SQL

Related topics

• Gemini works with SQL From the official documentation: “Real-time analysis: brings the world knowledge of Gemini to enterprise data using SQL.”
• BigQuery works with SQL From the official documentation: “You can also use Cloud Asset Inventory to transfer this data into BigQuery. This will allow you to query the data using SQL, which is easier to read compared to interpreting JSON-formatted data.”
• 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 Knowledge Catalog From the official documentation: “Some Knowledge Catalog functionalities trigger job execution using BigQuery.”
• BigQuery ML works with Cloud Storage From the official documentation: “You can export BigQuery ML models to Cloud Storage, and use them for online prediction, or edit them in Python.”
• BigQuery works with Gemini From the official documentation: “Use Gemini to analyze your enterprise data in BigQuery.”
• BigQuery works with Vertex AI From the official documentation: “The page provides guidance for getting predictions from Vertex AI, based on retail [data exported into BigQuery](/retail/docs/export-data-into-bq).”
• Agent Platform works with BigQuery ML From the official documentation: “Learn how to use BigQuery ML for training with Gemini Enterprise Agent Platform.”
• Agent Platform works with Cloud Storage From the official documentation: “Load the model's `state_dict` object into Cloud Storage by using a Agent Platform custom job:”
• AlloyDB for PostgreSQL works with BigQuery From the official documentation: “Datastream lets you stream from AlloyDB for PostgreSQL to BigQuery using the Free Tier, providing up to 100 GiB of change data capture data free every month.”
• AlloyDB for PostgreSQL works with Cloud Storage From the official documentation: “AlloyDB lets you import data from files located in a Cloud Storage bucket.”
• AlloyDB for PostgreSQL works with Gemini From the official documentation: “AlloyDB AI functions integrate large language models (LLMs) such as Gemini directly with your AlloyDB for PostgreSQL data so that you can combine natural language with SQL queries.”
• AlloyDB for PostgreSQL works with Knowledge Catalog From the official documentation: “Enable the Knowledge Catalog integration for your AlloyDB cluster”
• 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.”
• Agent Platform works with BigQuery From the official documentation: “BigQuery and Agent Platform are tightly integrated, letting you access and analyze your data directly within the Agent Platform platform.”
• Agent Platform works with Gemini From the official documentation: “Gemini on Agent Platform can handle large volumes of content, making it suitable for platforms of all sizes.”
• BigQuery works with BigQuery ML From the official documentation: “BigQuery ML lets you build and operationalize machine learning (ML) models over data in BigQuery by using SQL.”
• 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 ML is part of BigQuery From the official documentation: “BigQuery ML models are stored in BigQuery datasets, similar to tables and views.”
• BigQuery ML depends on Cloud Storage From the official documentation: “BigQuery ML imports the model from Cloud Storage by using the credentials of the user who runs the `CREATE MODEL` statement.”
• BigQuery ML works with Gemini From the official documentation: “This tutorial shows you how to gain insights from unstructured image data by integrating BigQuery ML with Gemini.”
• Cloud Storage works with Gemini From the official documentation: “Perform batch text prediction with Gemini using GCS data source as input.”
• Cloud Storage works with Vertex AI From the official documentation: “Access Cloud Storage content through your current project's Vertex AI service agent.”
• Gemini works with Knowledge Catalog From the official documentation: “**Chat powered by Gemini**: Powered by Gemini, the agent parses your natural language questions, runs search and metadata lookup tools against Knowledge Catalog, formulates multi-step exploration plans, and displays its progress in collapsible thought traces.”
• Gemini is part of Vertex AI From the official documentation: “Google Cloud provides a set of state-of-the-art foundation models through Vertex AI, including Gemini.”