Publisher's Synopsis
Are you ready to master the art of building efficient, scalable data pipelines with Python? Python for Data Engineering and Analytics offers a clear, practical guide to designing, automating, and optimizing data workflows that power today's data-driven organizations.
This book takes you step-by-step through foundational concepts and hands-on techniques-covering data ingestion, transformation, orchestration, and advanced analytics. Learn how to handle diverse data sources, manage environments, implement robust testing, and integrate machine learning within your pipelines. Explore modern architectures like streaming, batch processing, and cloud-native deployments to build resilient systems that scale effortlessly.
What makes this book stand out? It covers everything you need in one place, including:
Foundations of data engineering and Python essentials
Data acquisition from files, databases, APIs, and cloud storage
Cleaning and transforming data at scale with Pandas, Dask, and PySpark
Designing data models, managing schema evolution, and data warehousing
Building, automating, and orchestrating ETL/ELT pipelines with Airflow and Prefect
Working with big data and real-time streaming technologies
Advanced analytics, visualization, and interactive dashboard creation
Integrating machine learning into data workflows
Cloud data platform architectures, serverless engineering, and cost optimization
Best practices for security, governance, version control, testing, and collaboration
Real-world case studies demonstrating end-to-end solutions
Whether you're a data engineer, analyst, or software developer looking to expand your skillset, this book equips you with practical strategies and code examples to confidently build production-ready pipelines. Embrace modern data engineering principles and accelerate your ability to turn raw data into actionable insights.
Start building scalable, reliable, and efficient data systems today-transform your data workflows and drive meaningful business outcomes with Python.
