Snowflake Lag Function and Moving Averages

This tutorials shows you how to use the lag function to calculate moving averages in Snowflake.

It builds upon work we shared in Snowflake SQL Aggregate Functions & Table Joins and Snowflake Window Functions: Partition By and Order By.

(This article is part of our Snowflake Guide. Use the right-hand menu to navigate.)

Using lag to calculate a moving average

We can use the lag() function to calculate a moving average. We use the moving average when we want to spot trends or to reduce the volatility from the daily number as it can vary widely.

In other words, it’s better to look at a week of sales versus one day to see how a product is performing.

Create sample data

To study this, first create these two tables.

CREATE TABLE customers
( 
customernumber     varchar(100) PRIMARY KEY, 
customername varchar(50),
phonenumber varchar(50),
postalcode varchar(50),
locale varchar(10),
datecreated date,
email varchar(50)
);
CREATE TABLE orders
( 
customernumber     varchar(100) PRIMARY KEY,
ordernumber varchar(100),
comments varchar(200),
orderdate date,
ordertype varchar(10),
shipdate date,
discount number,
quantity int,
productnumber varchar(50)
)

Then paste in this SQL data. The top of the data looks like this:

insert into customers (customernumber,customername,phonenumber,postalcode,locale,datecreated,email) values ('440','tiqthogsjwsedifisiir','3077854','vdew','','2020-09-27','');
insert into orders (customernumber,ordernumber,comments,orderdate,ordertype,shipdate,discount,quantity,productnumber) values ('440','402','swgstdhmju','2020-09-27','sale','2020-10-01','0.7005950240358919','61','BB111');
insert into customers (customernumber,customername,phonenumber,postalcode,locale,datecreated,email) values ('802','hrdngzutwelfhgwcyznt','1606845','rnmk','','2020-09-27','');
insert into orders (customernumber,ordernumber,comments,orderdate,ordertype,shipdate,discount,quantity,productnumber) values ('802','829','jybwzvoyzb','2020-09-27','sale','2020-10-06','0.3702248922841853','75','FF4444');
insert into customers (customernumber,customername,phonenumber,postalcode,locale,datecreated,email) values ('199','ogvaevvhhqtjcqggafnv','8452159','hyxm','','2020-09-27','');