Скачать или смотреть Finding Missing Values in BigQuery by Comparing Two Tables Over a Date Range

Discover how to effectively identify `missing values` from two BigQuery tables while comparing stores over a date range. Learn step-by-step with SQL queries!
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Understanding Missing Values in BigQuery

When working with data in BigQuery, it's common to encounter scenarios where you need to compare two tables and identify values that are present in one but missing in the other. A particular use case arises when you have two tables representing stores and their reporting dates, and you need to find which stores are missing from the first table on certain dates when compared to the second table. In this guide, we'll break down how to solve this problem using SQL in BigQuery.

The Problem: Identifying Missing Stores

Let's start by defining our tables:

Table 1: Reporting Stores

StoreReport_Date112021-03-03122021-03-03112021-04-14132021-04-14Table 2: Available Stores

Store111213Expected Outcome

Our goal is to list each missing store from Table 1 for each date based on the comparison with Table 2. The expected output for missing stores would look like this:

MissingStoreReport_Date132021-03-03122021-04-14The Challenge with SQL

An attempt to solve this problem using SQL resulted in an incomplete query that didn’t provide the expected Report_Date, showing a 'null' value instead. Let's take a look at the initial query that was tried:

[[See Video to Reveal this Text or Code Snippet]]

As you can see, the initial approach didn't yield the results we were hoping for, particularly because it did not correctly associate missing stores with their respective reporting dates.

The Solution: Using a Calendar Table Approach

To properly identify the missing values, we can leverage a combined approach using a CROSS JOIN to create a Cartesian product of the stores and report dates, subsequently filtering out the missing records. Here's how you can do it:

[[See Video to Reveal this Text or Code Snippet]]

Breakdown of the Query Steps

Distinct Stores and Dates: First, we select distinct stores and report dates from Table 1.

CROSS JOIN: We use a CROSS JOIN to generate a combination of all distinct stores with all report dates. This ensures that every store is matched with every date.

INNER JOIN: We then perform an INNER JOIN with Table 2 to filter out stores that are actually available.

LEFT JOIN: Next, we perform a LEFT JOIN back to Table 1 to see if there's a record for that store on that date.

WHERE Condition: Finally, we filter the results with a WHERE clause to show only those records where the store is missing (i.e., t1.Store IS NULL).

Conclusion

Using the above method, you can effectively identify missing values by comparing two tables in BigQuery over a given date range. This technique not only provides a clear understanding of which stores are missing on specific report dates but also showcases the importance of leveraging various SQL joins and constructs to manipulate and retrieve data effectively.

By implementing these strategies in your data queries, you will enhance your ability to deal with missing data and thereby improve the accuracy of your analyses. Happy querying!