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SKU/Artículo: AMZ-B0GF5ZSQ68
Designing Data-Intensive Applications For High-Volume Analytics And Distributed Systems: Using Python Programming
Format:
Paperback
Detalles del producto
Disponibilidad:
En stock
En stock
Peso con empaque:
0.47 kg
0.47 kg
Devolución:
Sí
Sí
Condición
Nuevo
Nuevo
Producto de:
Amazon
Amazon
Viaja desde
USA
USA
Sobre este producto
- Master the Architecture of Modern Big Data SystemsIn an era where data is the new oil, simply writing code isn't enough—you must build systems that are resilient, scalable, and maintainable. Designing Data-Intensive Applications for High-Volume Analytics and Distributed Systems is a comprehensive guide to navigating the complex landscape of modern data architecture using Python.Summary of the BookThis book bridges the gap between theoretical distributed systems and practical, production-ready engineering. It dives deep into the core principles of data-intensive application design, covering everything from storage engines and data models to the intricacies of distributed coordination. By leveraging Python’s powerful ecosystem—including libraries like PySpark, Dask, and FastAPI—you will learn to build systems capable of processing terabytes of data with sub-second latency.What Makes This Book Different?Unlike most "how-to" coding manuals, this book focuses on the "Why" before the "How." It doesn't just show you how to use a tool; it teaches you how to evaluate trade-offs between consistency, availability, and partition tolerance (CAP theorem). It uniquely combines the high-level abstractions of Python with low-level architectural patterns, ensuring you aren't just a programmer, but a true system architect.Inside the BookArchitecting for Scale: Master horizontal scaling, sharding, and replication strategies.Pythonic Data Processing: Use Dask and PySpark for high-volume batch and stream processing.Storage Internals: Understand LSM-trees vs. B-trees and how to choose the right database for your workload.Distributed Systems Patterns: Implement consensus algorithms, leader election, and idempotent processing.Real-Time Analytics: Build low-latency pipelines using Kafka and Python-based consumers.Who is This Book For?Data Engineers looking to move beyond simple ETL pipelines into complex system design.Software Architects wanting to master the nuances of distributed state and data consistency.Python Developers aiming to break into the world of Big Data and High-Performance Computing.Computer Science Students seeking a practical application of distributed systems theory.
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U$S 30,78
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