Advanced BigQuery
2h 15mAdvanced2022-07-28
Authors
Kishan Iyer
Content Engineer, DevOps Expert, and Google Cloud Platform Power User
Course details
If you’re already using BigQuery for your data warehouse, you may be ready to take your next step. But the sheer array of available solutions can get a little overwhelming. In this course, software engineer and instructor Kishan Iyer shows you how to use advanced skills and techniques to get the most out of your BigQuery experience.
Learn to apply table partitioning and clustering to improve query performance and reduce data processing costs. Find out how to use nested data structures, regulate access to datasets and tables, monitor resource utilization, and access BigQuery programmatically, even from external services. Along the way, Kishan walks you through populating tables with the data transfer service.
Learn to apply table partitioning and clustering to improve query performance and reduce data processing costs. Find out how to use nested data structures, regulate access to datasets and tables, monitor resource utilization, and access BigQuery programmatically, even from external services. Along the way, Kishan walks you through populating tables with the data transfer service.
Skills covered
BigQueryData EngineeringAdvancedGoogleData Science
Concepts
0. Introduction
- 01 - Making the most of BigQuery
1. Partitioning BigQuery Tables
- 02 - Partitioning and clustering
- 03 - Creating a BigQuery data set
- 04 - Loading data into a table
- 05 - Creating a partitioned table
- 06 - Querying a partitioned table
- 07 - Partitioning on an integer column
- 08 - Understanding integer-based partitions
- 09 - Partitioning on ingestion time
2. Clustering BigQuery Tables
- 10 - Defining a clustered table
- 11 - Querying a clustered table
- 12 - Combining partitioning and clustering
3. Working with Nested Fields
- 13 - Working with composite data
- 14 - Creating tables with nested fields
- 15 - Inserting nested data
- 16 - Querying nested data
- 17 - Aggregating data using ARRAY AGG
- 18 - Performing Windows operations
4. Using the BigQuery Data Transfer Service
- 19 - Simplifying BigQuery tasks
- 20 - Setting up the source and destination for a transfer
- 21 - Configuring user permissions for a transfer
- 22 - Defining a scheduled transfer
- 23 - Running an ad-hoc transfer
5. Managing Permissions and Security in BigQuery
- 24 - Granting access to data sets
- 25 - Permissions on BigQuery tables
- 26 - Revoking permissions in BigQuery
- 27 - Restricting access to derived data
- 28 - Monitoring BigQuery usage
- 29 - Snapshots
6. Accessing BigQuery from External Applications
- 30 - Creating a service account
- 31 - Connecting to BigQuery with Python
Conclusion
- 32 - Next steps