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portada Data Engineering Best Practices: Architecture tools and techniques for the data analytics lifecycle (English Edition)
Type
Physical Book
Publisher
Author
Language
English
Pages
356
Format
Paperback
Dimensions
23.5 x 19.1 x 1.9 cm
ISBN13
9789365894615

Data Engineering Best Practices: Architecture tools and techniques for the data analytics lifecycle (English Edition)

Ramanna, Chandan; F. Dos Santos, Luiz Fernando (Author) · Bpb Publications · Paperback

Data Engineering Best Practices: Architecture tools and techniques for the data analytics lifecycle (English Edition) - Ramanna, Chandan; F. Dos Santos, Luiz Fernando

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Synopsis "Data Engineering Best Practices: Architecture tools and techniques for the data analytics lifecycle (English Edition)"

Data engineering is the backbone of modern business intelligence, yet navigating the complexities of roles and tools can be challenging for new and experienced professionals alike. However, data engineering sits at the core of modern analytics. As organizations scale their use of data, they need robust architecture, reliable pipelines, and strong governance to turn raw data into trusted insights. This book follows the journey of data from source to insight. It defines the data engineering role, presents reference architectures, and explains how to model, secure, and govern data for analytics. Subsequent chapters cover CI/CD, ETL versus ELT, infrastructure operations, data quality, operations, AI, and supporting processes. By the end of this book, the readers will possess the competency to build, design, and operate end-to-end data platforms, collaborate effectively with analysts and data scientists, and apply repeatable patterns to build secure, scalable, and high-quality data solutions.What you will learn● Grasp the core responsibilities of modern data engineers.● Design practical analytics and data platform architectures.● Model data for performance, clarity, and governance.● Secure, test, and automate pipelines with CI/CD.● Design agnostic models and analyze topologies.● Apply data operations to analytics, AI, and daily operations.Who this book is forThis book is designed for data engineers, analysts, BI developers, and scientists building analytics platforms and pipelines, and it also guides the professionals responsible for data strategy, governance, and reliable data-driven decisions.Table of Contents1. Data Engineering's Role2. Reference Architectures3. Data Models4. Permission Management5. Governance and Cataloguing6. Continuous Integration and Deployment7. ETL and ELT8. Infrastructure Operations9. Quality Assurance10. DataOps and AI11. Additional Processes12. Popular Technologies

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