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Course Outline

  • Introduction
  • Setting up Apache Superset
  • Overview of Apache Superset Features and Architecture
  • Connecting Your Own Data Sources
  • Exploring and Visualizing Data
  • Building Your Own Dashboards and Creating Reports
  • Integrating Apache Superset with SQL Databases
  • Installing and Configuring Cloud-Native Apache Superset
    • Utilizing Docker to initialize the development environment
    • Employing Python setup tools and pip
  • Overview of Basic Features and Architecture of Apache Superset
    • Rich and diverse visualization options
    • Intuitive and easy-to-navigate user interface
    • Seamless integration with the majority of databases
  • Connecting Data to Apache Superset
    • Configuring data input parameters
    • Optimizing the data input workflow
  • Conducting Advanced Data Analytics
    • Calculating rolling averages for time series data
    • Utilizing time comparison techniques
    • Resampling data through various methodologies
    • Scheduling automated queries in SQL Lab
  • Performing Advanced Visualization
    • Constructing Pivot Tables
    • Investigating diverse visualization types
    • Developing custom visualization plugins
  • Creating and Sharing Dynamic Dashboards
    • Incorporating Annotations into Charts
    • Leveraging the REST API
  • Integrating Apache Superset with Databases
    • Apache Druid
    • BigQuery
    • SQL Server
  • Managing Security in Apache Superset
    • Comprehending pre-defined roles and designing new ones
    • Refining and customizing permissions
  • Troubleshooting

Requirements

  • Familiarity with basic SQL and general database concepts.
  • Prior experience with Apache Superset is not a prerequisite.
 35 Hours

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