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

✍ Scribed by Mark Mucchetti

• Publisher: Apress
• Year: 2020
• Tongue: English
• Leaves: 539
• Edition: 1st ed.
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter ....Pages i-xxxv
Front Matter ....Pages 1-1
Settling into BigQuery (Mark Mucchetti)....Pages 3-21
Starting Your Warehouse Project (Mark Mucchetti)....Pages 23-43
All My Data (Mark Mucchetti)....Pages 45-60
Managing BigQuery Costs (Mark Mucchetti)....Pages 61-71
Front Matter ....Pages 73-73
Loading Data into the Warehouse (Mark Mucchetti)....Pages 75-103
Streaming Data into the Warehouse (Mark Mucchetti)....Pages 105-121
Dataflow (Mark Mucchetti)....Pages 123-152
Front Matter ....Pages 153-153
Care and Feeding of Your Warehouse (Mark Mucchetti)....Pages 155-172
Querying the Warehouse (Mark Mucchetti)....Pages 173-207
Scheduling Jobs (Mark Mucchetti)....Pages 209-230
Serverless Functions with GCP (Mark Mucchetti)....Pages 231-251
Cloud Logging (Mark Mucchetti)....Pages 253-269
Front Matter ....Pages 271-271
Advanced BigQuery (Mark Mucchetti)....Pages 273-303
Data Governance (Mark Mucchetti)....Pages 305-332
Adapting to Long-Term Change (Mark Mucchetti)....Pages 333-351
Front Matter ....Pages 353-353
Reporting (Mark Mucchetti)....Pages 355-378
Dashboards and Visualization (Mark Mucchetti)....Pages 379-400
Google Data Studio (Mark Mucchetti)....Pages 401-416
Front Matter ....Pages 417-417
BigQuery ML (Mark Mucchetti)....Pages 419-468
Jupyter Notebooks and Public Datasets (Mark Mucchetti)....Pages 469-496
Conclusion (Mark Mucchetti)....Pages 497-498
Cloud Shell and Cloud SDK (Mark Mucchetti)....Pages 499-508
Sample Project Charter (Mark Mucchetti)....Pages 509-513
Back Matter ....Pages 515-525

✦ Subjects

Computer Science; Database Management