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BigQuery for Data Warehousing: Managed Data Analysis in the Google Cloud
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BigQuery for Data Warehousing: Managed Data Analysis in the Google Cloud

BigQuery for Data Warehousing: Managed Data Analysis in the Google Cloud

Mark Mucchetti

525 pages, parution le 30/09/2020

Résumé

Intermediate-Advanced user levelCreate a data warehouse, complete with reporting and dashboards using Google's BigQuery technology. This book takes you from the basic concepts of data warehousing through the design, build, load, and maintenance phases. You will build capabilities to capture data from the operational environment, and then mine and analyze that data for insight into making your business more successful. You will gain practical knowledge about how to use BigQuery to solve data challenges in your organization.
BigQuery is a managed cloud platform from Google that provides enterprise data warehousing and reporting capabilities. Part I of this book shows you how to design and provision a data warehouse in the BigQuery platform. Part II teaches you how to load and stream your operational data into the warehouse to make it ready for analysis and reporting. Parts III and IV cover querying and maintaining, helping you keep your information relevant with other Google Cloud Platform services and advanced BigQuery. Part V takes reporting to the next level by showing you how to create dashboards to provide at-a-glance visual representations of your business situation. Part VI provides an introduction to data science with BigQuery, covering machine learning and Jupyter notebooks.

What You Will Learn
  • Design a data warehouse for your project or organization
  • Load data from a variety of external and internal sources
  • Integrate other Google Cloud Platform services for more complex workflows
  • Maintain and scale your data warehouse as your organization grows
  • Analyze, report, and create dashboards on the information in the warehouse
  • Become familiar with machine learning techniques using BigQuery ML

Who This Book Is For
Developers who want to provide business users with fast, reliable, and insightful analysis from operational data, and data analysts interested in a cloud-based solution that avoids the pain of provisioning their own servers.
Part I. Building a Warehouse 1. Settling into BigQuery 2. Starting Your Warehouse Project 3. All My Data 4. Managing BigQuery Costs Part II. Filling the Warehouse 5. Loading Data Into the Warehouse6. Streaming Data Into the Warehouse 7. Dataflow Part III. Using the Warehouse 8. Care and Feeding of Your Warehouse 9. Querying the Warehouse 10. Scheduling Jobs 11. Serverless Functions with GCP 12. Cloud Logging Part IV. Maintaining the Warehouse 13. Advanced BigQuery 14. Data Governance15. Adapting to Long-Term Change Part V. Reporting On and Visualizing Your Data 16. Reporting17. Dashboards and Visualization 18. Google Data Studio Part VI. Enhancing Your Data's Potential 19. BigQuery ML 20. Jupyter Notebooks and Public Datasets 21. Conclusion 22. Appendix A: Cloud Shell and Cloud SDK 23. Appendix B: Sample Project Charter
Mark Mucchetti is an industry technology leader in healthcare and ecommerce. He has been working with computers and writing software for over 30 years, starting with BASIC and Turbo C on an Intel 8088 and now using Node.js in the cloud. He has been building and managing technology groups for much of that time, combining his deep love of technical topics with his management skills to create world-class platforms. Mark has also worked in databases, release engineering, front- and back-end coding, and project management. He believes that the best decisions are made with the best data available, and that BigQuery is a great technology to increase the value and accessibility of data for business leaders on a day-to-day basis. He has seen the transformation that accurate, timely data has on an organization's ability to succeed, and wants to bring that knowledge to the world in a people-first way.

Caractéristiques techniques

  PAPIER
Éditeur(s) Apress
Auteur(s) Mark Mucchetti
Parution 30/09/2020
Nb. de pages 525
EAN13 9781484261859

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