txt Data Engineering with Python and PostgreSQL: ETL Pipelines, Warehousing, and Reporting

Brief Hook:
Build a production-grade data platform with the tools you already use. This hands-on guide shows how to combine Python and PostgreSQL to ingest, model, and serve analytics that teams can trust.

Book Summary:
"Data Engineering with Python and PostgreSQL: ETL Pipelines, Warehousing, and Reporting" takes you from a clean repo to a reliable system. You will load data at wire speed with COPY, model it with clear grain and keys, and precompute heavy queries with materialized views for fast dashboards. Along the way, you will add tests, constraints, and drift checks so quality issues are caught early.

Beyond SQL, the book shows how to package pipelines in Python with psycopg 3 and SQLAlchemy 2.0, expose safe slices via a minimal FastAPI service, and ship governed exports in CSV/Parquet with contracts and manifests. You will also learn Docker Compose for prod-like environments, health and readiness checks, versioned migrations with Alembic, scheduling with Cron/Prefect/Airflow, partitioning and indexing for performance, and least-privilege sharing with roles, RLS, and masked views.

Why Choose this Book?:

• Practical, copy-pasteable code: end-to-end examples using Python 3.12+, PostgreSQL 16+, psycopg 3, and SQLAlchemy 2.0.

• Clear warehouse layering: staging -> core -> marts, with idempotent upserts, slice refreshes, and materialized views.

• Production focus: Docker Compose stacks, health/readiness endpoints, metrics, alerts, and release checklists.

• Data quality built in: constraints as contracts, pytest fixtures, referential tests, and drift detection.

• Safe distribution: governed exports (CSV/Parquet) with schemas and checksums, plus a thin FastAPI reporting API.

Call-to-Action:
If you want a small, understandable stack that scales without surprises, this book is your playbook. Click "Buy now" to start building reliable pipelines, fast dashboards, and safe data shares with Python and PostgreSQL.