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Building Time-Series Workflows on AWS Timestream: Scalable Data Management, Querying, and Analytics is a practical, hands-on guide for architects, engineers, and analytics professionals who design and operate time-series systems in the cloud. The book begins with core time-series concepts and a clear exposition of Timestream's architectureits storage tiers, data model, and scalability strategieswhile comparing Timestream to RDBMS, NoSQL, and other time-series platforms to help you choose the right fit for your workloads and deployment scenarios.
You'll get step-by-step guidance for provisioning, configuring, and optimizing Timestream across the entire data lifecycle, with detailed patterns for batch, micro-batch, and streaming ingestion and proven integrations with Kinesis, Lambda, QuickSight, and Glue. Practical chapters cover advanced data modeling, query design, storage optimization, cost controls, security and compliance, and performance tuningeach illustrated with actionable examples and operational best practices to build reliable, high-performance analytics pipelines.
Looking ahead, the book explores emerging architectures such as serverless analytics, edge computing, AI/ML-driven workflows, and zero-ETL approaches, and surveys open standards and next-generation cloud strategies. Real-world case studies from IoT, DevOps, financial services, and manufacturing provide blueprints you can adapt for scalable telemetry, predictive analytics, and secure multi-tenant solutions. Whether you're building telemetry pipelines, forecasting engines, or enterprise monitoring platforms, this guide equips you to design, deploy, and scale effective time-series workflows on AWS Timestream.
You'll get step-by-step guidance for provisioning, configuring, and optimizing Timestream across the entire data lifecycle, with detailed patterns for batch, micro-batch, and streaming ingestion and proven integrations with Kinesis, Lambda, QuickSight, and Glue. Practical chapters cover advanced data modeling, query design, storage optimization, cost controls, security and compliance, and performance tuningeach illustrated with actionable examples and operational best practices to build reliable, high-performance analytics pipelines.
Looking ahead, the book explores emerging architectures such as serverless analytics, edge computing, AI/ML-driven workflows, and zero-ETL approaches, and surveys open standards and next-generation cloud strategies. Real-world case studies from IoT, DevOps, financial services, and manufacturing provide blueprints you can adapt for scalable telemetry, predictive analytics, and secure multi-tenant solutions. Whether you're building telemetry pipelines, forecasting engines, or enterprise monitoring platforms, this guide equips you to design, deploy, and scale effective time-series workflows on AWS Timestream.
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