Pipeline Engineer: Building Modern Data Infrastructure with Python, Airflow, dbt, and the Cloud

Design scalable, reliable, and production-ready data platforms for modern analytics and machine learningData systems are the backbone of modern organizations.From analytics dashboards and business intelligence to machine learning pipelines and real-time decision systems, companies depend on reliable data infrastructure to operate effectively."Pipeline Engineer" is a practical, engineering-focused guide to building modern data platforms using Python, Apache Airflow, dbt, and cloud-native infrastructure.This book teaches developers and data engineers how to design, orchestrate, transform, monitor, and scale production-grade data systems. Why modern data engineering mattersOrganizations today face challenges such as: - fragmented data sources- unreliable pipelines and failed jobs- poor data quality and governance- scaling transformation workloads- operational complexity across cloud systems- maintaining observability and lineageBuilding dependable data infrastructure requires both software engineering discipline and operational reliability. What you will learn- fundamentals of modern data architecture- designing ETL and ELT workflows- workflow orchestration with Airflow- transformation modeling with dbt- scalable data ingestion patterns- data warehouse and lakehouse concepts- pipeline testing and validation- observability and monitoring strategies- cloud-native deployment workflows- security, governance, and access management From raw data to reliable platformsThroughout the book, you will learn how to: - design maintainable data pipelines- orchestrate complex workflow dependencies- build reusable transformation layers- improve data quality and reliability- monitor pipelines proactively- scale data infrastructure across cloud environments- manage production operations confidentlyEach chapter focuses on practical workflows used in real-world data engineering teams. Practical applications- analytics engineering platforms- business intelligence pipelines- machine learning data infrastructure- event-driven data systems- cloud-native ETL and ELT platforms- enterprise reporting and governance systemsThese examples reflect real production data engineering challenges. Who this book is for- data engineers- analytics engineers- backend developers- cloud engineers- machine learning infrastructure teams- software engineers transitioning into data platformsIf you want to build scalable, maintainable, and production-ready data systems, this book provides the roadmap.Move data reliably. Transform intelligently. Engineer infrastructure that scales.

Pagina's: 286, Paperback, Independently published