The core challenge here is generating the month spine first and then joining usage rows correctly.
Start by clarifying the definitions of 'active paid theme usage', 'pirated usage', and 'pirating shop' to ensure alignment. Then, design a query that aggregates data monthly, joining usage and shop tables, and computes the piracy rate as pirated usages divided by total usages. Use a calendar table or generate months to ensure all months in 2023 are included, even if no data exists.
Pro tip: Mention that you would validate the query by checking edge cases, such as shops that switch from pirated to paid, and ensure the piracy rate is calculated correctly with appropriate denominators. Also, consider performance implications and suggest indexing on date and shop_id columns.
Define what constitutes an 'active paid theme usage', 'pirated usage', and a 'pirating shop'. Confirm whether a shop can have both paid and pirated usages in the same month and how to handle such cases.
Locate tables containing theme usage data, shop information, and piracy detection. Determine the date column to group by month and the shop identifier to count distinct pirating shops.
Write subqueries or CTEs to calculate total active paid usages, total pirated usages, and distinct count of pirating shops per month. Ensure you filter for the year 2023.
Calculate piracy rate as pirated usages divided by total usages (paid + pirated) per month. Use a calendar table or generate_series to include all months in 2023, filling missing data with zeros.
Check for data quality issues, such as duplicate records or missing months. Suggest indexing strategies and consider using window functions if needed for cumulative metrics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The deduplication warning in the problem statement is doing a lot of work here.
Start by clarifying the definitions of 'pirating shops' and 'potential theme revenue lost' to ensure alignment with the interviewer. Then, outline the necessary tables and joins, and build the query incrementally, using CTEs for clarity. Finally, compute the required metrics and present the results in a monthly aggregated format.
Pro tip: Mention that you would validate the definition of 'pirating shops' with stakeholders and consider edge cases like refunds or chargebacks that might affect revenue calculations.
Ask clarifying questions to define 'pirating shops', 'potential theme revenue lost', and 'platform revenue'. Confirm the time period and whether revenue is based on gross or net amounts.
Determine which tables contain shop information, theme purchases, and revenue data. Identify fields like shop_id, theme_id, revenue_amount, transaction_date, and a flag for pirating shops.
Plan to use CTEs to calculate monthly aggregates: total platform revenue, revenue from pirating shops, and potential theme revenue lost. Consider using LEFT JOINs to include all shops and filter appropriately.
Write the SQL query with proper grouping by month, calculating each metric. Use window functions or subqueries for percentages. Ensure performance by filtering early and using indexes.
Check for data quality issues, such as missing months or null values. Present the final table with clear column names and explain how each metric was derived.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one is where the deduplication problem gets gnarly.
Start by clarifying the business context and defining the key metrics: potential revenue lost, piracy counts, and platform revenue. Then outline a SQL query that aggregates data at the theme, plan, and vertical level, calculates the metrics, and ranks combinations by potential revenue lost to return the top 10.
Pro tip: Demonstrate awareness of data quality and business nuances: mention that piracy counts might be underreported and that potential revenue lost should be estimated using a defensible method, such as comparing actual revenue to expected revenue based on plan pricing and usage.
Ask clarifying questions to understand the data sources, definitions of 'piracy counts' and 'potential revenue lost', and the time frame. Define how each metric is calculated.
Determine which tables contain theme, subscription plan, merchant vertical, piracy events, and revenue data. Plan necessary joins and filters.
Write SQL to group by theme, plan, and vertical, then compute SUM of piracy counts, SUM of platform revenue, and an estimate of potential revenue lost (e.g., based on expected revenue minus actual).
Order the aggregated results by potential revenue lost descending and limit to the top 10 combinations.
Sanity-check the results, consider edge cases (e.g., nulls, outliers), and explain how the query informs prioritization.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.