Apache Superset Fundamentals

Overview

This instructor-led live (online or onsite) training is designed for data analysts and scientists who wish to explore, organize, and visualize data using Apache Superset.

By the end of the training, participants will be able to:

• Install and configure Apache Superset.
• Connect data from various databases.
• Visualize large datasets.
• Create and share complex dashboards.

Course Format

• Interactive lecture and discussion.
• Multiple exercises and hands-on practice.
• Practical implementation in a live-lab environment.

Target Audience

• Data Analysts
• Data Scientists

Requirements

Experience in business intelligence and data visualization.

09:00 – 09:30 — Introduction to Apache Superset

• The role of Superset in the analytical process
• Main modules: SQL Lab, Explore, Datasets, Dashboards
• Workflow: Source → dataset → chart → dashboard

09:30 – 11:00 — Working with Data: Datasets

• Fundamentals of working with datasets
• Dataset types: physical vs. virtual
• Defining columns, metrics, and filters
• Best practices for naming and documentation

Exercises

• Creating your own dataset from a table
• Adding a metric (e.g., count distinct, sum, ratio)
• Setting filterable fields

Break 11:00 – 11:15

11:15 – 13:00 — SQL LAB (core module)

• Writing SQL queries
• CTEs, analytical windows, aggregations
• Creating and saving queries (Saved Query)
• Building Virtual Datasets based on SQL
• Limits, filtering, and result previews

Exercises

• Aggregate query
• Preparing a data model for a chart
• Saving a query as a dataset

14:00 – 15:30 — Data Analysis and Verification

• How to verify data before creating visualizations
• Data quality control
• Comparing SQL results vs. dataset
• Creating datasets for other users

15:30 – 16:30 — Analytical Best Practices

• Naming datasets, metrics, and charts
• Data logic and consistency
• Avoiding interpretation errors
• Preparing data with dashboards in mind

16:30 – 17:00 — Q&A Session and Trainer Consultations

09:00 – 10:30 — Creating Visualizations (Explore)

• Types of charts and their applications
• Setting metrics, groupings, and filters
• Advanced chart options (annotations, forecast, custom SQL)
• Optimal presentation of results

Exercises

• Creating 3 charts of different types

10:30 – 11:30 — Analytical Tables and Metrics

• Creating pivot tables
• Configuring drilldowns, sorting, and dynamic columns
• Communicating results in tables

Exercises

• Creating a detailed + aggregate table

Break 11:30 – 11:45

11:45 – 13:00 — Building Dashboards

• Designing dashboard structure
• Adding charts and arranging their layout
• Global filters and filter mapping
• Dashboard color consistency and UX

Exercises

• Building a complete dashboard

14:00 – 15:00 — Sharing Resources and Teamwork

• Sharing: datasets, charts, dashboards
• Visibility for users and groups
• Creating drafts and copies
• How the admin-analyst supports users

15:00 – 16:00 — Data Export and Reporting

• Exporting CSV, Excel, downloading data from visualizations
• Exporting charts and dashboards
• Automatic email delivery (if active in the current instance)
• Working scenarios with external tools (Excel, Power BI, Teams, SharePoint)

16:00 – 16:45 — Case Study: from raw source → dashboard

• End-to-end process
• Connecting SQL, datasets, and visualizations
• Conclusions and best practices

16:45 – 17:00 — Summary + Q&A