Data pipelines & processing
ETL-style flows, batch transforms, validation, and moving data between formats.
How to Implement Incremental Load with Watermark by updated_at in Python
Load only new or changed rows into SQLite by comparing an updated_at timestamp against a stored watermark, returning counts and the new watermark.
import sqlite3
from datetime import datetime, timedelta
def watermark_incremental_load(db_path, table_name, last_watermark, source_data):
"""Load only rows with updated_at greater than the last watermark."""
conn = sqlite3.connect(db_path)
cursor = conn.cursor()
# Create table if it doesn't exist
…How to Implement Slowly Changing Dimension Type 2 History in Python
Build a type-2 slowly changing dimension pipeline that closes old records and opens new ones when customer data changes.
from datetime import datetime, timedelta
def apply_scd_type2(records, current_date):
"""Returns active records after inserting new records with type-2 history."""
history = []
active = {}
for record in records:
key = record["customer_id"]
if key in active:
active[key]["end…Browse by section
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Data pipelines & processing — Python code examples
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