Job

Related topics

• Job is part of Batch From the official documentation: “Batch consists of the following components: - **Job:** a scheduled program that runs a set of tasks to completion without any user interaction, typically for computational workloads.”
• Job is part of BigQuery From the official documentation: “Every time you [load](/bigquery/docs/loading-data), [export](/bigquery/exporting-data-from-bigquery), [query](/bigquery/docs/running-queries), or [copy data](/bigquery/docs/managing-tables#copy-table), BigQuery automatically creates, schedules, and runs a job that tracks the…”
• Job is part of Cloud Run From the official documentation: “On Cloud Run, your code can run as a *service*, *job*, *worker pool*, or *instance*.”
• Job is part of Dataflow From the official documentation: “In Dataflow, a [`Job`](/dataflow/docs/reference/rest/v1b3/projects.jobs#Job) resource represents a Dataflow job.”
• Job works with Knowledge Catalog From the official documentation: “When you run a data quality scan, Knowledge Catalog creates a job.”
• Job configures task From the official documentation: “A Job describes the Task to execute and automatically manages Task creation.”
• task is part of Job From the official documentation: “A Batch job consists of one or several tasks.”
• BigQuery is an alternative to Cloud Storage From the official documentation: “The `import_result_sink` must be a Cloud Storage path or a BigQuery table.”
• BigQuery works with Cloud Storage From the official documentation: “Sample to load Avro data from Cloud Storage into a new BigQuery table”
• BigQuery works with Cloud Logging From the official documentation: “BigQuery utilizes Cloud Logging for some of your log data.”
• Cloud Run depends on Pub/Sub From the official documentation: “Cloud Run function triggered by Pub/Sub to fetch HYPR MFA logs and write to GCS.”
• Dataflow works with Pub/Sub From the official documentation: “Pub/Sub notifies Dataflow about the upload.”
• BigQuery works with Cloud Monitoring From the official documentation: “You can use Monitoring to export your metrics to BigQuery.”
• BigQuery works with Dataflow From the official documentation: “Read from BigQuery to Dataflow”
• Cloud Storage works with Dataflow From the official documentation: “Write from Dataflow to Cloud Storage”
• VPC Service Controls secures BigQuery From the official documentation: “BigQuery data is protected with VPC Service Controls”
• BigQuery works with Knowledge Catalog From the official documentation: “Some Knowledge Catalog functionalities trigger job execution using BigQuery.”
• BigQuery works with Pub/Sub From the official documentation: “createBigQuerySubscription creates a Pub/Sub subscription that exports messages to BigQuery.”
• Cloud Logging works with Pub/Sub From the official documentation: “Pub/Sub writes various log entries to Cloud Logging in the Google Cloud console.”
• Cloud Storage is an alternative to Pub/Sub From the official documentation: “To keep audit logs for a longer period of time or to use more powerful search capabilities, you can route copies of your audit logs to Cloud Storage, BigQuery, or Pub/Sub.”
• Cloud Logging works with Cloud Monitoring From the official documentation: “You can store and view query-response activity logs in Cloud Logging and set up logs-based monitoring by using Cloud Monitoring.”
• Cloud Logging works with Cloud Storage From the official documentation: “Cloud Storage writes various log entries to Cloud Logging in the Google Cloud console.”
• Cloud Storage works with Pub/Sub From the official documentation: “| **Google Cloud** | Cloud Storage | Pub/Sub |”
• BigQuery is an alternative to Pub/Sub From the official documentation: “To keep audit logs for a longer period of time or to use more powerful search capabilities, you can route copies of your audit logs to Cloud Storage, BigQuery, or Pub/Sub.”
• Cloud Run works with Cloud Storage From the official documentation: “Receive events from Cloud Storage in a Cloud Run service”
• Cloud Run works with Identity and Access Management From the official documentation: “For example, a remote backend deployed on Cloud Run can use Identity and Access Management (IAM) to:”
• Identity and Access Management secures Cloud Storage From the official documentation: “Cloud Storage uses Identity and Access Management (IAM) for authorization.”
• VPC Service Controls secures Cloud Storage From the official documentation: “Storage Transfer Service supports transfers to Cloud Storage buckets protected by VPC Service Controls.”
• BigQuery depends on Cloud Logging From the official documentation: “BigQuery utilizes Cloud Logging for some of your log data.”
• BigQuery works with Identity and Access Management From the official documentation: “BigQuery uses Identity and Access Management (IAM) for authorization.”
• Cloud Logging is an alternative to Cloud Storage From the official documentation: “Instead, they are routed to either Cloud Storage (recommended) or Cloud Logging, depending on your organization's configuration.”
• Cloud Logging works with Knowledge Catalog From the official documentation: “Knowledge Catalog sends logs for service operations and job executions to Cloud Logging.”
• Cloud Monitoring works with Cloud Run From the official documentation: “Cloud Monitoring records metrics regarding the execution of Cloud Run.”
• Cloud Monitoring works with Dataflow From the official documentation: “Dataflow is fully integrated with Cloud Monitoring.”
• Cloud Monitoring works with Pub/Sub From the official documentation: “Use Cloud Monitoring and Pub/Sub to set up custom monitoring and alerting policies for exported logs.”
• Cloud Run works with Pub/Sub From the official documentation: “A Cloud Run app receives the Pub/Sub message.”
• Cloud Storage works with Knowledge Catalog From the official documentation: “Knowledge Catalog is an intelligent governance solution for data and AI assets in Google Cloud. It provides a centralized inventory where you can discover, manage, and govern your data across Google Cloud data sources like BigQuery, Cloud Storage, Pub/Sub, and Spanner.”
• Dataflow depends on Cloud Storage From the official documentation: “Begin by creating a Cloud Storage bucket using the Google Cloud console or Google Cloud CLI. The Dataflow pipeline uses this bucket as a temporary storage location.”
• Identity and Access Management works with VPC Service Controls From the official documentation: “VPC Service Controls perimeters and Identity and Access Management (IAM) roles work together.”
• Knowledge Catalog works with Pub/Sub From the official documentation: “Knowledge Catalog is an intelligent governance solution for data and AI assets in Google Cloud. It provides a centralized inventory where you can discover, manage, and govern your data across Google Cloud data sources like BigQuery, Cloud Storage, Pub/Sub, and Spanner.”
• Batch works with Cloud Logging From the official documentation: “This document describes how to enable, generate, and view [logs](/logging/docs/overview) from [Cloud Logging](/logging/docs) for a Batch job.”
• Batch works with Cloud Monitoring From the official documentation: “This document describes how to monitor and try to optimize the resources for a Batch job by viewing metrics in Cloud Monitoring.”
• Batch works with Pub/Sub From the official documentation: “Batch supports Pub/Sub notifications for changes to job and task states, which you can use for alerts, observability, or analysis.”
• Batch works with VPC Service Controls From the official documentation: “This document explains how to use VPC Service Controls with Batch.”
• BigQuery works with Cloud Run From the official documentation: “You can then use Cloud Run to transcode data present in the bucket and move it to BigQuery.”
• BigQuery depends on Cloud Storage From the official documentation: “BigQuery relies on Cloud Storage for staging file data and supports reading from and writing to several other sources and Google Cloud services.”
• BigQuery works with task From the official documentation: “To load CSV data from Cloud Storage into a BigQuery table that uses column-based time partitioning:”
• BigQuery works with VPC Service Controls From the official documentation: “BigQuery integrates with VPC Service Controls to restrict access to your BigQuery resources from specific networks.”
• Cloud Logging is an alternative to Cloud Monitoring From the official documentation: “To see the status of your invalidation request or find a definitive list of recently submitted requests, you can use Cloud Logging or Cloud Monitoring.”
• Cloud Logging works with Cloud Run From the official documentation: “Cloud Logging captures, stores, and analyzes Cloud Run logs.”
• Cloud Logging works with Dataflow From the official documentation: “You can control the volume of Dataflow logs ingested by Cloud Logging by following the [Filter logs documentation](/dataflow/docs/guides/filter-logs).”
• Cloud Logging works with VPC Service Controls From the official documentation: “This page describes how you can find VPC Service Controls errors using Cloud Logging.”
• Cloud Monitoring works with Knowledge Catalog From the official documentation: “Cloud Logging and Cloud Monitoring let you track billable token consumption for Knowledge Catalog (formerly Dataplex Universal Catalog) data insights scans.”
• Cloud Monitoring works with VPC Service Controls From the official documentation: “Cloud Monitoring performs the VPC perimeter check when a project is *added* to a metrics scope:”
• Cloud Storage works with Identity and Access Management From the official documentation: “To learn how to use IAM with Cloud Storage, see [Using IAM Permissions](/storage/docs/access-control/using-iam-permissions).”
• Cloud Storage works with task From the official documentation: “To load CSV data from Cloud Storage into a BigQuery table that uses column-based time partitioning:”
• Dataflow depends on Identity and Access Management From the official documentation: “Dataflow uses Identity and Access Management (IAM) for authorization.”
• Dataflow works with VPC Service Controls From the official documentation: “To customize DNS resolution when using Dataflow with VPC Service Controls, use Cloud DNS [private zones](/dns/docs/overview#dns-private-zones) instead of using custom BIND servers.”
• Identity and Access Management secures Batch From the official documentation: “Batch uses Identity and Access Management (IAM) for authorization.”
• Identity and Access Management secures BigQuery From the official documentation: “Security in BigQuery is intrinsically linked to [Identity and Access Management (IAM)](/iam/docs/overview) in Google Cloud.”