Apache Software Foundation (ASF), a top-level project.
• Project Origin: Started by Maxim Bamin at Airbnb, now under ASF.
• ASF Role: Governs the repository and project practices.

Community Roles

• Contributor: Anyone can contribute, technical or non-technical.
• Committer: Has write access to repository.
• PMC Member: Voted in for security and governance roles.

Benefits of Open Source BI

• Avoid Vendor Lock-in: Run and customize Superset as desired.
• Deployment Flexibility: Can be deployed anywhere and customized.

Superset Features

• Database Connectivity: Connects to various databases via SQLAlchemy.
• Chart Creation: Advanced SQL IDE, variety of charts, and dashboard creation.
• Customization and Extension: Plugin framework for additional features.
• Data Representation: Dashboards with multiple components and filters.

Superset Capabilities

• API: Robust API with v1 endpoints.
• CLI: For managing assets as code and importing/exporting dashboards.
• Alerting and Reporting: Automatic reports and alerts based on data conditions.

Introduction to Preset

• Preset: Managed version of Superset offering scalability, security, and additional features.

Demo Overview

• Data Connection: How to connect to a database (e.g., Mother Duck).
• SQL Lab Usage: Running SQL queries and creating datasets.
• Chart and Dashboard Creation: Using SQL Lab results to create charts and dashboards with filters.
• Cross Filters and Drill-downs: Interactive data exploration through dashboards.
• Alerts and Reports: Setting up regular alerts and reports via email or Slack.

Key Features Demonstrated

• SQL Lab: Writing and running SQL queries, creating charts and datasets.
• Dashboard Customization: Adding filters, customizing layouts, using markdown.
• Chart Types and Features: Including bar charts, pie charts, and more.
• Interactive Features: Cross filtering, drilling into data.

Additional Technical Features

• Caching: Configurable caching for efficient data handling.
• Theming and Customization: CSS styling and color palette adjustments.

Q&A Highlights

• Discussions on SQL generation via NLP.
• Complex report generation and integration with AI.
• Handling permissions and user access control.
• Exporting charts and dashboards as CSV or other formats.
• Localization and number formatting support.
• Support for GIS and map visualizations.

Conclusion

• Encouragement to engage with the community via Slack.
• Further exploration of Apache Superset and Preset recommended.