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The Google Cloud Data Stack: A Project-Based Guide to Dataflow and BigQuery, Architecting Scalable and Serverless Data Solutions
Paperback
Series: Cloud Data Engineering Series: Pipelines, Automation, and Modern Architectures
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Publisher: Independently Published
Published: Sep 30 2025
Pages: 122
Weight: 0.50
Height: 0.26 Width: 7.00 Depth: 10.00
Language: English
The data challenges of today demand more than traditional databases and ETL scripts. Businesses need platforms that can scale effortlessly, handle both real-time and historical data, and reduce the complexity of managing infrastructure. Google Cloud offers this through its serverless data stack, and this book shows you exactly how to use it.
The Google Cloud Data Stack: A Project-Based Guide to Dataflow and BigQuery, Architecting Scalable and Serverless Data Solutions is a hands-on guide for engineers, architects, and analysts who want to master Google Cloud's modern data ecosystem. With a practical, project-based approach, it walks you through building robust data pipelines, architecting data lakehouse solutions, and running advanced analytics with confidence. Whether your focus is on streaming IoT events, large-scale batch processing, or predictive analytics, you'll learn how to design solutions that scale without the operational burden of traditional systems.
What sets this book apart is its structured, progressive coverage of the Google Cloud data stack. You'll begin with the foundations, learning how to set up projects, configure IAM, and navigate the Cloud Console and Cloud Shell. From there, you'll move into building pipelines with Dataflow-covering the Apache Beam programming model, batch and streaming use cases, and ETL orchestration with templates. BigQuery is explored in depth, with chapters dedicated to architecture, data loading, querying at scale, and advanced features such as BigQuery ML for machine learning and BigQuery GIS for geospatial analytics. The book also dives into integrating Dataflow and BigQuery, applying best practices for schema evolution, and designing hybrid workflows that combine batch and streaming. Later chapters guide you through optimization strategies for cost and performance, real-time analytics with Pub/Sub, and security and governance essentials. Finally, a full case study brings everything together with an end-to-end solution, supported by extended code snippets and deployment templates in the appendix.
If you've been asking yourself how to build data platforms that grow with your business, how to make streaming and batch pipelines work together, or how to use BigQuery beyond basic SQL for predictive and geospatial analytics, this book provides the answers.
Take the next step toward mastering serverless data engineering. Equip yourself with the knowledge, examples, and patterns you need to architect data solutions on Google Cloud that are not only powerful but sustainable. Purchase your copy today and start building systems designed for scale, performance, and the future of data.
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