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.

Navigate the Apache Superset GUI to build dashboards, arrange charts, apply filters, and add context with text boxes, while exploring data sources and SQL Lab.

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.

Introduce base tables for the course and demonstrate running the SQL script to create product, customer, and orders tables in PostgreSQL, then insert records for dashboard use.

Download and install PostgreSQL on macOS using the interactive installer from postgresql.org, configure the superuser password, and set up Pgadmin four to connect to the server.

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.

Create base tables including product, customer, and orders using a provided sql script; execute, insert records, and verify row counts to understand table creation and data population.

Master sql table creation and data insertion by building an employee table, defining columns, inserting single and multiple records, and counting rows to verify results.

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.

Experiment with mathematical expressions in select queries, performing addition, multiplication, modulus, and power on static values and table columns. Observe the computed results across the dataset.

Learn how the distinct keyword removes duplicates to yield unique category values in SQL, via select distinct category from product. See how count(distinct category) measures unique records.

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 use SQL aliases to rename columns and tables with the as keyword, apply aliases to multiple columns, and even alias constant values, while understanding common errors.

Learn how to filter data with the where clause in SQL, using and/or to combine stock and price conditions, and apply greater-than and less-than filters to retrieve targeted results.

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.

Filter date columns with the where clause in SQL to retrieve orders by order date, using equals, not equals, and, or, greater than, and less than.

Learn to filter by multiple values using the in clause on a single column, for numbers and strings, with examples from a product table and stock values 15 and 0.

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.

Master inner queries and subqueries to dynamically filter data, retrieve a laptop's price, and use that result in a parent query with in and distinct for sold products from orders.

Explore inner queries and the any and all operators to write expressive subqueries, filter orders by electronic products, and optimize comparisons with practical examples and best practices.

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.

Explore how the SQL group by clause groups rows by category to compute aggregates like max price and sum of stock per group.

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 filter and sort data with where, order by, and group by on a product table. Sort by product name and compute total stock per category.

Explore Apache Superset's SQL Lab to write custom queries, analyze results, and create visualizations in a single data exploration environment.

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.

Explore custom datasets in Apache Superset using sql lab to write and save queries as persistent datasets. Create charts and dashboards from these datasets, ensuring visuals reflect the latest data.

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.

Upload excel data to Apache Superset and configure its database, schema, table, and sheet. Define columns, header rows, and null handling to enable charts and dashboards.

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

Explore the charts GUI in Apache Superset, learning category-based visualizations from correlations to maps and pivot tables, and how to select chart types for impactful dashboards.

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.

Create a Sankey chart in Apache Superset using SQL Lab to visualize flows from product categories to customers by joining products, orders, and customers, with count as the metric.

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.

Create custom metrics in Apache Superset using the Sample Superstore Orders data set, defining SQL expressions for average sales and max sales, then reuse them across charts and dashboards.

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 a word count chart in Apache Superset using the Sample Superstore data to visualize city names by sales in a readable word cloud.

Create a funnel chart from the products data set to visualize category values using category as the dimension and sum of price as the metric.

Create a waterfall chart from share market data to visualize cumulative net changes over time, as you upload data, create a dataset, and configure the chart in Apache Superset.

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.

Learn to build a world map in Apache Superset with population data, uploading a CSV, configuring country fields, using 2022 population, and enabling bubbles with color cues.

Learn to build a tree chart in Apache Superset to visualize hierarchical employee data, mapping managers to subordinates with root node setup, customization, and clear organizational structure.

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.

Create a scatter plot in Apache Superset to analyze stock quantity versus price using the product dataset, configuring x as stock quantity, y as sum of price, with axis titles.

Learn how to build a filtered table visualization in Apache Superset to display detailed product information, selecting columns like product ID, name, category, price, and inventory, with drag-and-drop configuration.

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 like filters on a table visualization in apache superset by dragging the name field into the filter section, setting starts with or ends with, then save and update chart.

Apply in-clause and not-in filters to a table visualization in Apache Superset, using drag-and-drop fields to filter products by name and price and 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.

Learn to build a product table in Apache Superset, apply custom number formatting, currency prefixes, and column alignment to create readable, interactive dashboards.