Google BigQuery allows users to perform simple manipulations of their data. Any BigQuery user with basic SQL knowledge should be able to perform simple manipulations with ease. This chapter covers the processes of adding data to a table from a flat .csv file, querying a table, inserting rows into tables, updating rows of tables, and deleting rows from tables.
- Preface
- Google Cloud and Google BigQuery
- Google Cloud SDK
- Google BigQuery Data Types
- Supported data types
- Data type considerations
- Converting data
- Sanitizing data
- When to transform your data? Before or after loading to BigQuery?
- Arithmetic Operators
- Comparison Operators
- Date Time Functions
- String Functions
- Regular Expression Functions
- Functions for transformation
- Mastering transformation with User-Defined Functions
- Summary
- Further Reading
- BigQuery SQL Basic
- BigQuery SQL Advanced
- Google BigQuery API
- Accessing Google BigQuery
- Programming with BigQuery API in C# .NET
- Authenticating the service account
- Listing all datasets and all tables in the project
- Creating a new dataset in the project
- Creating a new table within a dataset
- Loading data from a file in Google Cloud Storage to a BigQuery table
- Executing a query and displaying the result
- Executing the query and saving the result in a new table
- Streaming insert of rows
- Programming with BigQuery API in Python
- Listing all datasets and all tables in the project
- Creating a new dataset in the project
- Creating a new table within a dataset
- Importing data from a file in Google Cloud Storage to a BigQuery table
- Executing a query and displaying the result
- Execute query and copy results to a new table
- Streaming insert of rows
- Roles and permissions
- Summary
- Visualizing BigQuery Data
- Why is data visualization important?
- The danger of summary statistics
- Making data visualization work for you
- Three tools for visualizing BigQuery data
- Summary
- Google Cloud Pub/Sub
BigQuery SQL Basic
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