Essential Apache Beam
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Essential Apache Beam
"Essential Apache Beam" "Essential Apache Beam" is a definitive guide for practitioners and architects seeking to master the design, implementation, and optimization of data processing pipelines using Apache Beam. This comprehensive resource illuminates the unified programming model at the heart of Beam, encompassing both batch and streaming data processing. It meticulously examines core abstractions such as Pipelines, PCollections, and PTransforms, offering clear guidance on SDK selection, portability across execution engines, and practical insights into the lifecycle of a pipeline. Readers are introduced to the broader Beam ecosystem and will gain a deep understanding of community-driven innovations shaping the landscape of modern data engineering. Bridging theory and practice, the book provides actionable strategies for end-to-end pipeline design: from ingesting data from diverse sources to writing reliable outputs, managing schema evolution, and developing custom IO connectors for unique environments. Advanced chapters explore robust transformations, event-time semantics, windowing, stateful and timely processing, and real-time streaming pipeline patterns. The text delves into performance tuning, parallelism, autoscaling, and cost optimization for cloud deployments, equipping engineers to build scalable and efficient solutions ready for production workloads. Complemented by dedicated sections on observability, testing, security, compliance, and disaster recovery, "Essential Apache Beam" presents readers with the tools to deliver resilient and secure data pipelines. Dozens of case studies and design patterns highlight Beam’s versatility across industries—covering topics from machine learning workflows to continuous integration and delivery best practices. Whether you are building your first pipeline or architecting a production-scale deployment, this book serves as an indispensable reference for unleashing the full power of Apache Beam in real-world analytics and processing challenges.
InfluxDB Essentials
"InfluxDB Essentials" InfluxDB Essentials is a comprehensive guide for anyone seeking to harness the full potential of InfluxDB, the industry-leading time series database. The book begins by establishing a robust foundation in the principles of time series data, exploring its unique properties, architectural considerations, and the comparative strengths of InfluxDB versus other popular time series databases. Practical industry use cases in IoT, observability, finance, and scientific monitoring are presented, along with an insightful discussion on the challenges of large-scale time series storage and emerging trends in data management. Delving deep into the architecture and operational mechanics of InfluxDB, this book offers readers clear, practical guidance on schema design, performance tuning, and high-availability deployments—covering everything from core components such as the storage engine and write-ahead log to strategies for data ingestion, retention, clustering, and security. Advanced chapters navigate through data integration pipelines, optimal ingestion approaches, precise time synchronization, and real-world strategies for handling late, duplicate, or out-of-order data. Readers will also benefit from extensive coverage of advanced querying and analytics capabilities, performance and reliability optimization, rigorous backup and disaster recovery methodologies, and sophisticated security and compliance strategies. The book concludes by showcasing ecosystem integrations, observability enablers, and the future trajectory of InfluxDB in cutting-edge applications like serverless computing, edge analytics, machine learning, and global-scale deployments. Whether you are a developer, data engineer, or architect, InfluxDB Essentials is your indispensable companion for building scalable, secure, and intelligent time series data solutions.
LogDNA Essentials
"LogDNA Essentials" "LogDNA Essentials" is a comprehensive guide for modern log management professionals, bridging foundational concepts with advanced operational practices. The book begins by situating LogDNA within the rich history of logging, articulating how its architecture and core features—such as real-time ingestion, dynamic dashboards, and robust integrations—are purpose-built to address the complexity of today's distributed systems. Through a thorough exploration of security, compliance, scalability, and industry adoption patterns, readers gain a clear understanding of why LogDNA has become a trusted platform across diverse scenarios and organizational scales. The book methodically delves into every aspect of log management, from collection strategies and schema normalization to sophisticated enrichment, metadata tagging, and real-time pipeline transformations. Practical chapters provide hands-on guidance for architecting resilient storage, executing cost-effective data retention, and ensuring compliance—all while optimizing performance at scale. Advanced coverage of querying, analysis, and visualization empowers practitioners to extract actionable insights, correlate events across sources, and proactively monitor environments through powerful dashboards and automated alerts. Beyond operational excellence, "LogDNA Essentials" looks ahead to the future of observability, including guidance on extending LogDNA with APIs, plugins, and integration with security ecosystems like SIEM and SOAR platforms. Emerging practices—such as AI-powered analysis, serverless and edge logging, and unified observability—are explored alongside real-world case studies and guidance on cost, environmental impact, and autonomous operations. This is an indispensable resource for engineers, architects, and security professionals seeking to master the art and science of log data with LogDNA.