Advanced BigQuery

Learn advanced techniques to improve your performance in BigQuery, the cloud-hosted data warehouse on the Google Cloud Platform.

Introduction

• Making the most of BigQuery

1. Partitioning BigQuery Tables

• Partitioning and clustering
• Creating a BigQuery data set
• Loading data into a table
• Creating a partitioned table
• Querying a partitioned table
• Partitioning on an integer column
• Understanding integer-based partitions
• Partitioning on ingestion time

2. Clustering BigQuery Tables

• Defining a clustered table
• Querying a clustered table
• Combining partitioning and clustering

3. Working with Nested Fields

• Working with composite data
• Creating tables with nested fields
• Inserting nested data
• Querying nested data
• Aggregating data using ARRAY_AGG
• Performing Windows operations

4. Using the BigQuery Data Transfer Service

• Simplifying BigQuery tasks
• Setting up the source and destination for a transfer
• Configuring user permissions for a transfer
• Defining a scheduled transfer
• Running an ad-hoc transfer

5. Managing Permissions and Security in BigQuery

• Granting access to data sets
• Permissions on BigQuery tables
• Revoking permissions in BigQuery
• Restricting access to derived data
• Monitoring BigQuery usage
• Snapshots

6. Accessing BigQuery from External Applications

• Creating a service account
• Connecting to BigQuery with Python

Conclusion

• Next steps