November Data Challenge: Optimizing SQL Queries for Year-End Reports

Are you ready to dive into this month’s exciting Data Engineering / Analytics Challenge!?

As we approach the end of the year, data engineers are often tasked with preparing large-scale reports that provide insights on business performance. These reports can involve complex queries, requiring efficiency to avoid long processing times. This month’s code challenge focuses on optimizing SQL queries to handle high data volumes effectively.

Challenge Overview

In this challenge, you’ll be given a dataset of transaction records containing approximately 1 million entries. Your task is to create SQL queries that extract valuable insights while optimizing for performance. Think of it as preparing year-end reports without overwhelming your database!

Dataset Description

The dataset contains the following fields:

• TransactionID: A unique identifier for each transaction.

• CustomerID: ID of the customer making the transaction.

• ProductID: ID of the product purchased.

• TransactionDate: Date of the transaction (within the last two years).

• Quantity: Number of items purchased in the transaction.

• UnitPrice: Price per unit of the product.

Download the dataset here.

Challenge Tasks

1. Total Sales per Month
   Write a query to calculate the total sales for each month over the past two years. Consider how to minimize the query’s runtime as the data scales.

2. Top 10 Customers by Total Spend
   Identify the top 10 customers based on total spending. Remember to account for both Quantity and UnitPrice when calculating total spend. Aim to make the query efficient, especially if this needs to run regularly.

3. Product Performance Analysis
   Determine which products generated the highest revenue and how frequently they were sold. Your query should provide insights into which products are the top performers.

4. Customer Purchase Frequency
   Find the frequency of purchases for each customer and identify any customers with a high transaction frequency. This can help identify loyal customers. Optimize this query for faster response times, especially for cases where customer behavior might be analyzed frequently.

Optimization Tips

• Indexing: Think about which columns might benefit from indexing to speed up search and aggregation.

• Subqueries vs. Joins: Consider where subqueries may help streamline your solution or whether joins will make it more efficient.

• Date Filtering: Use date filters effectively to limit the dataset scope when analyzing time-based queries.

• Avoiding Full Scans: Explore ways to avoid scanning the full table where possible, especially for large datasets.

How to Participate

1. Download the dataset and use your preferred SQL environment.

2. Optimize your queries for each task above.

3. Share your optimized queries with the community, explaining the thought process behind your performance improvements.

This challenge is a great opportunity to test your SQL skills and prepare for real-world scenarios where query efficiency can make a significant impact. Good luck, and we look forward to seeing your optimized solutions!

We are Mid November already! I know that the holidays get hectic and busy so this month’s challenge will be due 12/29 to allow extra time!