Data pipelines & warehousing

Moving data from operational databases into analytical warehouses.

The idea

You shouldn't run massive analytical queries ("Total sales by month") on your live production database—it will slow down user traffic. Instead, data is extracted, transformed, and loaded (ETL) into a Data Warehouse.

Older systems run Batch Pipelines every night (e.g. at 2 AM). Modern systems use Streaming / CDC (Change Data Capture) to capture database changes in real-time. Good pipelines also include Quality Gates to quarantine bad data before it ruins executive dashboards.

Prod DB (Source) Transform & Cleanse Warehouse (Storage) Quarantine
Pipeline idle. Waiting for trigger.

How it works (Data Quality Gates)

def transform_data(record):
    # 1. Standardize formatting
    record['date'] = parse_iso(record['date'])
    
    # 2. Quality Gate (Data Contract)
    if record['amount'] < 0:
        # Invalid data! Do NOT load to warehouse.
        # Quarantine it for human review.
        send_to_dead_letter_queue(record)
        return None
        
    return record