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Course Outline

1. Introduction to Apache Superset

  • What is Apache Superset?
  • Role of Superset in modern Business Intelligence (BI)
  • Comparison with traditional BI platforms
  • Key features and capabilities
  • Typical use cases and business scenarios
  • Overview of the Superset ecosystem

2. Apache Superset Architecture and Environment Setup

  • Overview of Apache Superset architecture
  • Core components:
    • Web application
    • Metadata database
    • Visualization layer
    • Security layer
  • Installing Apache Superset
  • Running Superset using containers
  • Configuring development and production environments
  • User interface overview
  • Navigating Superset workspaces

3. Managing Users, Roles, and Security

  • User management
  • Role-based access control (RBAC)
  • Permissions and security models
  • Managing access to datasets and dashboards
  • Creating secure BI environments
  • Best practices for enterprise deployments

4. Connecting Data Sources

  • Understanding supported data sources
  • Connecting relational databases:
    • PostgreSQL
    • MySQL
    • SQL Server
    • Oracle
  • Connecting cloud-based databases
  • Database connection configuration
  • Managing datasets
  • Testing and troubleshooting data connections

5. Working with Datasets and Data Preparation

  • Understanding datasets in Superset
  • Creating datasets from databases
  • Defining columns and metrics
  • Creating calculated columns
  • Using SQL-based datasets
  • Data preparation best practices
  • Optimizing datasets for analysis

6. Exploring and Analyzing Data

  • Using the Explore interface
  • Filtering and slicing data
  • Creating custom queries
  • Selecting appropriate visualization types
  • Performing exploratory data analysis
  • Understanding metrics and dimensions
  • Working with large datasets

7. Creating Data Visualizations

  • Overview of Superset visualization options
  • Creating charts:
    • Bar charts
    • Line charts
    • Pie charts
    • Tables
    • Heatmaps
    • Geographic visualizations
    • Time-series charts
  • Customizing visualization settings
  • Formatting charts for business users
  • Improving data storytelling

8. Advanced Visualization Techniques

  • Creating interactive visualizations
  • Using filters and controls
  • Working with calculated metrics
  • Advanced chart configurations
  • Combining multiple analytical perspectives
  • Visualization performance optimization

9. Building Dashboards

  • Dashboard design principles
  • Creating dashboards from charts
  • Arranging dashboard layouts
  • Adding interactive filters
  • Creating business-focused dashboards
  • Sharing dashboards with users
  • Exporting and presenting reports

10. SQL Integration with Apache Superset

  • SQL Lab overview
  • Writing SQL queries
  • Creating virtual datasets
  • Using SQL for advanced analysis
  • Query optimization
  • Working with joins and complex queries
  • Managing SQL-based analytics workflows

11. Advanced Analytics and Reporting

  • Creating KPIs and business metrics
  • Trend analysis
  • Comparative analysis
  • Time-based reporting
  • Creating executive dashboards
  • Scheduling and sharing reports
  • Supporting data-driven decision-making

12. Performance Optimization

  • Working with large datasets
  • Query performance optimization
  • Database-side optimization
  • Caching strategies
  • Managing dashboard loading times
  • Best practices for scalable deployments

13. Troubleshooting and Administration

  • Common installation issues
  • Database connection problems
  • Debugging visualization errors
  • Managing Superset configuration
  • Monitoring Superset performance
  • Maintaining production environments

14. Hands-on Workshop and Summary

  • Connecting Apache Superset to a database
  • Creating datasets
  • Building interactive visualizations
  • Developing a complete dashboard
  • Applying security and sharing settings
  • Reviewing best practices
  • Questions and answers
  • Next steps for advanced Apache Superset usage

Requirements

  • Experience with business intelligence and data visualization.

Audience

  • Data analysts
  • Data scientists
 14 Hours

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