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Data Engineering Best Practices: Architecture tools and techniques for the data analytics lifecycle (English Edition)
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товар №:
225517826
KGS 5469
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By the end of this book, the readers will possess the competency to build, design, and operate end-to-end data platforms.
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What Stands Out
Comprehensive Coverage
This book covers essential architecture, tools, and techniques for data engineering, making it an invaluable resource for both beginners and professionals in the data analytics lifecycle.
Practical Insights
It offers practical insights and best practices, helping readers effectively tackle real-world data challenges and optimize their data workflows for greater efficiency.
Industry-Relevant Techniques
With a focus on up-to-date methodologies, this guide equips readers with industry-relevant techniques, ensuring they remain competitive in the rapidly evolving field of data analytics.
Информация о продукте
- 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
| Publisher | BPB Publications |
| Publication date | 30 Jan. 2026 |
| Language | English |
| Print length | 356 pages |
| ISBN-10 | 9365894611 |
| ISBN-13 | 978-9365894615 |
| Dimensions | 19.05 x 2.06 x 23.5 cm |
Who Should Buy?
-
Data Engineers
Provides essential methodologies and tools to enhance skill sets and improve data pipelines and architecture design.
-
Analytics Professionals
Help in understanding the data lifecycle, making it easier to analyze and extract insights from complex datasets.
-
Business Analysts
Equips them with best practices for data management and analytics, facilitating better decision-making based on data insights.
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Beginners in Data
May find the content too advanced, requiring foundational knowledge or practical experience in data engineering concepts first.
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English edition Luiz Fernando F Dos Santos Editorial Review
Data Engineering Best Practices: Architecture Tools And Techniques For The Data Analytics Lifecycle (English Edition) is a comprehensive guide published by BPB Publications that spans 356 pages. Released on 30 Jan. 2026, this book is tailored for those looking to deepen their knowledge in data engineering. With clear architecture tools and techniques, it’s designed to advance your understanding of the data analytics lifecycle. Readers will appreciate the structured approach and practical applications found within its pages, offering insights that can be implemented in real-world scenarios.
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Плюсы
- Detailed methodology for data engineering practices
- Clear explanations of technical concepts
- Practical applications for real-world scenarios
- Comprehensive coverage of data analytics lifecycle
- Well-structured and easy to follow
Минусы
- Some readers may find it a bit technical
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Особенности и преимущества
- Learn the core responsibilities of modern data engineers.
- Design robust analytics and data platform architectures.
- Model data for performance, clarity, and governance.
- Secure, test, and automate pipelines with CI/CD methodologies.
- Understand both ETL and ELT processes effectively.
- Gain practical insights to support data-driven decision-making.
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