Apache Superset for Data Engineers (Hands On)

Explore installing Apache Superset on a Linux machine with hands-on reinstallation and practical activities. Windows installation is not available in current docs, while Windows-related steps appear in the next video.

This hands-on guide walks you through installing Apache Superset on Ubuntu 20.04, setting up a virtual environment, installing dependencies, initializing the database, and launching the web user interface.

Install apache superset on ubuntu 24.04 with python 3.10, setting up dependencies, a virtual environment, upgrading the database, creating an admin, loading examples, and running the server.

install Docker Desktop on Windows, pull the Apache Superset image with the c7c30f dev tag, run the container on port 8080, and create an admin.

Learn to run Apache Superset on Windows with Docker Compose, from cloning the repo to building and starting containers. Access the dashboard at localhost:8088 and log in with admin/admin.

Learn hands-on steps to start and stop Apache Superset on Ubuntu 20.04, including installation, booting a virtual environment, and logging in.

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Explore the databases supported by Apache Superset, focusing on the listed RDBMS options and how the support list grows with upgrades. Next section covers connecting a few RDBMS.

Connect to a MySQL database from Apache Superset, install necessary drivers, create a connection string, and explore initial data visualization steps.

Connect Apache Superset to a MySQL database, configure the connection string, and view data using the secret connection in a practical, hands-on demonstration.

Upload a csv file into Apache Superset, configure database access, set delimiter and header options, map columns like name, last name, address, and city, then visualize the data.

Visualize an uploaded CSV in Apache Superset by creating a graph, selecting the city column, and aggregating data to display city-level insights from addresses.

Learn a hands-on workflow to connect Apache Drill to Apache Superset, query a local database, and explore visualizations with charts in the browser.

Learn to connect Hive with Apache Superset, create and access a Hive database, and view data using the SQL editor in a hands-on session.

Pull and run the official Postgres image in Docker Desktop, configure a container, and connect to Postgres to create a table, insert data, and query cars.

Learn to connect a PostgreSQL database to Apache Superset and run SQL queries in SQL Lab, using a Postgres container to explore a cars table with brands and years.

Select the right chart by matching data type and message, and use line, area, bar, column, pie, histogram, box plot, and scatter plots to reveal trends and distribution.

Learn to build a line chart in Apache Superset with the video game sales dataset, using a time-based x axis, year filters, and a genre breakdown on a dashboard.

Learn to build a line chart in Apache Superset by setting the x axis to time, then group by and view the sql for a dashboard titled my life chart.

learn to create a big number with a trend line chart in Apache Superset by selecting the slack threads dataset, configuring time series weekly, and applying a week-over-week filter.

Explore building a trend line chart in Apache Superset to compare new member counts month over month, using last year's July–November data and axis settings.

Explore table chart creation in Apache Superset for data engineers, from selecting the FCC 2018 survey dataset to filtering null values, limiting to 15 rows, and sorting for dashboard integration.

Build a chart by selecting data from a 2018 survey, adjust data, aggregate by language, filter out non-applicable values, and save and publish the chart.

Create a legacy bar chart from the video game sales dataset, adding regional metrics and global sales. Save it to the dashboard and explore stacking options and January sales.

Learn to create a bar chart in Apache Superset using video game sales data, configure the x axis and regional sums, and use stacked descending bars saved to a dashboard.

Master building a bar chart in Apache Superset by selecting data, adjusting columns, sorting in descending order, applying calculations, and generating various visualizations for different metrics.

Create an area chart in Apache Superset from video game sales, with time on the x axis and global sales on the y axis, and save to a dashboard.

Explore area chart visualizations in Apache Superset for data engineers by building an interactive area chart of video game sales, analyzing the rise and fall of consoles through dataset exploration.

Create a time series bar chart in Apache Superset by grouping data by product line, visualizing quarterly reports in a new job, and addressing axis naming and data quality issues.

Create a deck.gl polygon chart for the San Francisco population, using the condor coordinates and polygon-encoded json. Compute density as population divided by area and save in ten k format.

Explore creating a deck.gl polygon visualization by selecting longitude and latitude, building a population-based matrix, and generating a graph to explore spatial relationships.

Create a pie chart from the gender dimension, switch to donut format, save it as my pie chart, and add it to your dashboard in Apache Superset.

Build and customize a pie chart in Apache Superset through a hands-on exercise, grouping data by gender and tuning color schemes for clear visualization.

Create a time series table in Apache Superset using the flight dataset to analyze departure delays by city in the USA, with a spark line trend on the dashboard.

Create a time series table by grouping by city, filtering on departure delays, and computing time lag and day-to-day delay percentages.

Learn to build a pivot table and chart in Apache Superset by grouping rows by product line, summing vehicle sales, and embedding the result on a dashboard.

Practice building a pivot chart in Apache Superset by creating a chart, applying group by source, and configuring values, sums, and sorting options to reveal trends.

Create a histogram chart from FCC 2018 survey using the expected earn column, apply an aspiring developer filter, and save to the dashboard after increasing bins from five to ten.

Explore generating a histogram from the FCC 2018 survey data by selecting a column, adjusting timing, and inspecting the resulting figure to understand data distribution.

Learn to create a big number chart in Apache Superset using the FCC 2018 survey dataset, apply filters to adjust counts, and save the chart to a dashboard.

Create a big number visualization in Apache Superset by selecting a chart, filtering records, saving the entry, and adding sales figures to the dashboard.

Create a deck.gl scatterplot in Apache Superset by selecting sample data, configuring latitude and longitude columns, and visualizing points on a light map of San Francisco.

Learn how to build a deck.gl scatterplot in Apache Superset by selecting the longitude and latitude fields from a dataset, creating a new type, and rendering a geographic map.

Create deck.gl arcs in Apache Superset by selecting the flights dataset, configuring start and end longitude/latitude, adjusting the viewport, and adding the chart to a dashboard.

Create a deck.gl arc visualization in Apache Superset using the flight dataset, mapping longitude and latitude to destination coordinates, and configuring the chart with no filters.

Create a heatmap from video game sales dataset by using platform on the x axis and January on the y axis, with count(*) as the metric, then save to dashboard.

Generate a heat map by creating a new chart and selecting video games as the dataset. Remove time filter, set the x-axis to platform, and adjust colors to show counts.

Create a deck.gl grid chart in Apache Superset by mapping longitude and latitude from geo data, using count as height, visualizing on a satellite street map, and saving to dashboard.

Unlock hands-on mastery of deck.gl grid visualizations in Apache Superset by mapping longitude and latitude, configuring columns, and exploring satellite clip grid blocks up to 5000 records.

Create a dual line chart in apache superset, focusing on a colorless visualization, adding an average line, and saving the finished chart.

Create and customize a deck.gl screen grid chart in Apache Superset by mapping longitude and latitude, ignoring null locations, then save the chart to a dashboard for map visualization.

Explore deck.gl screen grid in Apache Superset by creating a chart that uses longitude and latitude to visualize spatial data.

Create and customize a tree map using the video game sales data, including setting the matrix, filtering, saving the chart, and adding it to a dashboard.

Analyze video game sales data using the gender column, apply group by, and explore color schemes to support gender-friendly analysis.

Create a box plot in Apache Superset using the FCC 2018 survey dataset. Filter for software development and last year income ≤ 200k; view mean, median, max, min.

Explore box plots in Apache Superset by building a chart from the FCC 2018 survey dataset, applying filters for software developers and income by education to reveal distribution patterns.

Create a sunburst chart in Apache Superset by adding region and country hierarchies, setting population total and rural metrics, applying a 2011 data filter, and adding chart to a dashboard.

Explore creating a sunburst chart in Apache Superset, using product categories and clinical stages, with an empty time column and no filter, in a hands-on data visualization workflow.

Learn to build a sankey diagram in Apache Superset using the FCC 2018 survey, with filters for software development and commute time, and save it to a dashboard.

Develop hands-on with the old sankey diagram by building and refining charts using filters, selecting time frames, and focusing on source and target columns to visualize community and regional data.

Create a word cloud chart from video game sales, using the word cloud type and default metric count; save it to the dashboard and explore by January or platform.

Create a word cloud from name data, apply a gender filter, and view the resulting visualization. Explore building charts in Apache Superset from name data.

Create a Mapbox chart with sample geo data by selecting longitude and latitude columns and add it to your dashboard, then update the chart and switch to satellite street view.

Learn to use MapBox in Apache Superset by building charts with longitude and latitude, fetching and uploading records, and visualizing data on maps.

Build a calendar heatmap in a hands-on session using Apache Superset for data engineers, generating calendar visualizations by counts across months and years.

Create a nightingale rose chart from the video game sales data, remove the time column, use the f(x) count star matrix, and save the chart to a dashboard.

Create a Nightingale Rose Chart in Apache Superset, add columns and grouping, and generate a visual of time and popularity to analyze sector totals.

Create a latest bubble chart in Apache Superset using video game sales data, with x axis as global sales, y axis as other sales, and bubble size by count.

Learn to build a bubble chart in Apache Superset in this hands-on session, using x and y axes and bubble sizes to visualize city-level data and growth.

Learn to build a horizontal chart in Apache Superset, using sales data and production line grouping, and visualize vehicle categories such as cars, motorcycles, planes, ships, and trucks.

Create a deck.gl path chart in Superset from the San Francisco BART lines dataset, convert longitude–latitude json into a path, map the data, and save the chart to a dashboard.

Build and customize a deck.gl path visualization in Apache Superset using longitude and latitude data, adjust line length, and save charts for scalable map insights.

Explore generating a graph chart in Apache Superset using a card with three columns: source, target, and value. Use an aggregator to analyze growth.

Create a world map chart in Apache Superset using the FCC 2018 Survey World Map dataset, mapping country and country live counts and color by country.

Explore a hands-on world map visualization of software developers by country, with counts like Canada 106 and India 945, using color-based representations and fields such as fullName and country.

Explore building a country map visualization in Apache Superset, mapping data by region and state, selecting countries, and displaying regional representations and averages.

Create a pivot table in Apache Superset, building a new chart with names, states, and counts of unique values. Sort by rows and columns to analyze changes.

Begin a hands-on HR analytics dashboard project in Apache Superset, exploring local versus cloud setup, logging in with Gmail, and initial design steps in a guided environment.

Develop an HR analytics dashboard in Apache Superset, generating graphs for attrition, age distribution by gender, job satisfaction, and income by gender; include department counts and average income by department.

Learn to build an HR analytics dashboard by connecting Google Sheets data, creating a database, and running SQL queries on employee attrition datasets for a practical data engineering workflow.

Build and customize an HR analytics dashboard in Apache Superset by creating charts, such as a gender distribution and an attrition by group chart, and save the first dashboard.

Build an hr analytics dashboard by creating charts for job satisfaction across industries and police, then add and save a monthly income by gender chart.

Create charts in Apache Superset to analyze HR analytics: show monthly income by gender, average salary by gender, and employed by department, with gender breakdowns and generation-based insights.

Part 6 of the hr analytics dashboard project builds an apache superset visualization of education level and degree types, attrition, department breakdowns, and income, using pie charts and graphs.

Builds an HR analytics dashboard in Apache Superset by creating three charts, including a pie chart for job satisfaction, median salary by job, and an attrition breakdown.

Develop HR analytics dashboards by adding charts for job growth, attrition, and job satisfaction, using a current manager and environmental satisfaction matrix to compare averages.

Use Apache Superset to build an HR analytics dashboard, charting work-life balance by department and analyzing attrition and business travel trends.

Build and customize an HR analytics dashboard in Apache Superset by creating charts for attrition by distance from home, grouping by job role, and applying color and filter options.

Build an Olympic analytics dashboard in Apache Superset using a free online instance, log in, access workspace, and name the dashboard olympic analytics.

Create a quick analytics dashboard to visualize Olympic medals by country with a world map and analyze age distribution and gender trends in gold medalists over time.

Upload the lead_events file (not CSV) to Google Sheets, then connect the sheet as a database in Superset and import the data into the Olympic Analytics Dashboard.

Load the Olympic dataset into Apache Superset in a preset cloud environment, use the SQL editor to select the Olympic table, and verify data availability and medal data.

Create charts from Olympic dataset by selecting medals and country, add a Google Sheet dataset, and save charts to the Olympic analytics dashboard showing gold, silver, and bronze by country.

Build and customize charts in Apache Superset using the Olympic dataset to visualize gold medals by age, athletes over 50 by sport, and female medals in summer games.

Build an olympic analytics dashboard in Apache Superset, featuring a bar chart of top five gold medal countries and a table of USA golds by event.

Create and customize bar charts of male and female athletes' participation over time in the Olympics using filters for sex and season, adjust sorting, and add to the analytics dashboard.

Visualize how the age of Olympic athletes varies over time by building bar charts for minimum, maximum, and average ages, filtered by sex and labeled by year.

Build charts showing variation of weight for male and female Olympic athletes over time, computing minimum, maximum, and average weights by year, and save the analytics dashboard with color customization.

Create and customize charts to show height variation for male and female athletes over time, then save and add these charts to the Olympic analytics dashboard for quick insights.