Apache Superset Masterclass: Build Modern BI Dashboards

Learn to install Apache Superset using Python by setting a secret key, creating a virtual environment, installing dependencies, configuring metadata, and launching the development server to access the dashboard.

Explore the architecture of Apache Superset, from a Python backend using Flask to a React frontend, with SQLAlchemy, caching, and a message broker enabling RESTful communication and scalable dashboards.

Connect Apache Superset to a Duckdb database by configuring the sql alchemy api connection, installing the duckdb library, and saving the new database connection for visualizations.

Connect Apache Superset to a MySQL database to visualize data by creating a database connection, installing the MySQL client library with pip if needed, and configuring hostname, port, and credentials.

Learn how to use PgAdmin to manage PostgreSQL databases, create databases and schemas with SQL, and run queries, and understand that drop commands cannot be undone.

master creating and dropping tables in sql by building an employee table with an int primary key, var char name, and numeric phone, then verify via table refresh.

Learn how databases store data and extract specific columns with sql, using PostgreSQL and pgAdmin; practice selecting product ID, price, stock, and using select star from product.

Experiment with select without tables to learn that queries can return literal values, perform arithmetic, and fetch the current date using now, illustrating SQL evaluation without a table.

Explore the distinct keyword through experiments with category and product name in the Apache Superset Masterclass, compare single and multi-column distinct, and count distinct records to reveal unique results.

Explore sql upper and lower functions to format text, standardize user input, and enable case-insensitive searches in reports and dashboards, demonstrated with product name examples.

Learn to filter string columns with the where clause in SQL, using equals, not equals, and combining product name and category with and/or, such as electronics.

Learn how to create the employees table, insert data, and cast a salary stored as a string to float or integer using cast, enabling accurate arithmetic in SQL.

Learn to filter string columns with the where clause using the like operator and wildcards, matching starts, ends, and in-between letters in product name queries.

Explore how default values work in a SQL table by creating an employees table, inserting records, and using 'unknown' when names are omitted.

Explore the union all operator in SQL, which combines results from two tables, preserves duplicates, and shows how ids and names from employees and updated employees appear together.

Learn to use the where clause with group by to filter groups by conditions such as price greater than 200 and is active.

Learn how the order by clause sorts data in SQL, using ASC and DESC, sorting by product name and price, including multi-column sorting.

Learn to create, configure, and manage data sets in Apache Superset, the layer between your database and visualizations, by selecting a database connection, schema, and table to build dashboards.

Enable file upload in settings, upload CSV to create datasets for charts and dashboards, then use for ad hoc analysis; for production, connect to a proper database.

Explore how Apache Superset's charting architecture connects the data, visualization, and interaction layers to fetch, transform, and render charts with Echarts and D3.

Learn to build a big number chart in Apache Superset that shows total count of products from products data set, using count function, adding a subheader, and saving to dashboards.

Learn to build a big number visualization in Apache Superset that counts only active products using a status filter, building on earlier total products and table grid tutorials.

Learn how to build a bar chart in Apache Superset that counts products by category, configure x axis and y axis, apply sort by count, and limit results for readability.

Customize bar charts in Apache Superset by switching orientation, adding titles, adjusting axis margins, positioning axis titles, and applying color schemes for cohesive dashboards.

Create a line chart in Apache Superset to visualize daily profit over time, using the orders data set with order date on x-axis and sum of profit on the y-axis.

Build a pie chart in Apache Superset using predefined product data sets to visualize category distribution with a count distinct metric, and customize labels and tooltips before saving.

Master big number with trend line visualizations in Apache Superset by building time series KPI dashboards with SQL Lab, selecting sales data, configuring daily aggregation, and period-over-period comparisons.

Learn to build a multi-line time series chart in Apache Superset using SQL Lab to compare sales performance across multiple products over time.

Create a pivot table in Apache Superset with the Superstore data to analyze sales across categories and segments using totals and conditional formatting.

Create an area chart in Apache Superset for sales and profit trends. Use Superstore data set with order date as the x-axis and show sales and profit as filled areas.

Create a histogram chart in Apache Superset to analyze the distribution of product prices, using a saved dataset and configurable bins, normalization, and cumulative options.

Apply integer filters to a table visualization in Apache Superset, using an interactive grid to filter stock values with equals and other conditions, then update the chart.

Apply integer filters on a table visualization to show records where price is between 200 and 500, using greater than, less than, and the between condition in Apache Superset.

Apply conditional formatting in Apache Superset to highlight discount amounts over 100 using the customize tab, selecting the column and alert color scheme for clearer, more readable charts.