executor

Related topics

• Cloud Storage works with executor From the official documentation: “The executor stores your notebook output in the Cloud Storage bucket.”
• executor is part of Cloud Data Fusion From the official documentation: “Recommended: Use 1 CPU and 4 GB of memory per Executor (this value applies to each Executor process). Set these in the **Configure > Resources** dialog. ![Optimize resource settings in Cloud Data Fusion Configure…”
• executor is part of Gemini Enterprise Agent Platform Workbench From the official documentation: “Learn how to use the executor feature of Gemini Enterprise Agent Platform Workbench to automate a workflow to train and deploy a model.”
• 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 SQL From the official documentation: “Add credentials to connect BigQuery to Cloud SQL.”
• BigQuery works with Cloud Logging From the official documentation: “BigQuery utilizes Cloud Logging for some of your log data.”
• Cloud SQL works with Cloud Storage From the official documentation: “Exports data from a Cloud SQL instance to a Cloud Storage bucket as a SQL dump or CSV file.”
• Dataflow works with Pub/Sub From the official documentation: “Pub/Sub notifies Dataflow about the upload.”
• 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”
• Cloud Data Fusion works with Managed Service for Apache Spark From the official documentation: “Deployed Cloud Data Fusion pipelines run on Managed Service for Apache Spark clusters by default.”
• 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 SQL From the official documentation: “Database Migration Service and Cloud SQL use Cloud Logging.”
• 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 Managed Service for Apache Spark From the official documentation: “Managed Service for Apache Spark accesses these files on Cloud Storage.”
• 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.”
• Managed Service for Apache Spark depends on Cloud Storage From the official documentation: “Managed Service for Apache Spark uses Cloud Storage as its default distributed file system.”
• BigQuery works with Cloud Data Fusion From the official documentation: “Cloud Data Fusion pulls data from the storage bucket and writes it to BigQuery.”
• BigQuery depends on Cloud Logging From the official documentation: “BigQuery utilizes Cloud Logging for some of your log data.”
• 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.”
• Cloud Data Fusion works with Cloud Logging From the official documentation: “To use Cloud Logging with your Cloud Data Fusion pipeline, create a Cloud Data Fusion instance with Cloud Logging enabled:”
• 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.”
• 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.”
• Dataflow is an alternative to Managed Service for Apache Spark From the official documentation: “For more complex data loads into your Cloud SQL for PostgreSQL database, consider using Dataflow or Managed Service for Apache Spark, which involves creating an ETL process.”
• 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 Gemini Enterprise Agent Platform Workbench From the official documentation: “Agent Platform Workbench instances also include a BigQuery integration that lets you browse and query data from within the JupyterLab interface.”
• BigQuery is an alternative to Managed Service for Apache Spark From the official documentation: “An Analytics Workload refers to the migration of new Data Lake and Data Warehouse environments from on-premises (e.g., Teradata, Cloudera) and cloud platforms (e.g., Snowflake, Redshift, Databricks) to BigQuery or Managed Service for Apache Spark on Google Cloud.”
• Cloud Data Fusion works with Cloud SQL From the official documentation: “This page describes how to connect a private Cloud Data Fusion instance to a private Cloud SQL instance that hosts a MySQL database.”
• Cloud Data Fusion depends on Managed Service for Apache Spark From the official documentation: “Cloud Data Fusion uses Managed Service for Apache Spark as the execution environment for pipelines.”
• Cloud Data Fusion works with Pub/Sub From the official documentation: “This page describes how to publish Cloud Data Fusion pipeline events, such as pipeline status, to Pub/Sub topics.”
• 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 Managed Service for Apache Spark From the official documentation: “Enables (`true`) or disables (`false`) Managed Service for Apache Spark job driver logs in [Cloud Logging](/logging/docs).”
• Cloud SQL depends on Cloud Storage From the official documentation: “When ingesting data from Cloud SQL, data is first staged to a Cloud Storage bucket.”
• Cloud SQL works with Dataflow From the official documentation: “Use Datastream to replicate changes from your MySQL instance to either BigQuery or Cloud Storage, and then use Dataflow or Managed Service for Apache Spark to bring the data into your Cloud SQL instance.”
• Cloud SQL works with Managed Service for Apache Spark From the official documentation: “Use Datastream to replicate changes from your MySQL instance to either BigQuery or Cloud Storage, and then use Dataflow or Managed Service for Apache Spark to bring the data into your Cloud SQL instance.”
• Dataflow works with Gemini Enterprise Agent Platform Workbench From the official documentation: “Dataflow only supports Workbench instances that use the Apache Beam container.”
• Managed Service for Apache Spark depends on Cloud Logging From the official documentation: “Managed Service for Apache Spark clusters require network connectivity to Google Cloud APIs and services, such as the Dataproc API, Cloud Storage, and Cloud Logging, and to user resources, such as data sources in other VPC networks or on-premises environments.”
• Managed Service for Apache Spark works with Pub/Sub From the official documentation: “You can use Managed Service for Apache Spark to process high-volume streams from a service like Pub/Sub or Apache Kafka and land the data in the bronze tier.”