Find out how to use Apache Superset to create data visualizations from a PostgreSQL table in an Aiven environment - and use it to drive your business decisions.
Francesco Tisiot
|RSS FeedHead of Developer Experience at Aiven
Subscribe for the latest news and insights on open source, Aiven offerings, and more.
"A picture is worth a thousand words" is a common saying in many languages. Similarly, in the data world, a visualization condenses huge amounts of raw points into an image that can be used to create insightful stories driving business decisions. Several tools exist in the dataviz ecosystem, with Apache Superset getting a lot of traction for its open source status and the advanced list of features it offers for data exploration, visualization and sharing.
This blog post shows how to start creating amazing visualizations with Apache Superset building on an existing PostgreSQL table.
Get your PostgreSQL free plan today
Ready to dive deeper into PostgreSQL? Sign up for a free PostgreSQL plan and follow along.Docker is a good friend here! It allows us to spin up - in a matter of minutes - complex software infrastructure without needing to understand and implement the setup manually. In cases like this when we're testing a new tool, it's a perfect fit!
We can start by cloning the Apache Superset GitHub repo, and navigating to the superset folder from our terminal:
Loading code...
Then we can use docker-compose to create all the containers needed for Apache Superset:
Loading code...
Boom! Our Apache Superset is ready with a nice set of pre-built content we can browse.
Now, let's go to localhost:8088 in our browser, and login with the safest duo ever (admin as user, and admin as password).
If you're curious, check out the Apache Superset docker page for more information.
We've explored happiness before on the blog, and how to push Kaggle's related dataset to PostgreSQL via Python. You can use the output mentioned there for the current example, or use your own. The end result in our example is a PostgreSQL instance named demo-pg, containing a table called happiness with content similar to the one below:
Loading code...
Apache Superset supports a wide range of source databases, and for PostgreSQL it uses the psycopg2 library. PostgreSQL support is already included in the default Docker setup.
All we need to do is go to the Apache Superset UI, and define a new datasource pointing to our PostgreSQL database:
demo-pg as DISPLAY NAME (or your own instance name).Connection looks good! message.demo-pg database definition.
Now, it's time to include the happiness table.
demo-pg, the SCHEMA with public and the table with - no surprises - happiness.Ta daaah!
Now we are ready to use our artistic skills, and create visualizations of our data: representing data this way makes it much easier to process.
public.happiness dataset we created before, and Pivot Table v2 as visualization.Let's use the dataset, and create a heatmap of the top 10 happiest countries over the years. We can do that with the following configuration:
country for the ROWSsurvey_yr for the COLUMNSoverall_rank column with AVG as aggregated function.overall_rank metrics being <= to 10.Average, this will drive the overall row/column aggregation in the pivot table.Check out the Query parameters overview:
Now, in the CUSTOMIZE tab set the following options:
Table Heatmapvalue ascendingCountry ranking heatmapWe're ready to press the RUN button above CUSTOMIZE.
Aaaaand, we end up with a lovely red shaded heatmap showing the countries that made the top 10 at least once in the previous 5 years, ordered by their overall average position.
To see where these countries are around the world, the same data could be used to create a map. Try the following settings:
country as COUNTRY COLUMNFull Name as COUNTRY FIELD TYPEscore column with AVG aggregation for the METRIC FOR COLOR parameterred/yellow/blue but the choice is yours. Go wild!The resulting map clearly indicates that a lot of work needs to be done to raise happiness levels in the Southern Hemisphere.
Sometimes the source dataset doesn't contain all the required fields. This is the case in our example too, with the survey_yr field containing only the last two digits of the year (ie. 18, 19), which means it won't be recognized by Apache Superset as a timestamp, stopping us from using any trend visualization.
Fear not! Apache Superset allows us to change the shape of our dataset, without any modification to the original table, by adding calculated columns.
2000+survey_yr as SQL EXPRESSIONYearDATETIME as DATA TYPE%Y as DATETIME FORMATNow the Year column is available, enabling us to create trend visualizations like a linechart of the top 5 positions over time.
Challenge yourself, and try creating this chart yourself as an Apache Superset.
As you'll see, Finland is a pretty happy place - and we think so too!
A dataset is only useful when stored properly, and made available for queries. It becomes meaningful when insights are discovered and communicated across the company. The combination of PostgreSQL and Apache Superset offers a best-in-class solution for data storage, discovery and visualization, enabling companies to be effective and data-driven.
Some more info:
--
Not using Aiven services yet? Sign up now for your free trial at https://console.aiven.io/signup!
In the meantime, make sure you follow our changelog and blog RSS feeds or our LinkedIn and Twitter accounts to stay up-to-date with product and feature-related news.
git clone https://github.com/apache/superset.git
cd supersetdocker-compose -f docker-compose-non-dev.yml upsurvey_yr | country | overall_rank | score | gdp | generosity | freedom | social_support | life_exp | gov_trust
----------+---------+--------------+---------+--------+------------+----------+----------------+----------+----------
19 | Finland | 1 | 7.7690 | 1.3400 | 0.1530 | 0.5960 | 1.587 | 0.9860 | 0.3930
19 | Denmark | 2 | 7.60 | 1.3830 | 0.2520 | 0.5920 | 1.573 | 0.9960 | 0.4100
19 | Norway | 3 | 7.554 | 1.4880 | 0.2710 | 0.6030 | 1.582 | 1.0280 | 0.3410