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Course Outline

  • Introduction
  • Installing and Setting Up Apache Superset
  • Overview of Apache Superset Features and Architecture
  • Connecting Custom Data Sources
  • Data Exploration and Visualization Techniques
  • Dashboard Construction and Report Generation
  • Integrating Apache Superset with SQL Databases
  • Deploying Cloud-Native Apache Superset
    • Initializing the development environment with Docker
    • Leveraging Python's setup tools and pip
  • Overview of Core Features and Apache Superset Architecture
    • Rich and diverse visualization capabilities
    • User-friendly navigation and interface
    • Broad integration support for major databases
  • Connecting Data to Apache Superset
    • Configuring data input streams
    • Optimizing the data ingestion process
  • Executing Advanced Data Analytics
    • Calculating rolling averages for time series data
    • Utilizing Time Comparison features
    • Resampling data via various methodologies
    • Scheduling queries within SQL Lab
  • Advanced Visualization Practices
    • Constructing Pivot Tables
    • Investigating various visualization types
    • Developing custom visualization plugins
  • Creating and Sharing Dynamic Dashboards
    • Incorporating Annotations into Charts
    • Utilizing the REST API
  • Database Integrations with Apache Superset
    • Apache Druid
    • BigQuery
    • SQL Server
  • Security Management in Apache Superset
    • Understanding default roles and creating custom roles
    • Tailoring user permissions
  • Troubleshooting Common Issues

Requirements

  • Foundational knowledge of SQL and database management.
  • No previous experience with Apache Superset is necessary.
 35 Hours

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