Understanding and Implementing a 7-Day Window in Big Query SQL

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As data analysts and scientists, we often encounter scenarios where we need to analyze data within a specific time window. In this article, we will explore how to implement a 7-day window in Big Query SQL, excluding the day of first open. We will break down the concept, provide example code, and discuss potential pitfalls and use cases.

What is a Time Window?

A time window refers to a specific period during which data is considered relevant for analysis or reporting. In this case, we want to analyze orders within a 7-day window after the first open date.

Understanding the Problem

The original query uses the following conditions:

and oh.Order_Date >= first_open_date.first_open
and oh.Order_Date <= first_open_date.first_open + 7

However, this approach has a flaw. It does not exclude the day of first open from the window, as required. To fix this, we need to adjust our query to only consider dates after the first open date.

Solution: Using Big Query’s Date Functions

One way to achieve this is by using Big Query’s DATE_SUB function to subtract 7 days from the current date and then using a LEFT JOIN to include orders with an open date before that. Here’s an example query:

SELECT 
  orders.*, 
  campaigns.campaign_name
FROM 
  orders_table AS orders 
  LEFT JOIN 
  (
    SELECT 
      DISTINCT timestamp AS open_date, 
      campaign_name 
    FROM 
      campaign_data 
  ) AS campaigns 
  ON 
  orders.user_id = campaigns.user_id 
  AND 
  campaigns.open_date < orders.order_date 
  AND 
  campaigns.open_date >= DATE_SUB(orders.order_date, INTERVAL 7 DAY)

In this query:

• We use DATE_SUB to subtract 7 days from the current date.
• We then join the orders_table with a subquery that returns distinct open dates and campaign names.
• We filter orders based on whether the open date is before the order date and within the 7-day window.

Using Window Functions

Another approach uses window functions like ROW_NUMBER() or DENSE_RANK(). These functions allow us to assign a ranking to each row within a partition of a result set. Here’s an example:

WITH ordered_data AS (
  SELECT 
    orders.order_date, 
    campaigns.campaign_name, 
    ROW_NUMBER() OVER (PARTITION BY orders.user_id ORDER BY orders.order_date) as row_num
  FROM 
    orders_table AS orders 
    LEFT JOIN 
    (
      SELECT DISTINCT timestamp AS open_date, campaign_name FROM campaign_data 
    ) AS campaigns ON orders.user_id = campaigns.user_id AND campaigns.open_date < orders.order_date
)
SELECT 
  order_date, 
  campaign_name, 
  row_num
FROM ordered_data
WHERE row_num <= 7

In this query:

• We use a Common Table Expression (CTE) to rank orders within each user’s data by order date.
• We then select only the top 7 rows for each user.

Handling Edge Cases

When working with time windows, it’s essential to consider edge cases. For example, what if there are no open dates for a particular user? In such scenarios, you may want to exclude those users from your analysis or provide special handling.

Additionally, be aware of potential issues like date arithmetic and daylight saving time (DST) changes.

Best Practices

When implementing time windows in Big Query SQL:

• Use DATE_SUB instead of manual date arithmetic.
• Avoid using hardcoded dates; instead, use the current date or a timestamp column.
• Consider using window functions to simplify your queries and improve performance.

Conclusion

In this article, we explored how to implement a 7-day window in Big Query SQL, excluding the day of first open. We provided example code, discussed potential pitfalls, and offered best practices for handling edge cases and improving performance. By following these guidelines and using the right tools, you can effectively analyze data within specific time windows and gain valuable insights from your data.

Last modified on 2023-11-28