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Python and SQL for Data Professionals: Step-by-Step Guide to Querying Databases, Building ETL Pipelines, and Launching a Career in Data Analytics and
Paperback
Publisher: Independently Published
Published: Aug 4 2026
Pages: 358
Weight: 1.83
Height: 0.74 Width: 8.50 Depth: 11.00
Language: English
Most books teach Python or SQL. This book teaches you how to use both together to build real-world data solutions.
Whether you're an aspiring data analyst, future data engineer, self-taught developer, or career changer, this hands-on guide will help you develop the practical skills employers are looking for.
Inside this book, you'll learn how to:
- Master Python and SQL as one professional skill set.
- Design and build relational databases with PostgreSQL and SQLite.
- Write advanced SQL queries using joins, subqueries, CTEs, window functions, and query optimization.
- Connect Python to databases using SQLAlchemy and psycopg2.
- Build complete ETL pipelines that extract, clean, transform, validate, and load data.
- Analyze data efficiently with pandas and SQL.
- Automate workflows with scheduling, logging, monitoring, and error handling.
- Optimize SQL performance and troubleshoot slow queries.
- Apply industry best practices for testing, database migrations, documentation, and version control.
- Build a portfolio-ready capstone project that demonstrates real-world data engineering skills.
- Prepare for Python and SQL technical interviews with confidence.
This book is ideal for:- Aspiring Data Analysts
- Future Data Engineers
- Business Intelligence Professionals
- Computer Science Students
- Self-Taught Developers
- Career Changers
- Anyone who wants practical, job-ready Python and SQL skills
You'll work with industry-standard tools, including:- Python 3
- PostgreSQL & SQLite
- pandas
- SQLAlchemy
- psycopg2
- Alembic
- pytest
- Great Expectations
- Apache Airflow, Prefect & Dagster
- Git, Streamlit, AWS RDS, Google BigQuery & dbt
Instead of isolated tutorials, you'll build complete data workflows that mirror real production environments-from database design and SQL querying to ETL pipelines, automation, and reporting.
By the end of this book, you'll have the knowledge, practical experience, and portfolio projects to confidently pursue opportunities in data analytics, data engineering, and other data-focused careers.
If you're ready to move beyond learning individual tools and start building professional data systems with Python and SQL, this book is your complete step-by-step guide.
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