Architecting Real Time Analytics With Druid
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Architecting Real-Time Analytics with Druid
"Architecting Real-Time Analytics with Druid" "Architecting Real-Time Analytics with Druid" is a comprehensive guide for engineers, architects, and data professionals seeking to harness the full power of Apache Druid for large-scale, low-latency analytics. The book opens with a thorough exploration of the real-time analytics landscape, carefully positioning Druid within the modern data ecosystem alongside databases, warehouses, and stream processors. Readers are equipped with foundational context on the architectural drivers behind real-time analytics, receive guidance on navigating the book based on their unique use cases, and benefit from a candid discussion of both success factors and common pitfalls for organizations new to this fast-evolving space. Delving into the technical depths, the book systematically unpacks Druid’s architecture, including its core components, storage internals, and data ingestion pipelines supporting both stream and batch modalities. It offers advanced strategies for modeling schemas, managing high-cardinality and semi-structured data, and tuning ingestion for correctness and resilience at scale. Chapters on query performance, resource management, high availability, and operational best practices ensure that readers can design, deploy, and maintain robust Druid clusters able to withstand the rigors of production workloads in fields ranging from financial surveillance to IoT telemetry. Recognizing the multifaceted needs of contemporary analytics platforms, "Architecting Real-Time Analytics with Druid" extends its coverage to advanced Druid extensions, integration patterns for BI and data science, and the implementation of security, governance, and compliance protocols. The book brings theory to life through case studies drawn from mission-critical deployments in telecommunications, fraud detection, and media analytics, concluding with a forward-looking view on serverless architectures, AI-driven operations, and emerging trends shaping the future of real-time analytics. This resource is essential reading for anyone seeking to build resilient, scalable, and innovative analytics solutions with Apache Druid at their core.
Memcached Architecture and Deployment
"Memcached Architecture and Deployment" "Memcached Architecture and Deployment" delivers a comprehensive and authoritative exploration of Memcached—from its foundational principles and architectural evolution to advanced deployment and optimization strategies. Carefully structured into thematic chapters, this book guides readers through the core concepts and inner workings of Memcached, including process models, memory management, security, and protocols. Discerning examinations reveal how Memcached compares to related technologies, elucidate common use cases in modern systems, and provide practical insights for integrating and scaling distributed caching effectively. With an emphasis on real-world applicability, the book details deployment models spanning on-premises, cloud-native, and hybrid environments, addressing challenges such as service discovery, automation, and zero-downtime upgrades. Readers gain essential knowledge on high-availability architectures, advanced sharding, geo-distributed clusters, and robust performance optimization techniques. Each chapter combines in-depth theoretical context with actionable guidance, including strategies for monitoring, troubleshooting, and maintaining operational excellence at scale. Security, compliance, and best practices for integration are thoroughly covered to ensure resilient, enterprise-grade deployments. Additionally, the book looks ahead to the future of in-memory caching, discussing trends like AI-enhanced caching, edge computing, and hybrid architectural patterns. Whether you are an architect, DevOps engineer, or developer, "Memcached Architecture and Deployment" is an indispensable reference for mastering distributed caching in today’s data-driven ecosystem.
Real-Time Big Data Analytics: Emerging Architecture
Author: Mike Barlow
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2013-06-24
Five or six years ago, analysts working with big datasets made queries and got the results back overnight. The data world was revolutionized a few years ago when Hadoop and other tools made it possible to getthe results from queries in minutes. But the revolution continues. Analysts now demand sub-second, near real-time query results. Fortunately, we have the tools to deliver them. This report examines tools and technologies that are driving real-time big data analytics.