This video demonstrates how to calculate daily sales trend from an e-commerce database using SQL. It covers joining orders and order_items tables, filtering delivered orders, grouping by order date, and summing revenue. The tutorial also shows sorting results by date for accurate chronological order.
A short editorial from the VEONIB team on why this content matters.
This tutorial simplifies daily sales trend calculation in SQL, focusing on joins and aggregation for e-commerce data.
It stands out by contrasting daily vs monthly trends, emphasizing the ease of grouping by date without extraction, which is a common interview question.
Data analyst aspirants should practice this query on sample data and explore variations like adding filters or calculating moving averages.
Combines rows from two tables based on a related column, like order_id.
Groups rows that have the same values in specified columns to aggregate data.
Calculates the total sum of a numeric column, often used with GROUP BY.
Sorts the result set in ascending or descending order based on one or more columns.
In e-commerce, typically computed as quantity multiplied by unit price.
Filters records based on specified conditions before grouping or aggregation.
How to calculate daily sales trend in SQL?
Join orders and order_items tables on order_id, filter for delivered orders, group by order_date, and sum quantity*unit_price as revenue.
What tables are needed for daily sales trend in e-commerce?
Typically orders and order_items tables, where orders contain order date and status, and order_items contain quantity and unit price.
Why use GROUP BY on order_date?
To aggregate sales data for each day, allowing calculation of total revenue per day.
How to ensure only completed orders are included?
Add a WHERE clause filtering order_status = 'delivered'.
How to sort daily sales data chronologically?
Use ORDER BY order_date (or the date column) to sort results in ascending order.
What is the difference between daily and monthly sales trend queries?
Monthly trend requires extracting year and month from the date, while daily trend simply groups by the full date.
How to calculate revenue from order_items?
Multiply quantity by unit_price for each item and sum the results using SUM(quantity * unit_price).
Why is it important to use aliases in SQL joins?
Aliases make the query readable and avoid ambiguity when columns have the same name in different tables.