AWS Timestream Data Management and Analysis: Definitive Reference for Developers and Engineers

"AWS Timestream Data Management and Analysis"

Discover the definitive guide to harnessing the power of AWS Timestream with "AWS Timestream Data Management and Analysis." Designed for architects, engineers, and analytic professionals, this comprehensive book delves deeply into time series data concepts and the unique architectural foundations underpinning Timestream. It offers a clear exploration of how AWS Timestream compares to traditional RDBMS, NoSQL, and other time series solutions, highlighting its data structures, scalability strategies, and ideal deployment scenarios in the modern cloud.

The book provides hands-on guidance for provisioning, configuring, and optimizing Timestream environments at every stage of the data lifecycle. Readers will gain practical insights into efficient data ingestion—batch, micro-batch, and streaming—as well as robust integration with AWS services like Kinesis, Lambda, QuickSight, and Glue. Detailed chapters address advanced data modeling, analytics, and storage optimization techniques, along with cost management, security best practices, compliance frameworks, and performance tuning. Each topic is presented through clear explanations and actionable patterns, empowering professionals to architect reliable, high-performance time series solutions for IoT, DevOps, financial, and manufacturing applications.

Looking toward the future, the book explores emerging innovations such as serverless analytics, edge computing, AI/ML integrations, and zero-ETL architectures, while also surveying open standards and next-generation cloud strategies. Real-world case studies and industry applications illustrate Timestream’s value across diverse sectors, providing readers with practical blueprints for success. Whether you're building scalable telemetry pipelines, predictive analytics engines, or secure, multi-tenant data architectures, this authoritative guide offers the tools, context, and confidence to excel with AWS Timestream.

© 2025 HiTeX Press (eBook): 6610000811366

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eBook: 28 maja 2025