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