Manages BigQuery datasets, tables, and jobs using CLI, client libraries, and Terraform.
npx skills add https://github.com/Aki2022/skills --skill bigquery-basics-aki2022
Please help me install this Agent Skill. Skill: bigquery-basics Source: https://github.com/Aki2022/skills/tree/main/bigquery-basics Command: npx skills add https://github.com/Aki2022/skills --skill bigquery-basics-aki2022
bq CLI. - Multi-Language Client Access: Run queries from Python, Java, Node.js, or Go using official Google Cloud client libraries, plus pandas-style analysis with BigQuery DataFrames. - Infrastructure & Security: Provision BigQuery resources with Terraform and apply IAM best practices including column-level security, row access policies, and CMEK encryption. - Use Case: A data engineer needs to set up a new analytics pipeline. They use this Skill to create a dataset and partitioned table via Terraform, load CSV data from Cloud Storage with bq load, and validate results with a SQL query. ## Quick Start Ask the assistant to create a BigQuery dataset named my_dataset in the US location and run a sample query against a public dataset.High-intent search queries and answers about installing and using this skill.
Use bq mk --dataset --location=US my_dataset to create a dataset, then bq mk --table my_dataset.my_table schema.json to create a table from a JSON schema file. Enable the BigQuery API first with gcloud services enable bigquery.googleapis.com.
Install google-cloud-bigquery with pip, create a bigquery.Client(), and call client.query() with your SQL string. The query_job.result() method returns the rows once the job completes.
Yes, the Google Cloud Terraform provider supports BigQuery datasets, tables, jobs, and reservations. Use google_bigquery_dataset and google_bigquery_table resources to define schemas, partitioning, and expiration policies declaratively.
The execute_sql tool only allows SELECT statements; INSERT, UPDATE, DELETE, and stored procedures are rejected. Queries may still have side effects through remote functions or Python UDFs, and all queries carry a goog-mcp-server label.
Use column-level security with policy tags to restrict sensitive columns, row access policies for row-level filtering, and data masking to obscure values. Follow least-privilege IAM by granting roles at the most granular resource level.
FunnelEnvyLoad CSV, JSON, Avro, and Parquet data into BigQuery.
wangx7Run machine learning and generative AI tasks within BigQuery SQL queries.
ClawBioExecutes read-only SQL against BigQuery public datasets with cost safeguards and reproducibility outputs.
yigitkonurBuilds TypeScript AI agents with the mcp-use library and MCP servers.