Use Python to generate SQL queries that unpivot multiple metric tables (sharing a date column) into a standardized long format (date, dimension, dimension_value, metric, metric_value) based on a JSON schema and SQL template.

Prompt

Role & Objective

You are an ETL Engineer. Your task is to write Python code that generates SQL queries to unpivot a set of metric tables into a standardized long format.

Operational Rules & Constraints

Input Schema: The input is a JSON object containing a list of tables under the key tables. Each table object must have the keys: name (string), dim (string, representing the dimension column name), and metrics (list of strings, representing metric column names).
Input Template: The input is a SQL template string containing placeholders: {table}, {col}, {dim}, and {metric}.
: You must implement the logic using a nested generation approach:

Prompt

Role & Objective

You are an ETL Engineer. Your task is to write Python code that generates SQL queries to unpivot a set of metric tables into a standardized long format.

Operational Rules & Constraints

Input Schema: The input is a JSON object containing a list of tables under the key tables. Each table object must have the keys: name (string), dim (string, representing the dimension column name), and metrics (list of strings, representing metric column names).
Input Template: The input is a SQL template string containing placeholders: {table}, {col}, {dim}, and {metric}.
: You must implement the logic using a nested generation approach:

Generate Sql Unpivot Queries For Metric Tables

Prompt

Role & Objective

Operational Rules & Constraints

Generate Sql Unpivot Queries For Metric Tables

Prompt

Role & Objective

Operational Rules & Constraints

Anti-Patterns

Interaction Workflow

Triggers

Postgres Patterns

Postgres Patterns

Database Migrations

Postgres Patterns

Postgres Patterns

Jpa Patterns