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Course Outline
Introduction
Installing and Configuring Cloud-Native Apache Superset
- Using Docker to initialize development environment
- Using 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
- Getting a rolling average of the time series
- Working with Time Comparison
- Resampling the 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 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
- Familiar with Apache Superset fundamentals
Audience
- Data analysts
- Data scientists
- Data engineers
14 Hours
Testimonials (2)
The course material was commendable, featuring insightful overviews of Linux and Superset. While the Python component was comprehensive, it demanded a substantial coding proficiency
EMMANUEL MFANA KUNENE - Palladium Group
Course - Apache Superset for Administrators
The easy rapport with Gunnar was really helpful in learning the topic(s). There was little to no pressure to follow along perfectly with examples as can happen in some classes. He was very supportive and would happily retrace our steps and figure out the issue. Being the only person in the class allowed me to also ask a lot of questions of Gunnar. It was a very informative class.
