This part felt manageable at first but the 'raw vs cleaned vs dimensions' framing tripped me up a bit.
Start by clarifying the requirements and data sources, then design a layered data model that separates raw events from cleaned facts and dimensions. Emphasize scalability, data quality, and how the model supports key analytics like call duration and user engagement.
Pro tip: Mention partitioning and clustering strategies for the fact table to handle Discord's scale, and discuss how to handle late-arriving or duplicate events from client-side logs.
Ask about the volume, latency, and specific analytics goals. Identify that client-side events may be unreliable and server-side events are authoritative.
Create separate raw tables for client and server events with minimal processing, including all relevant fields and ingestion metadata.
Build a fact table at the call-user granularity, aggregating metrics like duration, join count, and rejoin count, with deduplication and validation.
Create user and country dimension tables with surrogate keys and slowly changing dimension (SCD) handling for historical accuracy.
Discuss partitioning, indexing, and how to handle late data. Consider using a star schema for simplicity or snowflake for normalization.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Straightforward once the data model is settled.
Start by clarifying the schema and definitions: what tables contain call data, how country is associated with a call, and what constitutes a distinct video call. Then write a SQL query that groups by country and date, counting distinct call identifiers, and consider edge cases like time zones and nulls.
Pro tip: Mention that you'd confirm whether 'distinct video calls' means unique call IDs or unique participants per call, and discuss how time zone handling can affect daily counts—showing you think about data semantics, not just syntax.
Ask about the tables involved, how country is determined (e.g., user's country, call's country), and what defines a distinct video call (e.g., call_id). Confirm the date field and time zone.
Choose the table that logs video calls and filter for video call type if needed. Ensure you only include completed or valid calls as per business rules.
Use DATE_TRUNC or equivalent to extract the calendar day from the timestamp, then GROUP BY country and day.
Use COUNT(DISTINCT call_id) to get the number of unique video calls per group. Handle NULLs appropriately.
Check for edge cases like multiple countries per call, time zone conversions, and performance considerations (indexes, partitioning).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify the schema and definitions (e.g., video call event table, country field, date granularity), then propose a windowed aggregation using SQL window functions or a self-join. For each date and country, compute counts over the trailing 7, 14, and 30 days, handling missing dates and ensuring correct partitioning.
Pro tip: Mention that you would pre-aggregate daily counts per country before applying window functions to avoid performance issues, and discuss how to handle countries with no calls on a given date (e.g., using a date spine).
Ask about the table schema: what defines a video call, how country is determined, and whether dates are continuous. Confirm the exact trailing window definition (inclusive of current date).
Compute the number of video calls per country per day. This reduces data volume and simplifies window calculations.
Create a complete list of dates (and countries) to ensure all combinations are represented, even if no calls occurred. This avoids gaps in the output.
Use SQL window functions (e.g., SUM with ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) to compute trailing 7, 14, and 30-day sums, partitioned by country and ordered by date.
Check results for edge cases (e.g., first few dates) and discuss performance considerations like indexing or partitioning for large datasets.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.