BigQuery for Data Warehousing: Managed Data Analysis in the Google Cloud
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.
1137195810
BigQuery for Data Warehousing: Managed Data Analysis in the Google Cloud
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.
64.99 In Stock
BigQuery for Data Warehousing: Managed Data Analysis in the Google Cloud

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

by Mark Mucchetti
BigQuery for Data Warehousing: Managed Data Analysis in the Google Cloud

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

by Mark Mucchetti

Paperback(1st ed.)

$64.99 
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Overview

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.

Product Details

ISBN-13: 9781484261859
Publisher: Apress
Publication date: 10/01/2020
Edition description: 1st ed.
Pages: 525
Product dimensions: 7.01(w) x 10.00(h) x (d)

About the Author

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.

Table of Contents

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
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