Principles of Real-Time Data Streaming (eBook, ePUB)
Definitive Reference for Developers and Engineers
Sofort per Download lieferbar
6,99 €
inkl. MwSt.
PAYBACK Punkte
3 °P sammeln!
"Principles of Real-Time Data Streaming" "Principles of Real-Time Data Streaming" offers a thorough and authoritative exploration of the foundational concepts, architectures, and evolving practices in the high-velocity world of real-time data processing. Beginning with the essential distinctions between real-time, batch, and micro-batch paradigms, the book traces the historical development of streaming systems and illuminates the core principles—such as low-latency processing, scalability, and resilience—that underpin modern streaming infrastructure. Key industry use cases across finance, ...
"Principles of Real-Time Data Streaming"
"Principles of Real-Time Data Streaming" offers a thorough and authoritative exploration of the foundational concepts, architectures, and evolving practices in the high-velocity world of real-time data processing. Beginning with the essential distinctions between real-time, batch, and micro-batch paradigms, the book traces the historical development of streaming systems and illuminates the core principles—such as low-latency processing, scalability, and resilience—that underpin modern streaming infrastructure. Key industry use cases across finance, IoT, and analytics provide a practical backdrop, demonstrating the transformative power of streaming data and the unique characteristics that differentiate real-time architectures from their traditional counterparts.
Moving from foundational theory to architectural design, the book guides readers through the blueprint of robust streaming systems, including component patterns, deployment models (on-premises, cloud, and edge), and the integration of batch and streaming for unified analytics. Comprehensive treatment is given to data ingestion challenges, advanced processing semantics, and state management, equipping practitioners with strategies for schema evolution, flow control, late-event handling, and the creation of custom computational operators. Dedicated chapters delve into performance tuning, horizontal scaling, failure recovery, and stringent mechanisms for maintaining data quality and system reliability even at scale.
Recognizing the heightened demands of operational security, advanced analytics, and compliance in a streaming context, the book covers encryption, auditing, privacy approaches, and adherence to regulatory standards such as GDPR and HIPAA. Readers benefit from actionable guidance on deploying machine learning models in streaming pipelines, leveraging online feature engineering, and implementing adaptive analytics. Finally, the book looks ahead to the future of streaming, assessing emergent paradigms—such as edge processing, serverless architectures, and sustainability—while charting the open research frontiers poised to shape the next generation of real-time data solutions.
"Principles of Real-Time Data Streaming" offers a thorough and authoritative exploration of the foundational concepts, architectures, and evolving practices in the high-velocity world of real-time data processing. Beginning with the essential distinctions between real-time, batch, and micro-batch paradigms, the book traces the historical development of streaming systems and illuminates the core principles—such as low-latency processing, scalability, and resilience—that underpin modern streaming infrastructure. Key industry use cases across finance, IoT, and analytics provide a practical backdrop, demonstrating the transformative power of streaming data and the unique characteristics that differentiate real-time architectures from their traditional counterparts.
Moving from foundational theory to architectural design, the book guides readers through the blueprint of robust streaming systems, including component patterns, deployment models (on-premises, cloud, and edge), and the integration of batch and streaming for unified analytics. Comprehensive treatment is given to data ingestion challenges, advanced processing semantics, and state management, equipping practitioners with strategies for schema evolution, flow control, late-event handling, and the creation of custom computational operators. Dedicated chapters delve into performance tuning, horizontal scaling, failure recovery, and stringent mechanisms for maintaining data quality and system reliability even at scale.
Recognizing the heightened demands of operational security, advanced analytics, and compliance in a streaming context, the book covers encryption, auditing, privacy approaches, and adherence to regulatory standards such as GDPR and HIPAA. Readers benefit from actionable guidance on deploying machine learning models in streaming pipelines, leveraging online feature engineering, and implementing adaptive analytics. Finally, the book looks ahead to the future of streaming, assessing emergent paradigms—such as edge processing, serverless architectures, and sustainability—while charting the open research frontiers poised to shape the next generation of real-time data solutions.
Andere Kunden interessierten sich für
