Most Recent Orders | Advanced SQL Interview Questions | Data Engineer Interview Question | FAANG SQL

Published: 20 August 2024
on channel: Grow with Data

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Video 365: This is the 39th video of the SQL Interview Question series.

00:00 - Introduction to dataset and Question
04:00 - Approach 1: CTE (Common Table Expression) Approach
12:20 - Approach 2: Subquery Approach
13:00 - Approach 3: Correlated Subquery Approach
16:20 - Approach 4: Concatenated Values Approach
17:45 - Approach 5: Window Function Approach
22:40 - Conclusion

We are given three tables Customers, Orders and Products.

The Customers table has customer_id and Customer Name. Its kind of a lookup table
Same way, the Products table has product_id, name and the price of the product
Last one id the Orders table, which has order_id, order date, the customer who bought the product and the product id.

We are asked to write a solution to find the most recent order(s) of each product.

In this video, We’ll look at five distinct approaches to solving this problem, each leveraging different SQL techniques. Here's a breakdown of the methods covered:

** Approach 1: Using Common Table Expressions (CTE) **
In this approach, we utilize a CTE to pre-calculate the most recent order date for each product. This intermediate result is then joined with the original data to retrieve the relevant orders.

** Approach 2: Using Subquery Table **
This method involves embedding a subquery within the main query to dynamically generate the most recent order date for each product, followed by a join to get the full details.

** Approach 3: Using Correlated Subquery **
Here, we use a correlated subquery to directly compare each row with the maximum order date of the corresponding product, fetching the latest orders in a single step.

** Approach 4: Using Concatenated Values **
In this approach, we concatenate the product ID and order date to filter out the most recent orders, followed by a join with the products table to get the final result.

** Approach 5: Using Window Functions **
This advanced approach leverages the power of window functions to rank orders by their date within each product category. The most recent orders are then selected based on their rank.

Each approach has its unique benefits and scenarios where it might be preferred.

For a comprehensive understanding of these SQL methodologies and their application, please refer to this explanatory video.

code: https://github.com/jeganpillai/adv_sq...

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