Data is a key asset for organizations in this era of digitization. Harnessing the insights from data is a key challenge as data is generated at high speed and volume. Use cases like advanced analytics, real-time analytics, and data science applications need data to be gathered and processed at scale. With the flexibility and advanced features of the public cloud, organizations are moving their existing data systems to the cloud or building future-ready data platforms.
GCP is one of the leading cloud providers with a huge catalog of data services. GCP uses the same infrastructure that powers Google Search, YouTube, Gmail, etc.
This book is a field guide that helps you build modern platforms on GCP. The book starts by going deep into the fundamentals of data architectures, then takes a hands-on approach to create a unified design for future-ready data platforms, and concludes with best practices for scalability and cost. This book offers a balanced approach of theoretical concepts and hands-on exercises to help readers learn to make decisions for selecting cloud services and technologies for architecture design and implementation.
By the end of the book you'll be able to build Data Architectures using GCP and scale them by Implementing systems for data analysis, visualisation, AI & ML

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Spis treści

Modern Data Architecture on Google Cloud. Master the Art of Building High-Performance Data and AI Platforms on GCP for Enterprises eBook -- spis treści

• 1. Fundamentals of Data Architecture
• 2. Essentials of Data Engineering
• 3. Designing a Data Architecture
• 4. GCP for Data Analytics
• 5. GCP Networking
• 6. GCP Security
• 7. GCP Environment Set-up
• 8. Designing the Bronze Layer of Data & Implementing ingestion architecture
• 9. Data Catalog : Serverless, fully managed, scalable metadata management service
• 10. Silver Layer : Implementing data processing systems for batch
• 11. Silver Layer : Implementing data processing systems for streaming
• 12. Setting-up DevOps for development and deployment
• 13. Logging, Monitoring, Data Quality, Lineage and Observability
• 14. Implementing Golden Layer of Data
• 15. Orchestrating on GCP
• 16. Building Systems for Data Analysis and Visualisation
• 17. Design and Implement robust systems for Advanced Analytics ( AI & ML)
• 18. Migrating Data Platforms to GCP
• 19. Design Best practices for performance and cost
• 20. GCP Certifications for Data Architects