In modern analytics, decision-makers don’t just want raw numbers—they want quick insights with context. A single KPI is useful, but combining it with a trend over time makes it far more powerful.
In this guide, we’ll walk through how to create a Big Number with Trend Line chart in Apache Superset, using a custom dataset.
Let's use following Website Traffic for the demo.
visit_date,page,traffic_source,visitors 2024-01-01,Home,Google,1200 2024-01-01,Home,Direct,800 2024-01-02,Product,Google,950 2024-01-02,Product,Facebook,600 2024-01-03,Checkout,Direct,400 2024-01-03,Checkout,Google,700 2024-01-04,Home,Facebook,500 2024-01-04,Product,Direct,650
Here:
· visit_date: Specifies the day when users visited your website
· page: The specific page on your website that the user visited
· traffic_source: Where the user came from before visiting your site
· visitors: Number of users who visited that page from that source on that date
Each row represents number of visitors for a specific combination of date + page + traffic source.
website-traffic.csv
visit_date,page,traffic_source,visitors 2026-05-04,Home,Google,1320 2026-05-04,Product,Direct,870 2026-05-04,Checkout,Facebook,430 2026-05-03,Home,Direct,910 2026-05-03,Product,Google,1020 2026-05-03,Checkout,Google,510 2026-05-02,Home,Facebook,780 2026-05-02,Product,Direct,640 2026-05-02,Checkout,Google,590 2026-05-01,Home,Google,1250 2026-05-01,Product,Facebook,720 2026-05-01,Checkout,Direct,410 2026-04-30,Home,Direct,890 2026-04-30,Product,Google,980 2026-04-30,Checkout,Facebook,460 2026-04-29,Home,Google,1340 2026-04-29,Product,Direct,760 2026-04-29,Checkout,Google,520 2026-04-28,Home,Facebook,800 2026-04-28,Product,Google,1010 2026-04-28,Checkout,Direct,390 2026-04-27,Home,Direct,870 2026-04-27,Product,Facebook,710 2026-04-27,Checkout,Google,540 2026-04-26,Home,Google,1280 2026-04-26,Product,Direct,690 2026-04-26,Checkout,Facebook,420 2026-04-25,Home,Direct,920 2026-04-25,Product,Google,990 2026-04-25,Checkout,Google,560 2026-04-24,Home,Facebook,760 2026-04-24,Product,Direct,650 2026-04-24,Checkout,Google,600 2026-04-23,Home,Google,1310 2026-04-23,Product,Facebook,740 2026-04-23,Checkout,Direct,400 2026-04-22,Home,Direct,880 2026-04-22,Product,Google,970 2026-04-22,Checkout,Facebook,450 2026-04-21,Home,Google,1290 2026-04-21,Product,Direct,720 2026-04-21,Checkout,Google,530 2026-04-20,Home,Facebook,790 2026-04-20,Product,Google,1000 2026-04-20,Checkout,Direct,380 2026-04-19,Home,Direct,860 2026-04-19,Product,Facebook,700 2026-04-19,Checkout,Google,550 2026-04-18,Home,Google,1270 2026-04-18,Product,Direct,680 2026-04-18,Checkout,Facebook,410 2026-04-17,Home,Direct,930 2026-04-17,Product,Google,995 2026-04-17,Checkout,Google,570 2026-04-16,Home,Facebook,770 2026-04-16,Product,Direct,660 2026-04-16,Checkout,Google,610 2026-04-15,Home,Google,1305 2026-04-15,Product,Facebook,735 2026-04-15,Checkout,Direct,405 2026-04-14,Home,Direct,875 2026-04-14,Product,Google,965 2026-04-14,Checkout,Facebook,455 2026-04-13,Home,Google,1335 2026-04-13,Product,Direct,745 2026-04-13,Checkout,Google,515 2026-04-12,Home,Facebook,805 2026-04-12,Product,Google,1005 2026-04-12,Checkout,Direct,395 2026-04-11,Home,Direct,865 2026-04-11,Product,Facebook,705 2026-04-11,Checkout,Google,545 2026-04-10,Home,Google,1265 2026-04-10,Product,Direct,675 2026-04-10,Checkout,Facebook,415 2026-04-09,Home,Direct,940 2026-04-09,Product,Google,985 2026-04-09,Checkout,Google,575 2026-04-08,Home,Facebook,775 2026-04-08,Product,Direct,665 2026-04-08,Checkout,Google,605 2026-04-07,Home,Google,1315 2026-04-07,Product,Facebook,725 2026-04-07,Checkout,Direct,420 2026-04-06,Home,Direct,885 2026-04-06,Product,Google,975 2026-04-06,Checkout,Facebook,465 2026-04-05,Home,Google,1325 2026-04-05,Product,Direct,755 2026-04-05,Checkout,Google,525
Follow below step-by-step procedure to design a Big Number with trend line chart.
Step 1: Upload website-traffic.csv to Superset.
Data -> Upload CSV to database
· Upload the file website-traffic.csv
· Choose the database and schema where you want to upload the csv file.
· Set the table name as website-traffic.
Expand Columns section, select all the columsn and add following data to Column Data types section
{
"visit_date": "datetime64[ns]",
"page": "string",
"traffic_source": "string",
"visitors": "int64"
}
Click on Upload button to create the dataset.
Navigate to Datasets listing page, you can see that website-traffic dataset is created and shown.
Step 2: Write Custom SQL Query.
We want:
· A time-series dataset
· Aggregated daily total visitors
· Clean structure for Big Number + Trend Line
Open SQL -> SQL Lab.
Select website-traffic table and paste following query in SQL Editor and run the query.
SELECT DATE(visit_date) AS traffic_date, SUM(visitors) AS total_visitors FROM 'website-traffic' WHERE DATE(visit_date) >= CURRENT_DATE - INTERVAL '30 days' GROUP BY traffic_date ORDER BY traffic_date;
You will see following result.
Click on Save -> Save dataset.
Give the dataset name as ‘website traffic count with trend line’ and click on ‘Save & Explore’ button.
Step 3: Create Chart.
You will be navigated to Chart Configuration screen.
Click on ‘View all charts’ link.
Select ‘Big Number with Trendline’.
Now we need to configure our Big Number with Trendline chart.
Select traffic_date for the X-axis to instruct Apache Superset to use this field for time-based analysis and trend calculations.
Next, define the metric to be displayed as the primary value. In the Metrics section, drag and drop the total_visitors column and keep the aggregation set to SUM so that the chart reflects the total visitor count.
Click on Save button, and click on ‘Update chart’ button, you can see the chart like below.
In Apache Superset, the Big Number with Trend Line:
· Big Number (2.62K), shows the latest value (based on the most recent date in your data)
· Trend Line → shows the historical trend over time
The key advantage of the big number with trend line chart is that it automatically adds a compact line chart to illustrate how your metric evolves over time.
As you configure the metric, you’ll see a prominent value representing your key performance indicator, along with a subtle trend line that highlights its historical movement.
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