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211022390
Data Engineering Design Patterns: Scalable data engineering for efficient data systems and workflows (English Edition)
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211022390
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This book enables a data engineer to not only become familiar with data engineering patterns but also understand their application in real world use cases.
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What Stands Out
Scalable Solutions
Offers design patterns tailored for scalability, ensuring that data architectures can expand efficiently as data volumes grow, catering to enterprises experiencing rapid data influx.
Efficient Workflows
Focuses on optimizing data workflows, enabling users to streamline processes and reduce latency, thus enhancing overall productivity and responsiveness in data handling.
Comprehensive Framework
Provides a holistic approach to data engineering, integrating best practices and principles that empower professionals to build robust and maintainable data systems across various platforms.
Product Details
- Data engineering has gained even more relevance than before, and data engineering patterns are key to the successful implementation of data engineering projects. This book enables a data engineer to not only become familiar with data engineering patterns but also understand their application in real world use cases.This book presents a comprehensive collection of data engineering patterns, each illustrated with relevant enterprise use cases to highlight their value and simplicity. It showcases both open-source and cloud technologies, guiding readers in building data systems for on-premise and cloud environments. The book covers patterns for data ingestion, transformation, storage, and serving, while also offering insights into performance engineering for data pipelines. Once we understand fundamental data engineering patterns, we then shift focus to patterns that help us build high-performance low latency data systems. We cover data caching, partitioning, replication, and how to select the technology stack for building out the patterns in this book.By the end of the book, readers will have a deep understanding of various data engineering use cases and will be able to map the appropriate patterns to address them. They will also be equipped to choose the right technical stack for implementing these patterns, enabling them to create robust and efficient data systems in a secure and a cost-effective manner.What you will learn● Key data engineering patterns.● Data ingestion and processing patterns.● Modern architectures like Lambda.● Explore time-tested data patterns of ETL and ELT.● Modern data systems like data lake and medallion architectures.● Domain-specific patterns and also on data orchestration, observability, and security.● Overcoming performance challenges in building complex data systems.Who this book is forThis book is designed for data engineers with beginner to intermediate experience in building enterprise-grade data systems. ETL developers transitioning into data engineering roles will also find this book valuable for understanding essential data engineering patterns. The code snippets provided throughout the book are written in Python or Scala, so a basic understanding of either language will help readers more easily grasp the concepts presented.Table of Contents1. Understanding Data Engineering2. Data Engineering Patterns, Terminologies, and Technical Stack3. Batch Ingestion and Processing4. Real-time Ingestion and Processing5. Micro-batching6. Lambda Architecture7. ETL and ELT8. Data Fundamentals9. Databases and Transactional Data10. Data Warehouse and Data Analytics11. Data Lake and Medallion Architecture12. Data Replication and Partitioning13. Hot Versus Cold Data Storage14. Data Caching and Low Latency Serving15. Data Search Patterns16. Domain Specific Patterns17. Data Security Patterns18. Data Observability and Monitoring Patterns19. Idempotency and Deduplication Patterns20. Data Orchestration Patterns21. Common Performance Pitfalls22. Technology and Infrastructure Selection23. Recap and Next Steps
| Publisher | BPB Publications |
| Publication date | 23 Sept. 2025 |
| Language | English |
| Print length | 346 pages |
| ISBN-10 | 9365891760 |
| ISBN-13 | 978-9365891768 |
| Dimensions | 19.05 x 1.98 x 23.5 cm |
Who Should Buy?
-
Data Engineers
Ideal for data engineers seeking to enhance their knowledge of design patterns for scalable data workflows.
-
Technical Managers
Helps technical managers understand scalable data engineering concepts to better lead their teams in efficient data projects.
-
Data Scientists
Useful for data scientists wanting to optimize data systems and workflows for improved analysis and insights.
-
Beginners in Data
Not suitable for beginners lacking foundational knowledge in data engineering concepts and practices.
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English edition Amit Kulkarni Editorial Review
Data Engineering Design Patterns: Scalable Data Engineering For Efficient Data Systems And Workflows (English Edition) provides a comprehensive guide for mastering data engineering practices. With a print length of 346 pages, this book, published by BPB Publications, dives into essential patterns that help in creating efficient data systems and workflows. It is crafted in English and is set to be released on September 23, 2025. Readers can expect a thorough exploration of scalable design patterns that can be applied in real-world scenarios, making it an invaluable resource for aspiring data engineers and seasoned practitioners alike.
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Pros
- Comprehensive coverage of data engineering concepts
- Detailed explanations of design patterns
- Practical examples for real-world application
- Well-structured information for easy understanding
- Useful for both beginners and experts
Cons
- May require prior knowledge of data engineering concepts
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Features & Benefits
- Comprehensive guide to data engineering patterns.
- Focuses on real-world applications to enhance understanding.
- Covers modern architectures and time-tested data patterns.
- Includes insights on performance engineering for pipelines.
- Equips readers to choose the right tech stack for efficient data systems.
- Designed for beginner to intermediate data engineers and ETL developers.
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