Greetings! In this engaging project, we embark on an exploration of international debt statistics using SQL. Let's delve into the intricacies of my code:
- Laying the Foundation: Crafting Sample Data
We begin by establishing our virtual environment with the creation of the international_debt_table. Think of it as our canvas, ready to capture the nuances of global debt. To infuse realism, we inject life into our canvas by populating it with sample data, offering a glimpse into plausible international debt scenarios. 2. Organizing the Workspace: Introducing Temporary Tables
Say hello to temp_debt_data, our backstage area. This temporary table is meticulously curated to hold only the essential columns required for our in-depth analysis. 3. The Essentials: Unveiling Fundamental Queries
Query 1: Casts a spotlight on the number of countries grappling with debt (num_countries_with_debt). It's our initial exploration into the global landscape. Query 2: Summons the grand total of debt worldwide (total_debt_amount). It's about getting a panoramic view of the financial landscape. Query 3: Identifies the heavyweight – the country with the loftiest debt and its precise amount (highest_debt_amount). Query 4: Engages in a nuanced analysis, computing the average debt per country for distinct debt indicators (avg_debt_amount). Query 5: Decodes the pulse of the market, revealing the most prevalent debt indicator (num_occurrences). 4. Elevating Complexity: Unleashing Advanced SQL Techniques
Query 6: Orchestrates a symphony using Common Table Expressions (CTEs) to track cumulative debt for each country. Query 7: Raises the bar with SQL window functions, orchestrating a ranking of countries based on their debt (debt_rank). Query 8: Dives into the temporal dimension, extracting data based on specific dates. Query 9: Initiates a social discourse, uncovering countries with analogous debt levels through an intricate self-join. Query 10: Infuses a touch of artistry, utilizing regular expressions to discern patterns within debt indicators. Query 11: Celebrates diversity, employing set operators to identify distinctive debt indicators. Query 12: Simplifies the narrative by creating a view, debt_summary_view, offering a streamlined perspective. Query 13: Leverages the view to unveil the latest debt figures for each country. Query 14: Demonstrates respect for privacy, truncating data by rounding debt amounts. Query 15: Paints a vivid picture by labeling countries based on debt amounts using CASE statements. 5. Closing Act: Tidying Up the Stage
The curtains fall gracefully as we bid adieu to our temporary table, temp_debt_data. It served its purpose, and now it gracefully exits the scene.