name: bigquery-historical-data-aggregator description: Aggregates and analyzes historical data from multiple BigQuery tables with similar schemas. Queries multiple tables using UNION ALL, calculates aggregate metrics (averages, sums, counts), handles table discovery via INFORMATION_SCHEMA, and processes large datasets efficiently with batch queries.

Instructions

Core Workflow

1. Discover Tables: First, query INFORMATION_SCHEMA.TABLES to identify all available tables within the target dataset. This ensures the skill adapts to the actual table names present.
2. Aggregate Historical Data: Construct a SQL query that uses UNION ALL to combine data from all identified tables. Calculate the required aggregate metric (e.g., AVG(score)) grouped by the relevant key (e.g., student_id, name).
3. Handle Large Results: For datasets returning many rows (>50), use a batched querying strategy (e.g., LIMIT and OFFSET or filtering by key ranges) to retrieve the complete result set without truncation.
4. Join with Latest Data: Read the latest data from the provided source (e.g., a local CSV file). Perform a join between the aggregated historical data and the latest data to enable comparative analysis.
5. Calculate Deltas & Filter: Compute the percentage change or difference between historical and latest values. Apply the user-specified threshold filter (e.g., drop_percentage > 0.25).
6. Output Results: Write the filtered results to the specified output file (e.g., bad_student.csv).
7. Trigger Critical Actions: For records exceeding a higher, critical threshold (e.g., drop_percentage > 0.45), execute immediate actions such as writing critical log entries to a designated logging service.

Key Techniques

• Dynamic Table Inclusion: Use the list from INFORMATION_SCHEMA to build the UNION ALL query dynamically. Do not hardcode table names.
• Efficient Batch Retrieval: When the final aggregated list or intermediate results are large, retrieve data in manageable chunks using WHERE clauses on sequential keys or LIMIT/OFFSET.
• Precise Percentage Calculation: Ensure the percentage change formula is correct: (historical_value - latest_value) / historical_value.
• Logging for Notification: When writing critical logs, include all necessary identifiers (e.g., name, ID) and context so downstream systems can trigger alerts or notifications.

Error Handling & Validation

• Confirm the target dataset exists before querying.
• Verify that source files (e.g., CSV) exist and are readable.
• Validate that the log bucket or destination for critical alerts exists and is accessible.

Bundled Resources

• scripts/aggregate_query_template.sql: A parameterized SQL template for the core aggregation logic.
• references/schema_example.md: An example schema to illustrate the expected table structure.