About This Architecture

ABC Phones Credit Portfolio data pipeline orchestrates daily ingestion of credit snapshots, sales, customer, and NPS data from CSV and XLSX sources via Apache Airflow DAG, landing raw files in Snowflake Bronze layer. Python extractors perform schema validation and type standardization before COPY INTO operations, while dbt transforms Bronze staging tables into Silver and Gold layers with business logic, feature engineering, and dimensional models. Multi-layer data quality checks—Python pre-load validation, dbt tests for uniqueness and referential integrity, and Great Expectations post-transform checks—ensure freshness, schema compliance, and distribution accuracy. Alerts route critical failures to PagerDuty and routine notifications to Slack, enabling portfolio monitoring dashboards in Metabase/Looker and ad-hoc analyst SQL access. Fork this diagram to customize source connectors, dbt model lineage, or alerting thresholds for your own medallion architecture.

People also ask

How do you build a production ETL pipeline with Apache Airflow, Snowflake medallion architecture, and automated data quality checks?

This diagram shows a complete daily batch pipeline: Apache Airflow orchestrates sense_files → ingest_to_snowflake → dbt_run_models → dbt_test → data_quality_checks → notify_alerts. Python extractors validate schemas and standardize types before loading to Snowflake Bronze (raw), dbt transforms to Silver (staging) and Gold (analytics), and Great Expectations plus dbt tests enforce freshness, unique