Analyzing data trends with Amazon Redshift’s LEAD() function

AWS Redshift @

Data analysis often involves examining how values change over time or comparing current data points with subsequent ones. Amazon Redshift offers a powerful tool for such tasks – the LEAD() function. In this comprehensive article, we will explore the LEAD() function in detail, including its Data Definition Language (DDL) and Data Manipulation Language (DML) usage, with practical examples. We will also delve into real-world use cases to highlight the function’s significance.

Creating a sample table

Let’s begin by creating a sample table named freshers_in_webstats to demonstrate the usage of the LEAD() function.

CREATE TABLE freshers_in_webstats (
    date DATE,
    pageviews INT,
    visitors INT

The table consists of three columns: date, pageviews, and visitors, representing the date of the statistics, the number of pageviews, and the number of visitors, respectively.

Next, let’s insert some sample data into our freshers_in_webstats table for illustration:

INSERT INTO freshers_in_webstats (date, pageviews, visitors)
    ('2023-09-01', 1000, 500),
    ('2023-09-02', 1200, 600),
    ('2023-09-03', 1500, 750),
    ('2023-09-04', 800, 400),
    ('2023-09-05', 1300, 650);

Now, we have a dataset that spans five days of web statistics.

Understanding LEAD()

The LEAD() function is used to retrieve the value of a column from the next row within the result set. It can be particularly helpful when you need to create time series data or compare consecutive rows. The formula for LEAD() is as follows:

LEAD(column_name, offset, default_value) OVER (ORDER BY ordering_column)

column_name: The column whose value you want to retrieve from the next row.
offset: The number of rows forward to look for the next value.
default_value: An optional parameter specifying the value to return if there is no next row (default is NULL).
ordering_column: The column based on which the rows are ordered.

Real-world use case: Tracking web traffic trends

Let’s explore a real-world use case where the LEAD() function can provide valuable insights. Suppose you are tasked with analyzing the daily change in pageviews for “freshers_in” to identify trends in web traffic.

Here’s how you can use the LEAD() function to accomplish this:

    LEAD(pageviews, 1) OVER (ORDER BY date) AS next_day_pageviews

In this SQL query:

We select the date and pageviews columns.
We apply the LEAD() function to retrieve the pageviews value for the next day (offset of 1) based on the date column’s ordering.
We alias the result as next_day_pageviews.

Result set:

    date       | pageviews | next_day_pageviews
  2023-09-01  |   1000    |         1200
  2023-09-02  |   1200    |         1500
  2023-09-03  |   1500    |         800
  2023-09-04  |   800     |         1300
  2023-09-05  |   1300    |         NULL

The next_day_pageviews column now displays the pageviews for the following day. This information can be used to track daily changes in web traffic. For instance, you can identify days with significant increases or decreases in pageviews, allowing you to investigate the factors contributing to these trends.

Applications of LEAD()

The LEAD() function has several practical applications:

Time Series Analysis: It enables the creation of time series data for various analyses, including trend analysis and forecasting.
Comparative Analysis: You can use LEAD() to compare values between consecutive rows, making it useful for performance evaluation and anomaly detection.
Sequential Data Processing: In scenarios like log analysis, LEAD() helps analyze data sequentially to detect patterns or anomalies in logs.

Read more on Redshift
Read more on Hive
Read more on Snowflake

Author: user

Leave a Reply