1. Connection
   1. Let's connect to a data warehouse and configure a data source
      1. Data ‣ Databases and select the + Database
      2. Create a new database
         1. database name
         2. SQLAlchemy URI
      3. Test Connection
2. Dataset
   1. Let's select specific tables (Dataset)
      1. Data ‣ Datasets and select the + Dataset
      2. Select your Database, Schema and Table
   2. Customizing column properties if you need
      1. Is the column temporal?
      2. Is the column filterable?
      3. Is the column dimensional?
   3. Define Superset semantic layer
      1. 2 types of computed data
         1. Virtual metrics
         2. Virtual calculated column
3. Chart
   1. Explore
      1. no-code
      2. Select your dataset ‣ select the chart ‣ customize the appearance (define chart type and more) ‣ publish
   2. SQL Lab
      1. SQL IDE for cleaning, joining, and preparing data for Explore workflow
4. Dashbord
   1. your can save chart add it to an existing dashboard
   2. create new dashboard add chart(s) to a new dashboard
5. Use SQL Lab
   1. SQL Lab >> SQL Editor
   2. write a sql query and run it
6. Why?
   1. Open source (!!!)
   2. Community!
   3. Pre built visualizations
   4. we can use dashboards into our data applications
   5. use can use locally with one command using Docker Compose
      1. $ docker-compose -f docker-compose-non-dev.yml up
7. SOURCES
   1. Apache Superset Tutorial
   2. What is Apache Superset?
   3. Exploring Data in Superset