Here’s a clean, single-copy version without code blocks or formatting boxes so you can easily copy it all at once:
Excellent question — this is a very relevant problem in data engineering pipelines following the medallion architecture (Bronze → Silver → Gold).
Let’s go through the best practices for using YAML + Python to define and generate table schemas and views for each layer (Bronze, Silver, Gold).
- Core Concept
- Instead of hardcoding SQL or schema definitions, you maintain YAML files that describe:
- Table name and layer (bronze/silver/gold)
- Columns and types
- Source or transformation logic
- Primary/foreign keys
- Partition and clustering details
Your Python code then reads the YAML and:
- Generates CREATE TABLE DDL for each layer
- Generates views for transformations (Silver, Gold)
- Optionally executes them (e.g., via BigQuery, Databricks, or Snowflake SDKs)
- Example YAML Schema Design
tables:
- name: customer_bronze
- layer: bronze
- source: gs://raw-data/customers.csv
- description: “Raw customer data from CRM system”
- columns:
- name: customer_id
- type: STRING
- nullable: false
- name: name
- type: STRING
- name: created_at
- type: TIMESTAMP
- metadata:
- partition_by: created_at
- format: parquet
- name: customer_silver
- layer: silver
- source_table: customer_bronze
- description: “Cleaned and deduplicated customer data”
- transformations:
- “SELECT DISTINCT * FROM {{source_table}}”
- columns:
- name: customer_id
- type: STRING
- name: name
- type: STRING
- name: created_at
- type: TIMESTAMP
- name: customer_gold
- layer: gold
- source_table: customer_silver
- description: “Customer aggregated metrics”
- transformations:
- |
- SELECT
- customer_id,
- COUNT(*) AS transaction_count,
- MAX(created_at) AS last_transaction
- FROM {{source_table}}
- GROUP BY customer_id
- columns:
- name: customer_id
- type: STRING
- name: transaction_count
- type: INTEGER
- name: last_transaction
- type: TIMESTAMP
- Python Script to Generate DDL and Views
import yaml
from jinja2 import Template
def load_yaml(yaml_file):
with open(yaml_file, ‘r’) as f:
return yaml.safe_load(f)
def generate_create_table_sql(table):
cols = “,\n “.join([
f”{col[‘name’]} {col[‘type’]}{’’ if col.get(‘nullable’, True) else ’ NOT NULL’}”
for col in table[‘columns’]
])
table_name = table[‘name’]
sql = f”CREATE OR REPLACE TABLE {table_name} (\n {cols}\n)”
if ‘metadata’ in table and table[‘metadata’].get(‘partition_by’):
sql += f”\nPARTITION BY {table[‘metadata’][‘partition_by’]}”
if ‘metadata’ in table and table[‘metadata’].get(‘format’):
sql += f”\nOPTIONS (format=’{table[‘metadata’][‘format’]}’)”
return sql + “;”
def generate_view_sql(table):
if ‘transformations’ not in table:
return None
source_table = table.get(‘source_table’)
transformation_sql = “\n”.join(table[‘transformations’])
sql_template = Template(transformation_sql)
rendered_sql = sql_template.render(source_table=source_table)
return f”CREATE OR REPLACE VIEW {table[‘name’]} AS\n{rendered_sql};”
if name == “main”:
schema = load_yaml(‘tables.yaml’)
for table in schema[‘tables’]:
if table[‘layer’] == ‘bronze’:
ddl = generate_create_table_sql(table)
else:
ddl = generate_view_sql(table)
print(f”\n– {table[‘name’].upper()} ({table[‘layer’]})\n{ddl}\n”)
- Best Practices Summary
YAML Design
- Keep each layer’s YAML separate (bronze.yaml, silver.yaml, gold.yaml) for modularity.
- Use Jinja templating inside YAML for reusable transformations.
- Add metadata for partitioning, file format, and owner/team.
Code Design
- Use a class-based model (Table, Column) to represent tables.
- Validate YAML schema using pydantic or cerberus.
- Add unit tests to verify SQL generation.
- Optionally, store schema history (for auditing schema evolution).
Layer Logic