High-Performance Stream Processing with Faust and Python (eBook, ePUB)
The Complete Guide for Developers and Engineers
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"High-Performance Stream Processing with Faust and Python" "High-Performance Stream Processing with Faust and Python" is a comprehensive guide to designing, building, and optimizing real-time data pipelines using Faust-a powerful stream processing framework tailored for the Python ecosystem. Beginning with a methodical overview of modern stream processing principles, the book navigates through the fundamental distinctions between batch and streaming paradigms, critical performance metrics, architectural considerations for distributed systems, and the increasing demands for low latency and scal...
"High-Performance Stream Processing with Faust and Python" "High-Performance Stream Processing with Faust and Python" is a comprehensive guide to designing, building, and optimizing real-time data pipelines using Faust-a powerful stream processing framework tailored for the Python ecosystem. Beginning with a methodical overview of modern stream processing principles, the book navigates through the fundamental distinctions between batch and streaming paradigms, critical performance metrics, architectural considerations for distributed systems, and the increasing demands for low latency and scalability in real-world sectors such as finance, IoT, and analytics. It demystifies key concepts like time semantics, stateful computations, and the performance guarantees essential for designing robust streaming applications. Diving into the architecture of Faust, the book offers an in-depth exploration of its core abstractions-agents, streams, and tables-and the seamless integration with Python's asyncIO for highly concurrent, scalable stream processing. Readers will learn practical techniques for stream partitioning, state management with RocksDB, serialization strategies, and fault-tolerance mechanisms, all supported by detailed use cases and architectural blueprints. The book systematically addresses pipeline design patterns, including joining, windowing, and aggregating streams, microservice choreography, durability strategies, and techniques for handling out-of-order or late event data-all while maintaining data consistency and reliability across complex, distributed systems. Practical guidance extends to integration with external systems such as Kafka, databases, cloud-native services, and various message brokers, along with proven methods for deployment, monitoring, and securing production stream processing applications. Advanced chapters cover rigorous testing methodologies, chaos engineering, performance optimization, and observability in modern operational environments. The book concludes with cutting-edge topics including machine learning pipelines, hybrid cloud architectures, open-source ecosystem contributions, and forward-looking perspectives on the evolution of Python stream processing. Whether you are a platform engineer, software architect, or data practitioner, this book equips you with the insights and best practices needed to build, operate, and future-proof high-throughput streaming systems with Faust and Python.
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