python-data-engineering

To build data pipelines in Python with SQLAlchemy 2.0, you use factory-based data transformation patterns to convert API responses into strongly typed models, apply safe upserts, and enforce a three-layer architecture separating sources, warehouse, and applications.

For incremental sync in PostgreSQL data pipelines, you implement high-water-mark tracking and use JSONB-based schema resilience. This approach captures only changed records from source systems and handles evolving API schemas without breaking existing warehouse tables.

Yes, you can apply Kimball-style dimensional modeling in Python by defining dim_, fact_, and stg_ table conventions. The patterns support slowly changing dimensions (SCD), medallion architectures, and advanced SQLAlchemy features like TypeDecorator and hybrid properties.

Yes, the data pipeline architecture supports orchestration with dbt, Airflow, or Dagster. You can integrate these tools to schedule ETL/ELT workflows that move data from API sources into a PostgreSQL warehouse using the defined SQLAlchemy 2.0 transformation patterns.

You convert raw API responses into typed SQLAlchemy models by using a from_api_response factory pattern. This transformation validates and maps unstructured JSON data directly into strongly typed database records ready for safe upserts and merges.

Enforcing a three-layer architecture—source, warehouse, and application—in ETL workflows maintains a single source of truth. It cleanly separates raw API data ingestion from transformed warehouse tables and consuming application views, ensuring scalable and reusable analytics.