BigQuery for Data Warehousing

Create 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.

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. 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 Warehouse

6. 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 Governance

15. Adapting to Long-Term Change

PART V. REPORTING ON AND VISUALIZING YOUR DATA

16. Reporting

17. 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