Apache Superset Data Visualization
Apache Superset Data Visualization is a course in building an open-source BI platform with Apache Superset, from installing it with Docker and connecting databases to exploring data in SQL Lab, building charts and interactive dashboards, and managing permissions, row level security and caching. It suits data analysts, database administrators and IT teams who want to host their own dashboards.
- Duration18 hours · 3 days
- FormatOnsite / live online
- Next roundOn request
- CertificateIncluded
Course overview
Many organisations want every department to look at dashboards built on the same data, but are held back by per-user licence costs, data that must stay on their own servers, or a mix of databases their current tools cannot all connect to. Apache Superset is an open-source business intelligence platform that runs in the browser, connects to a wide range of SQL databases, and offers SQL Lab for exploring data alongside interactive dashboards and fine-grained permissions.
This course takes learners through Apache Superset 6, from installing it with Docker Compose, connecting databases and creating datasets, to writing queries in SQL Lab, building basic, time series and map charts, and designing dashboards with native filters and cross-filtering. Learners then look after the platform as administrators, covering roles, row level security, caching, alerts and reports, and theming. The course closes with a project that builds an organisation's BI platform from start to finish.
What you’ll gain
- Understand the Apache Superset architecture and the components you need to look after
- Install and configure Superset and connect it to databases
- Write queries in SQL Lab and build datasets, metrics and many kinds of chart
- Design dashboards with native filters, cross-filtering and drilling
- Manage roles, permissions and row level security safely
- Improve performance with caching and set up alerts and reports
Who this course is for
- Data analysts and report writers who want an open-source BI tool
- Database administrators and IT teams who install and run an organisation's BI platform
- Data engineers who prepare datasets for users across departments
- Anyone with some data and SQL background
Prerequisites
- Some knowledge of data and experience with database systems
- Able to write basic SQL such as SELECT, WHERE, GROUP BY and JOIN
- Basic command-line skills; some Docker experience helps
- A laptop that can run Docker, with permission to install software
Curriculum
Course Details
This course is the 2026 edition of Apache Superset Open-Source Data Visualization, rewritten for Apache Superset 6. It runs for 3 days, 6 hours per day (18 hours in total, 09:00-16:00), as lectures with labs on each learner's own laptop. Beginner to Intermediate level. Day one installs Superset with Docker Compose, connects databases, uses SQL Lab and builds the first charts. Day two builds advanced charts and designs dashboards with alerts and reports. Day three covers administration: roles, row level security, caching, theming and the Embedded SDK, and closes with a workshop that builds an organisation's BI platform. Learners take home the installation files and the dashboard files for every lab.
Day 1 Installing Superset, Connecting Data and Exploring with SQL Lab
Section 1: Getting to Know Apache Superset 6 and Its Architecture
- What Superset is, how it is used and how it differs from licensed BI tools
- Components: web server, metadata database, Redis cache and Celery workers
- What is new in version 6: token-based theming and dark mode
- User roles: administrators, chart authors and dashboard viewers
- Lab: explore the example dashboards and the main Superset screens
Section 2: Lab: Installing Superset with Docker Compose
- Prepare Docker and download a Superset release with a fixed tag
- What the docker-compose file and superset_config.py do
- Set SECRET_KEY, create an administrator and load the example data
- Lean images, and adding database drivers to your own image
- Lab: install Superset on your own laptop and log in for the first time
Section 3: Lab: Connecting Databases and Creating Datasets
- Connect PostgreSQL, MySQL, SQL Server and other databases through a SQLAlchemy URI
- Connection settings: SQL Lab access, DML and file uploads
- Upload CSV or Excel files into a database from the browser
- Physical datasets and virtual datasets built from a query
- Lab: connect the sales database and create ready-to-use datasets
Section 4: Lab: SQL Lab and Jinja Templating
- The SQL Lab screen, schema browser and autocomplete
- Query history, saved queries and sharing queries with the team
- Creating a chart or dataset from query results
- An introduction to Jinja templating and how administrators enable it
- Lab: explore sales data with SQL and save it as a virtual dataset
Section 5: Lab: Building Your First Charts in Explore
- The Explore screen: metrics, dimensions, filters and time grain
- Tables, Big Number, bar, line and pie charts
- Customising colours, labels, number formats and descriptions
- Saving charts, adding them to a dashboard and sharing links
- Lab: a set of KPI charts for monthly and branch sales
Day 2 Advanced Charts and Dashboard Design
Section 6: Lab: Metrics and Calculated Columns in Datasets
- Define shared metrics in SQL so every chart uses the same formula
- Calculated columns for grouping and transforming values
- Time column settings, descriptions and certified datasets
- Datasets that are easy to maintain and avoid repeated SQL
- Lab: shared metrics for sales, profit and margin for the whole team
Section 7: Lab: Time Series and Advanced Analytics
- Line, area, bar and mixed time series charts
- Rolling windows, time comparison and resampling
- Comparing with previous periods and showing percentage change
- Annotation layers for key events
- Lab: daily sales trends with a moving average and a prior-year comparison
Section 8: Lab: Maps and Specialised Charts
- Country maps and province or regional maps
- deck.gl map charts and setting a Mapbox API key
- Heatmaps, treemaps, Sankey and funnel charts
- Choosing the right chart for the business question
- Lab: a sales map by area and a customer journey Sankey chart
Section 9: Lab: Designing Interactive Dashboards
- Layouts with rows, columns, tabs, headers and Markdown
- Native filters, cascading filters and filter scope
- Cross-filtering, Drill to Detail and Drill By
- Design principles for dashboards that managers read quickly
- Lab: a sales dashboard with date, branch and product filters
Section 10: Lab: Sharing Dashboards, Alerts and Reports
- Permalinks and exporting to image, PDF and CSV
- Alerts and reports: scheduled dashboards and condition-based alerts
- What is needed: Celery workers, beat and a headless browser
- Sending by email or Slack, and checking the execution log
- Lab: a weekly sales report and an alert when sales fall below target
Day 3 Administration, Security and Performance
Section 11: Lab: Users, Roles and Permissions
- Built-in roles: Admin, Alpha, Gamma and sql_lab
- Creating custom roles with database, dataset and dashboard access
- An overview of authentication with the database, LDAP and OAuth
- Separating content creators from report viewers
- Lab: design roles for sales, accounting and management
Section 12: Lab: Row Level Security and Auditing
- Regular and base row level security rules tied to datasets and roles
- Writing SQL conditions and testing with users in several roles
- The action log and usage tracking
- Security practices: SECRET_KEY, HTTPS and removing example data
- Lab: regional managers see only their region in a single dashboard
Section 13: Lab: Performance and Caching
- Superset caching on Redis and its settings in superset_config.py
- Cache timeouts at database, dataset and chart level
- Async queries and query time limits
- Tuning queries with indexes, summary tables and lighter virtual datasets
- Lab: measure dashboard load time before and after tuning cache and queries
Section 14: Lab: Theming, Embedding and the REST API
- Brand themes and colours, and dark mode settings
- Embedding dashboards in your own site with the Embedded SDK and guest tokens
- The REST API and exporting or importing dashboards between environments
- Backing up the metadata database and planning upgrades
- Lab: apply a brand theme and move a dashboard from test to production
Section 15: Workshop: An Organisation's BI Platform from Start to Finish
- Take a business brief and data from several tables
- Build the datasets, metrics and charts needed
- Design a dashboard with filters and row level security
- Set up caching and an automated report, then present the result
- Summarise a checklist before launching Superset in the organisation
Schedule & training options
For individuals — public rounds
No public rounds are open right now. Join the waiting list and we will contact you first when the next round opens, or ask us on LINE. Or call 02-570-8449 or 088-807-9770
For organisations — in-house / private
- Tailor the content to your team’s tools and projects
- Your dates, at your office or live online
- Quotation with tax ID for procurement
Instructors
Frequently asked questions
What is Apache Superset and what can you do with it?
Apache Superset is an open-source business intelligence platform from the Apache Software Foundation for building charts and dashboards in the browser. It connects to many SQL databases, includes SQL Lab for exploring data with queries, offers a wide range of charts including time series and maps, and supports detailed user permissions. Version 6 also adds a new theming system with dark mode, making it an option for organisations that want to host their own BI platform.
What are the steps to build a dashboard in Apache Superset?
First connect your database under Database Connections, then create a dataset from a table or from a query written in SQL Lab, and define shared metrics on the dataset. Build a chart in Explore by choosing the chart type, metrics, dimensions and filters, and save it to a dashboard, where you arrange the layout. Add native filters and turn on cross-filtering so viewers can filter by clicking, then set permissions and share it with the people who need it.
What is row level security in Apache Superset?
Row level security restricts users to the rows they are allowed to see within the same charts and dashboards. You create a rule tied to a dataset and a role, with an SQL condition such as region equals North, and Superset adds that condition to the queries automatically. Regional managers can then share one dashboard without separate copies for each region, and you should always test the rules with real accounts for each role.
Who is the Apache Superset Data Visualization course for, and what should I know beforehand?
It suits data analysts, database administrators, data engineers and IT teams who want to turn data into dashboards on their own BI platform. You should have some data knowledge and be able to write basic SQL such as SELECT and GROUP BY, have basic command-line skills, and bring a laptop that can run Docker. Previous Docker experience makes the installation smoother.
What will I be able to do after the Apache Superset Data Visualization course?
You will be able to install Superset with Docker Compose and configure superset_config.py, connect databases and create datasets with shared metrics, write queries in SQL Lab, build basic, time series and map charts, design dashboards with native filters and cross-filtering, set up roles, row level security and caching, and schedule automated reports. Participants receive a Certificate of Completion.
Can the Apache Superset course be run in-house for our team?
Yes. It can be run in-house for your team, with examples adapted to your own work, such as connecting to the databases your organisation already uses and designing roles and row level security around your structure. You can request a quotation from this page, or contact us on LINE @itgenius or by phone on 02-570-8449.