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Data Engineering & Analytics: Parallel Data Pipelines in Production: Design and Scale Real-Time Data Systems Using Modern Distributed Frameworks
Paperback
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ISBN13: 9798195654290
Publisher: Independently Published
Published: May 5 2026
Pages: 374
Weight: 1.91
Height: 0.77 Width: 8.50 Depth: 11.00
Language: English
Publisher: Independently Published
Published: May 5 2026
Pages: 374
Weight: 1.91
Height: 0.77 Width: 8.50 Depth: 11.00
Language: English
Build production-grade data pipelines that scale - from your first ETL workflow to distributed systems handling real-time data under production pressure
Data engineering isn't about scripts that work once. It's about systems that process massive volumes of data continuously, survive failures, and deliver results when it matters. As datasets grow and systems become distributed, the real challenge is no longer writing code - it's designing pipelines that scale, perform, and remain reliable in production. This book takes you from zero to production-ready data systems with a practical, no-nonsense approach. You'll start by understanding how distributed processing actually works - why single machines fail at scale, and how parallelism, latency, and throughput define system performance. Then you'll build a complete pipeline from scratch, implementing extraction, transformation, and loading while adding logging, monitoring, and debugging practices used in real-world systems.
Data engineering isn't about scripts that work once. It's about systems that process massive volumes of data continuously, survive failures, and deliver results when it matters. As datasets grow and systems become distributed, the real challenge is no longer writing code - it's designing pipelines that scale, perform, and remain reliable in production. This book takes you from zero to production-ready data systems with a practical, no-nonsense approach. You'll start by understanding how distributed processing actually works - why single machines fail at scale, and how parallelism, latency, and throughput define system performance. Then you'll build a complete pipeline from scratch, implementing extraction, transformation, and loading while adding logging, monitoring, and debugging practices used in real-world systems.
As your pipeline grows, you'll move beyond basics into the problems that break most systems. You'll learn how to partition large datasets correctly, eliminate bottlenecks caused by skewed data, and process streaming data in real time. You'll integrate message brokers to decouple services and build pipelines that don't collapse under load. You'll design systems that tolerate failure by default, implement checkpointing and recovery mechanisms, and optimise performance using profiling and resource tuning. Security is treated as a core requirement, not an afterthought, with practical approaches to encryption, access control, and audit logging.
You'll then step into operating data systems at scale - building monitoring and observability pipelines, setting up alerting, managing infrastructure costs, and testing systems under real-world conditions. The book concludes with deployment strategies using CI/CD, zero-downtime updates, and advanced architectures like Lambda, Kappa, and event-driven systems used in modern data platforms. Key Features
This book is for developers and engineers who want to build serious data systems - not demos. You should be comfortable writing code and understand basic data processing concepts.
If you've built pipelines that work locally but break at scale, this book will show you how to fix that. Backend developers moving into data engineering, data analysts stepping into engineering roles, and DevOps engineers managing data infrastructure will find this especially valuable. No prior experience with distributed systems is required, but this is not a beginner's walkthrough. It's a practical guide for engineers who want to build systems that actually run in production - reliably, efficiently, and at scale.
You'll then step into operating data systems at scale - building monitoring and observability pipelines, setting up alerting, managing infrastructure costs, and testing systems under real-world conditions. The book concludes with deployment strategies using CI/CD, zero-downtime updates, and advanced architectures like Lambda, Kappa, and event-driven systems used in modern data platforms. Key Features
- Build scalable data pipelines using parallel and distributed processing for both batch and real-time systems
- Design high-performance pipelines with efficient partitioning, resource optimisation, and bottleneck elimination
- Implement production-grade reliability with fault tolerance, monitoring, logging, and secure data handling
- Understand how distributed data systems work and why scalability, latency, and throughput matter
- Build end-to-end ETL pipelines with logging, monitoring, and debugging built in from the start
- Design partitioning strategies that prevent data skew and maximise parallel performance
- Process real-time data streams using event-time semantics, windowing, and aggregation techniques
- Integrate message brokers to decouple systems and handle high-throughput data flows and more
This book is for developers and engineers who want to build serious data systems - not demos. You should be comfortable writing code and understand basic data processing concepts.
If you've built pipelines that work locally but break at scale, this book will show you how to fix that. Backend developers moving into data engineering, data analysts stepping into engineering roles, and DevOps engineers managing data infrastructure will find this especially valuable. No prior experience with distributed systems is required, but this is not a beginner's walkthrough. It's a practical guide for engineers who want to build systems that actually run in production - reliably, efficiently, and at scale.
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