How to Use RANK to Group Matched Records in SQL

Learn how to effectively use the `RANK` function in SQL to group duplicate records identified by address. This guide includes a step-by-step solution and sample code.
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How to Use RANK to Group Matched Records in SQL

Working with large datasets often comes with challenges, especially when trying to identify and manage duplicate records. A common scenario is identifying duplicate addresses within a list of records. In this guide, we will explore how to utilize the RANK function in SQL to group matched records effectively.

The Challenge

Suppose you have a dataset full of suppliers, and you want to identify duplicates based on their address. You might have multiple variations of the same address spread across various fields, making it tricky to identify these duplicates straightforwardly. This can complicate data management and clean-up efforts. In our case, we had traditional columns like [Address] and [Remit_Address] to work with.

The Goal

Your ultimate aim might be to group matched records together based on the identified duplicates, ensuring the results are easily readable and organized. However, achieving this with standard SQL queries can lead to repeated entries, as sorting by existing fields will not adequately display these duplicates side by side.

The Solution

To efficiently group matched records, we can leverage the RANK function in SQL. Here’s a step-by-step breakdown of how to do this:

1. Setting Up the Data

First, let’s establish your sample data which includes the relevant tables:

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2. Using Common Table Expressions (CTEs)

To simplify our query, we will use a Common Table Expression (CTE) that joins on the cleaned address:

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

3. Understanding the Query

CTE Setup: The CTE named cte performs an inner join between our sample_data and dupe_addresses using an OR condition to ensure that both cleaned address variants are considered.

RANK Function: The RANK() function assigns a rank to each row within the partition defined by the Supplier_No. This helps us to organize our records effectively and identify duplicates.

Final Filter: The outer select filters for only the first rank (WHERE rnk = 1), which means you'll see only one of the matching records grouped together, eliminating duplicates.

4. Desired Output

Executing the above query will yield a results table grouping matching records by address, ensuring readability and organization:

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

Conclusion

Using the RANK function together with a CTE provides a powerful way to group matched records without the clutter of duplicates. By understanding partitioning and ranking, you can streamline your SQL queries to manage addresses or any set of repeated records efficiently.

Now, armed with this knowledge, you can dive into your datasets confidently, ensuring that your data remains organized and duplicates are adequately handled!

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