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Course Outline

Introduction

Installing and Configuring Cloud-Native Apache Superset

  • Using Docker to initialize the development environment.
  • Utilizing Python's setup tools and pip.

Overview of Basic Features and Architecture of Apache Superset

  • Rich visualizations.
  • Easy-to-navigate user interface.
  • Integration with most databases.

Connecting Data to Apache Superset

  • Configuring data input.
  • Improving the input process.

Conducting Advanced Data Analytics

  • Calculating a rolling average of time series.
  • Working with Time Comparison.
  • Resampling data using various methods.
  • Scheduling queries in SQL Lab.

Performing Advanced Visualization

  • Creating a Pivot Table.
  • Exploring different visualization types.
  • Building a visualization plugin.

Creating and Sharing Dynamic Dashboards

  • Adding annotations to your chart.
  • Using the REST API.

Integrating Apache Superset with Databases

  • Apache Druid
  • BigQuery
  • SQL Server

Managing Security in Apache Superset

  • Understanding provided roles and creating new roles.
  • Customizing permissions.

Troubleshooting

Summary and Conclusion

Requirements

  • Experience with business intelligence and data visualization.
  • Familiarity with Apache Superset fundamentals.

Audience

  • Data analysts
  • Data scientists
  • Data engineers
 14 Hours

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