Build and operate the data processing systems behind analytics and ML on Google Cloud, including BigQuery, Dataflow, and Pub/Sub, and track your readiness for the Professional Data Engineer exam.
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Google Cloud feature
bq command-line tool
Topic in 1 exam domain
- bq command-line tool · Feature
- bq command-line tool · Service
Also called: bq, bq cli, bq tool
The command-line interface used to manage BigQuery resources and transfer configurations.
Where this appears on the exam
1 objective in 1 of 5 domains
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bq command-line toolFeature
Lakehouse runtime catalogFeatureIAMServiceIdentity and Access ManagementServiceManaged Service for Apache SparkServiceBigQueryServiceGoogle Cloud consoleFeatureSpannerServiceDataformServiceBigQuery StudioFeaturePoints to what builds on itWorks with or alternative toHover or tap a line to see what connects the two
- BigQuery works with bq command-line tool From the official documentation: “- Run the `bq query` command in the [bq command-line tool](/bigquery/bq-command-line-tool).”
- BigQuery Studio is an alternative to bq command-line tool From the official documentation: “The following are supported only when you query a linked BigQuery dataset by using the **BigQuery Studio** and **Looker Studio** pages, or by using the [bq command-line tool](/bigquery/docs/reference/bq-cli-reference):”
- bq command-line tool is an alternative to Google Cloud console From the official documentation: “To authorize a routine, use the Google Cloud console, the bq command-line tool, or the REST API:”
- BigQuery works with Dataform From the official documentation: “You can define custom SQL commands for Dataform to run in BigQuery.”
- BigQuery works with Lakehouse runtime catalog From the official documentation: “When querying Lakehouse runtime catalog tables from BigQuery, you use a four-part naming structure, often referred to as **P.C.N.T**:”
- BigQuery works with Spanner From the official documentation: “Reverse ETL (BigQuery to Spanner)”
- BigQuery works with Managed Service for Apache Spark From the official documentation: “This means you can quickly read and write BigQuery data to and from Managed Service for Apache Spark.”
- IAM secures BigQuery From the official documentation: “BigQuery uses IAM to manage access to resources.”
- Lakehouse runtime catalog works with Managed Service for Apache Spark From the official documentation: “Managed Service for Apache Spark supports only PySpark jobs with borderless Lakehouse Metastore.”
- BigQuery is an alternative to Managed Service for Apache Spark From the official documentation: “Run queries against your federated data using BigQuery or Managed Service for Apache Spark.”
- Identity and Access Management secures BigQuery From the official documentation: “Access to BigQuery data from AlloyDB is managed through IAM.”
- BigQuery works with Identity and Access Management From the official documentation: “BigQuery uses Identity and Access Management (IAM) for authorization.”
- Identity and Access Management secures Spanner From the official documentation: “Spanner uses Identity and Access Management (IAM) for authorization.”
- Identity and Access Management secures Dataform From the official documentation: “Dataform uses [Identity and Access Management (IAM)](/iam/docs) for access control.”
- BigQuery works with IAM From the official documentation: “BigQuery uses [IAM](/iam) to manage access to resources and provides centralized [access management](/bigquery/docs/access-control) to resources and actions.”
- BigQuery depends on Lakehouse runtime catalog From the official documentation: “BigQuery federates queries to these endpoints using the Google Cloud Lakehouse runtime catalog connection.”
- BigQuery Studio is part of BigQuery From the official documentation: “BigQuery Studio helps you discover, analyze, and run inference on data in BigQuery with the following features:”
- BigQuery Studio depends on Dataform From the official documentation: “To use CMEK to protect your BigQuery Studio code assets, you must set a default Dataform CMEK key for the Google Cloud project that contains your code assets.”
- BigQuery Studio works with Lakehouse runtime catalog From the official documentation: “The Lakehouse runtime catalog acts as the unified hub that connects BigQuery Studio to your external open-format data.”
- BigQuery Studio is an alternative to Managed Service for Apache Spark From the official documentation: “Configure your Spark session by using your preferred tool (such as Managed Service for Apache Spark or BigQuery Studio).”
- Dataform depends on BigQuery From the official documentation: “Dataform uses the following Google Cloud services that have their own quota limits: - [BigQuery](/bigquery/quotas)”
- Dataform depends on Identity and Access Management From the official documentation: “Dataform uses the following Google Cloud services that have their own quota limits: - [BigQuery](/bigquery/quotas) - [Identity and Access Management](/iam/quotas)”
- Dataform works with Identity and Access Management From the official documentation: “Dataform uses Identity and Access Management (IAM) for authorization.”
- Google Cloud console works with Spanner From the official documentation: “The Google Cloud console provides an interface for inserting, editing, and deleting data in a Spanner table.”
- IAM secures Google Cloud console From the official documentation: “For information about the various user roles and getting access control to Google Cloud console see [Access control with IAM](https://cloud.google.com/support/docs/access-control).”
- Identity and Access Management secures Google Cloud console From the official documentation: “Access to the console is controlled by Identity and Access Management (IAM).”
- Spanner depends on Identity and Access Management From the official documentation: “Spanner relies on IAM for identity management rather than managing user credentials within the database.”
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