bigquery

name

bigquery

description

Use bigquery CLI (instead of `bq`) for all Google BigQuery and GCP data warehouse operations including SQL query execution, data ingestion (streaming insert, bulk load, JSONL/CSV/Parquet), data extraction/export, dataset/table/view management, external tables, schema operations, query templates, cost estimation with dry-run, authentication with gcloud, data pipelines, ETL workflows, and MCP/LSP server integration for AI-assisted querying and editor support. Modern Rust-based replacement for the Python `bq` CLI with faster startup, better cost awareness, and streaming support. Handles both small-scale streaming inserts (<1000 rows) and large-scale bulk loading (>10MB files), with support for Cloud Storage integration.

BigQuery CLI Skill

You are a BigQuery specialist using the bigquery CLI tool. This skill provides comprehensive guidance for working with Google BigQuery through a unified Rust-based CLI with query execution, template management, and server modes.

Core Capabilities

The bigquery CLI provides:

1. Authentication: Check status and login with gcloud
2. Query Execution: Run SQL queries with cost awareness and confirmation prompts
3. Dry Run: Estimate query costs without execution
4. Dataset Operations: List datasets in a project
5. Table Operations: List, describe, insert, load, and manage external tables
6. Template System: Named query templates with parameter substitution
7. MCP Server: Semantic search via stdio or HTTP modes
8. LSP Server: SQL language server for editor integration

Authentication

Check Authentication Status

# Check if authenticated and verify required scopes
bigquery auth check

# Will show:
# - Authentication status
# - Active account
# - BigQuery scopes availability

Login with gcloud

# Authenticate with gcloud including all required BigQuery scopes
bigquery auth login

# This will:
# 1. Run gcloud auth login
# 2. Ensure all necessary BigQuery scopes are granted
# 3. Verify authentication succeeded

Best Practice: Always run bigquery auth check first to verify authentication before operations.

Query Operations

Running Queries

# Basic query execution (interactive cost confirmation)
bigquery query "SELECT * FROM dataset.table LIMIT 10"

# Skip cost confirmation for automation
bigquery query --yes "SELECT COUNT(*) FROM dataset.table"

# JSON output (default)
bigquery query "SELECT * FROM dataset.table LIMIT 5"

# Text/table output
bigquery query --format text "SELECT * FROM dataset.table LIMIT 5"

Cost Awareness: The query command automatically:

1. Estimates query cost before execution
2. Displays bytes to be processed
3. Prompts for confirmation (unless --yes is used)
4. Prevents accidental expensive queries

Query Output Formats

# JSON output (default, machine-readable)
bigquery query "SELECT * FROM dataset.table"
bigquery query --format json "SELECT * FROM dataset.table"

# Text output (human-readable table)
bigquery query --format text "SELECT * FROM dataset.table"

Dry Run (Cost Estimation)

# Estimate cost without executing
bigquery dry-run "SELECT * FROM large_dataset.table WHERE date >= '2025-01-01'"

# Returns:
# - Bytes that would be processed
# - Estimated cost
# - No actual data

Use dry-run to:

• Estimate costs before running expensive queries
• Validate query syntax
• Check partition pruning effectiveness
• Test queries in CI/CD pipelines

Dataset Operations

Listing Datasets

# List datasets in current project (text format, default)
bigquery datasets list my-project

# JSON output
bigquery datasets list my-project --format json

# Example output shows:
# - Dataset ID
# - Location
# - Creation time
# - Labels (if any)

Note: Dataset reference format is project.dataset or just project to list all datasets.

Table Operations

Listing Tables

# List tables in a dataset (text format, first 10)
bigquery tables list my-project.my-dataset

# JSON output
bigquery tables list my-project.my-dataset --format json

# Limit results
bigquery tables list my-project.my-dataset --limit 20

# Maximum limit is 100
bigquery tables list my-project.my-dataset --limit 100

Describing Table Schema

# Show table schema and metadata (text format)
bigquery tables describe my-project.my-dataset.my-table

# JSON output
bigquery tables describe my-project.my-dataset.my-table --format json

# Output includes:
# - Column names and types
# - Nullability (NULLABLE, REQUIRED, REPEATED)
# - Mode information
# - Table metadata

Inserting Rows (Small Datasets)

Best for <1000 rows. Uses streaming insert API for immediate availability.

JSONL (Newline-Delimited JSON) Format

From JSONL File:

# Create sample JSONL file
cat > users.jsonl <<EOF
{"id": "1", "name": "Alice Johnson", "email": "[e-mail]", "age": 30}
{"id": "2", "name": "Bob Smith", "email": "[e-mail]", "age": 25}
{"id": "3", "name": "Charlie Brown", "email": "[e-mail]", "age": 35}
EOF

# Insert from JSONL file
bigquery tables insert my-project.dataset.users \
  --data users.jsonl --format json

From JSONL Stream (stdin):

# Stream from command output
echo '{"id": "1", "name": "Alice", "email": "[e-mail]"}' | \
  bigquery tables insert my-project.dataset.users --data - --format json

# Stream from multiple sources (heredoc)
cat << EOF | bigquery tables insert my-project.dataset.users --data - --format json
{"id": "1", "name": "Alice", "email": "[e-mail]", "age": 30}
{"id": "2", "name": "Bob", "email": "[e-mail]", "age": 25}
{"id": "3", "name": "Charlie", "email": "[e-mail]", "age": 35}
EOF

# Stream from application output
my-etl-tool --output jsonl | bigquery tables insert my-project.dataset.events --data -

# Stream from compressed file
gunzip -c logs.jsonl.gz | bigquery tables insert my-project.dataset.logs --data -

# Stream from jq transformation
cat raw_data.json | jq -c '.records[]' | \
  bigquery tables insert my-project.dataset.processed --data -

JSONL Format Requirements:

• Each line is a separate JSON object
• Empty lines are automatically skipped
• No commas between objects
• Ideal for streaming and large datasets
• Format: {"field1":"value1","field2":"value2"}\n

CSV Format

From CSV File:

# Create sample CSV file
cat > users.csv <<EOF
id,name,email,age
1,Alice Johnson,[e-mail],30
2,Bob Smith,[e-mail],25
3,"Charlie Brown, Jr.",[e-mail],35
EOF

# Insert from CSV file
bigquery tables insert my-project.dataset.users \
  --data users.csv --format csv

From CSV Stream (stdin):

# Stream from heredoc
cat << EOF | bigquery tables insert my-project.dataset.users --data - --format csv
id,name,email,age
1,Alice Johnson,[e-mail],30
2,Bob Smith,[e-mail],25
3,Charlie Brown,[e-mail],35
EOF

# Stream from application output
./generate_report.sh | bigquery tables insert my-project.dataset.reports --data - --format csv

# Stream from compressed CSV
gunzip -c data.csv.gz | bigquery tables insert my-project.dataset.imports --data -

# Stream from curl/API response
curl -s https://api.example.com/export.csv | \
  bigquery tables insert my-project.dataset.api_data --data - --format csv

# Transform and stream CSV
cat raw.csv | tail -n +2 | awk '{print tolower($0)}' | \
  bigquery tables insert my-project.dataset.cleaned --data - --format csv

CSV Format Requirements:

• First row must contain column headers matching BigQuery table schema
• Values are inserted as strings (BigQuery will coerce types)
• Supports quoted fields, escaped quotes, and newlines (RFC 4180 compliant)
• Headers are case-sensitive and must match table column names

Additional Insert Options

# Insert inline JSON (single object)
bigquery tables insert my-project.dataset.users \
  --json '{"id": "1", "name": "Alice", "email": "[e-mail]"}'

# Insert inline JSON array
bigquery tables insert my-project.dataset.users \
  --json '[{"id": "1", "name": "Alice"}, {"id": "2", "name": "Bob"}]'

# Dry-run validation (no data inserted)
bigquery tables insert my-project.dataset.users \
  --data users.csv --format csv --dry-run

# Skip invalid rows instead of failing
bigquery tables insert my-project.dataset.users \
  --data users.csv --format csv --skip-invalid

# Ignore unknown fields in data
bigquery tables insert my-project.dataset.users \
  --data users.csv --format csv --ignore-unknown

# Combine options for production pipelines
cat production_data.jsonl | \
  bigquery tables insert my-project.dataset.production \
    --data - --format json \
    --skip-invalid \
    --ignore-unknown

Insert Options:

• --json <JSON>: Inline JSON data (object or array)
• --data <PATH>: Path to data file, or - for stdin
• --format <FORMAT>: Data format (json or csv, default: json)
• --dry-run: Validate without inserting
• --skip-invalid: Skip invalid rows instead of failing
• --ignore-unknown: Ignore unknown fields in data
• --yes: Skip confirmation prompts

Loading Data (Large Datasets)

Best for >10MB files or >1000 rows. Uses BigQuery load jobs.

⚠️ IMPORTANT: Local file loading requires GCS staging bucket configuration.

• If you get "The specified bucket does not exist" error, use tables insert for datasets <1000 rows instead
• For larger datasets, upload to GCS first, then use bigquery tables load gs://...

# Load from Cloud Storage URI (RECOMMENDED - no bucket config needed)
bigquery tables load my-project.dataset.users \
  gs://my-bucket/data.csv --format csv

# Load from local CSV file (requires GCS staging bucket configured)
bigquery tables load my-project.dataset.users data.csv --format csv

# Load with schema auto-detection
bigquery tables load my-project.dataset.new_table data.csv \
  --format csv --autodetect

# Load with replace write disposition (truncates table first)
bigquery tables load my-project.dataset.users data.csv \
  --format csv --write-disposition replace

# Load JSON file
bigquery tables load my-project.dataset.events events.json \
  --format json

# Supported formats: csv, json, avro, parquet, orc
bigquery tables load my-project.dataset.table data.parquet \
  --format parquet

# Dry-run validation (no data loaded)
bigquery tables load my-project.dataset.users data.csv \
  --format csv --dry-run

# Allow some bad records (skip up to 100 invalid rows)
bigquery tables load my-project.dataset.users data.csv \
  --format csv --max-bad-records 100

# Ignore unknown fields
bigquery tables load my-project.dataset.users data.csv \
  --format csv --ignore-unknown

# Skip confirmation prompts (for automation/CI)
bigquery tables load my-project.dataset.users data.csv \
  --format csv --write-disposition replace --yes

Load Job Features:

• GCS Staging Bucket Required: Local file loading needs GCS bucket configuration
• Real-time progress tracking with exponential backoff
• Automatic cleanup of temporary files after completion
• Write modes: append (default) or replace (truncate first)
• Safety confirmations for destructive operations
• Configurable error tolerance with --max-bad-records

When to Use:

• Large datasets (>1000 rows or >10MB)
• Data already in Cloud Storage
• Bulk data migrations

When NOT to Use:

• Small datasets (<1000 rows) → Use tables insert instead (no GCS required)
• Don't have GCS staging bucket configured → Use tables insert or upload to GCS first

Load Options:

• --format <FORMAT>: csv, json, avro, parquet, orc (default: csv)
• --write-disposition <DISPOSITION>: append or replace (default: append)
• --autodetect: Auto-detect schema from source files
• --dry-run: Validate without loading
• --max-bad-records <N>: Maximum bad records before failing
• --ignore-unknown: Ignore unknown fields
• --yes: Skip confirmation prompts

Extracting Data

Export table data to Cloud Storage in various formats:

# Extract table to Cloud Storage as CSV
bigquery tables extract my-project.dataset.users \
  gs://my-bucket/exports/users.csv --format csv

# Extract as JSON
bigquery tables extract my-project.dataset.events \
  gs://my-bucket/exports/events-*.json --format json

# Extract with compression
bigquery tables extract my-project.dataset.large_table \
  gs://my-bucket/exports/data-*.csv.gz --format csv --compression gzip

# Extract as Avro with Snappy compression
bigquery tables extract my-project.dataset.events \
  gs://my-bucket/exports/events-*.avro --format avro --compression snappy

# Extract as Parquet
bigquery tables extract my-project.dataset.analytics \
  gs://my-bucket/exports/analytics.parquet --format parquet

# CSV with custom delimiter and header
bigquery tables extract my-project.dataset.data \
  gs://my-bucket/data.csv \
  --format csv \
  --field-delimiter "|" \
  --print-header

# Dry-run to validate configuration
bigquery tables extract my-project.dataset.users \
  gs://my-bucket/users.csv --format csv --dry-run

# Skip confirmation prompt
bigquery tables extract my-project.dataset.large \
  gs://my-bucket/export.csv --format csv --yes

Supported Formats: CSV, JSON (newline-delimited), Avro, Parquet Compression: none, gzip, snappy (Avro/Parquet only)

External Tables

External tables reference data in Cloud Storage without copying it to BigQuery.

Creating External Tables

# Create CSV external table
bigquery tables create-external my-project.dataset.external_table \
  --source-uri gs://bucket/data.csv \
  --format csv \
  --schema "id:INTEGER,name:STRING,created_at:TIMESTAMP"

# Create with auto-detected schema
bigquery tables create-external my-project.dataset.external_table \
  --source-uri gs://bucket/data.csv \
  --format csv \
  --autodetect

# Multiple source URIs (comma-separated)
bigquery tables create-external my-project.dataset.external_table \
  --source-uri "gs://bucket/file1.csv,gs://bucket/file2.csv" \
  --format csv \
  --autodetect

# Multiple source URIs (multiple flags)
bigquery tables create-external my-project.dataset.external_table \
  --source-uri gs://bucket/file1.csv \
  --source-uri gs://bucket/file2.csv \
  --format csv \
  --autodetect

# CSV-specific options
bigquery tables create-external my-project.dataset.external_table \
  --source-uri gs://bucket/data.csv \
  --format csv \
  --schema "id:INTEGER,name:STRING" \
  --field-delimiter "," \
  --skip-leading-rows 1

# Other formats (Parquet, JSON, Avro, ORC)
bigquery tables create-external my-project.dataset.parquet_table \
  --source-uri gs://bucket/data.parquet \
  --format parquet \
  --autodetect

bigquery tables create-external my-project.dataset.json_table \
  --source-uri gs://bucket/data.jsonl \
  --format json \
  --autodetect

External Table Options:

• --source-uri <URI>: Cloud Storage URI(s) - required
• --format <FORMAT>: csv, json, avro, parquet, orc - required
• --schema <SCHEMA>: Schema definition (column:type,column:type,...)
• --autodetect: Auto-detect schema from source files
• --field-delimiter <DELIMITER>: CSV field delimiter (default: ,)
• --skip-leading-rows <N>: CSV header rows to skip

Updating External Tables

# Update source URIs
bigquery tables update-external my-project.dataset.external_table \
  --source-uri gs://bucket/new-data.csv

# Update schema
bigquery tables update-external my-project.dataset.external_table \
  --schema "id:INTEGER,name:STRING,email:STRING"

# Update CSV options
bigquery tables update-external my-project.dataset.external_table \
  --field-delimiter "|" \
  --skip-leading-rows 2

# Update multiple properties
bigquery tables update-external my-project.dataset.external_table \
  --source-uri gs://bucket/new-data.csv \
  --schema "id:INTEGER,name:STRING,updated_at:TIMESTAMP" \
  --skip-leading-rows 1

Template System

Named query templates allow you to save frequently-used queries with parameter placeholders.

Listing Templates

# List all available templates (text format)
bigquery templates list

# JSON output
bigquery templates list --format json

# Shows:
# - Template name
# - Description
# - Parameters
# - Query preview

Searching Templates

# Search by name or description
bigquery templates search "customer"
bigquery templates search "daily metrics"

# JSON output
bigquery templates search "analytics" --format json

Validating Templates

# Validate template for parameter consistency
bigquery templates validate my-template

# Checks:
# - Parameter definitions match query placeholders
# - Required parameters are defined
# - Parameter types are valid

Running Templates

# Run template with default parameters
bigquery templates run my-template

# Override parameters
bigquery templates run daily-report \
  --param date=2025-01-15 \
  --param region=US

# Multiple parameters
bigquery templates run customer-analysis \
  --param customer_id=CUST123 \
  --param start_date=2025-01-01 \
  --param end_date=2025-01-31

# JSON output
bigquery templates run my-template --format json

# Skip cost confirmation
bigquery templates run expensive-query --yes

Template Run Options:

• --param <KEY=VALUE>: Parameter override (can be used multiple times)
• --format <FORMAT>: Output format (json or text, default: json)
• --yes: Skip cost confirmation prompt

Template Workflow Example

# 1. Search for templates
bigquery templates search "revenue"

# 2. Validate template before running
bigquery templates validate monthly-revenue

# 3. Run with parameters
bigquery templates run monthly-revenue \
  --param month=2025-01 \
  --param min_amount=1000

# 4. Run in automation (skip confirmation)
bigquery templates run monthly-revenue \
  --param month=2025-01 \
  --yes \
  --format json > output.json

Use templates for:

• Standardized reporting queries
• Common analytics patterns
• Scheduled data pipelines
• Team query sharing
• Reducing query errors

MCP Server Integration

The BigQuery MCP server provides semantic search and natural language query capabilities via Model Context Protocol.

Starting MCP Server

STDIO Mode (for local clients):

# Start MCP server in stdio mode
bigquery mcp stdio

# Server will:
# - Accept MCP protocol messages on stdin
# - Send responses on stdout
# - Expose BigQuery tools to MCP clients

HTTP Mode (for network clients):

# Start HTTP MCP server on default port 8080
bigquery mcp http

# Specify custom port
bigquery mcp http --port 3000

# Server provides:
# - HTTP endpoint for MCP protocol
# - JSON-RPC over HTTP
# - Remote access to BigQuery tools

MCP Server Capabilities

The MCP server exposes these tools through the Model Context Protocol:

1. semantic_search: Search tables using natural language
2. execute_query: Run SQL queries with automatic formatting
3. get_schema: Retrieve table schemas
4. list_tables: List available tables
5. list_datasets: List available datasets
6. explain_query: Get query execution plan
7. optimize_query: Suggest query optimizations
8. run_template: Execute named templates with parameters

MCP Configuration

Configure in Claude Code or other MCP-enabled applications:

STDIO Mode (.claude/mcp.json or similar):

{
  "mcpServers": {
    "bigquery": {
      "command": "bigquery",
      "args": ["mcp", "stdio"],
      "env": {
        "GOOGLE_CLOUD_PROJECT": "my-project"
      }
    }
  }
}

HTTP Mode:

{
  "mcpServers": {
    "bigquery": {
      "url": "http://localhost:8080",
      "transport": "http"
    }
  }
}

MCP Usage Patterns

When using BigQuery MCP through clients:

Semantic Search:

"Find all tables containing customer purchase data from the last 30 days"
→ MCP translates to appropriate SQL query

Schema Discovery:

"What columns are in the analytics.events table?"
→ MCP returns schema information

Natural Language Queries:

"Show me total revenue by region for Q1 2025"
→ MCP generates and executes SQL

Template Execution:

"Run the monthly revenue template for January 2025"
→ MCP executes template with parameters

LSP Integration

The BigQuery LSP provides SQL language features in text editors.

Starting LSP Server

# Start LSP server
bigquery lsp

# Server provides:
# - Language Server Protocol communication
# - SQL syntax validation
# - Schema-aware completions
# - Query formatting
# - Hover documentation

LSP Features

• SQL syntax highlighting: Proper tokenization and highlighting
• Schema completion: Table and column suggestions based on project schema
• Query validation: Real-time syntax and semantic checks
• Hover documentation: Table and column info on hover
• Go to definition: Navigate to table definitions
• Query formatting: Auto-format SQL queries
• Diagnostics: Show errors and warnings inline

Editor Configuration

Neovim:

-- In nvim/lua/bigquery-lsp.lua or init.lua
vim.api.nvim_create_autocmd("FileType", {
  pattern = { "sql", "bq", "bigquery" },
  callback = function()
    vim.lsp.start({
      name = "bigquery-lsp",
      cmd = { "bigquery", "lsp" },
      root_dir = vim.fn.getcwd(),
    })
  end,
})

VS Code (in settings.json or language server config):

{
  "bigquery-lsp": {
    "command": "bigquery",
    "args": ["lsp"],
    "filetypes": ["sql", "bq", "bigquery"]
  }
}

Helix (in languages.toml):

[[language]]
name = "sql"
language-servers = ["bigquery-lsp"]

[language-server.bigquery-lsp]
command = "bigquery"
args = ["lsp"]

Common Workflows

Workflow 1: Exploratory Data Analysis

# 1. Verify authentication
bigquery auth check

# 2. List available datasets
bigquery datasets list my-project

# 3. List tables in dataset
bigquery tables list my-project.analytics

# 4. Check table schema
bigquery tables describe my-project.analytics.events

# 5. Preview data (text format for readability)
bigquery query --format text \
  "SELECT * FROM my-project.analytics.events LIMIT 10"

# 6. Get row count
bigquery query "SELECT COUNT(*) as total FROM my-project.analytics.events"

# 7. Check data distribution
bigquery query --format text "
  SELECT
    DATE(timestamp) as date,
    COUNT(*) as events
  FROM my-project.analytics.events
  GROUP BY date
  ORDER BY date DESC
  LIMIT 30
"

Workflow 2: Cost-Aware Query Development

# 1. Dry run to estimate cost
bigquery dry-run "
  SELECT *
  FROM my-project.large_dataset.table
  WHERE date >= '2025-01-01'
"

# 2. If cost is acceptable, run query
bigquery query "
  SELECT *
  FROM my-project.large_dataset.table
  WHERE date >= '2025-01-01'
"

# 3. For automation, skip confirmation
bigquery query --yes "
  SELECT *
  FROM my-project.large_dataset.table
  WHERE date >= '2025-01-01'
" > results.json

Workflow 3: Template-Based Reporting

# 1. Search for relevant templates
bigquery templates search "daily"

# 2. Validate template
bigquery templates validate daily-metrics

# 3. Run template with parameters
bigquery templates run daily-metrics \
  --param date=$(date +%Y-%m-%d) \
  --param region=US \
  --format json > daily-report.json

# 4. Schedule in cron or CI/CD
# 0 1 * * * bigquery templates run daily-metrics --param date=$(date +%Y-%m-%d) --yes

Workflow 4: External Data Analysis

# 1. Create external table pointing to GCS
bigquery tables create-external my-project.staging.raw_logs \
  --source-uri gs://logs-bucket/2025-01-*.json \
  --format json \
  --autodetect

# 2. Query external table
bigquery query "
  SELECT
    timestamp,
    user_id,
    action
  FROM my-project.staging.raw_logs
  WHERE action = 'purchase'
  LIMIT 100
"

# 3. Update external table when new files arrive
bigquery tables update-external my-project.staging.raw_logs \
  --source-uri gs://logs-bucket/2025-02-*.json

Workflow 5: Data Loading Pipeline

# 1. Load initial data
bigquery tables load my-project.dataset.events \
  gs://bucket/events-2025-01-01.csv \
  --format csv \
  --write-disposition replace

# 2. Append incremental data
bigquery tables load my-project.dataset.events \
  gs://bucket/events-2025-01-02.csv \
  --format csv \
  --write-disposition append

# 3. Verify data loaded
bigquery query "
  SELECT
    DATE(timestamp) as date,
    COUNT(*) as count
  FROM my-project.dataset.events
  GROUP BY date
  ORDER BY date
"

Workflow 6: Real-Time Data Insertion

# 1. Insert single event (inline JSON)
bigquery tables insert my-project.dataset.events \
  --json '{"user_id": "U123", "event": "click", "timestamp": "2025-01-15T10:00:00Z"}'

# 2. Stream JSONL from application
my-app --output jsonl | bigquery tables insert my-project.dataset.events --data - --format json

# 3. Insert batch from JSONL file
bigquery tables insert my-project.dataset.events \
  --data events.jsonl --format json

# 4. Stream with transformation and error handling
cat raw_events.json | jq -c '.events[]' | \
  bigquery tables insert my-project.dataset.events \
    --data - --format json \
    --skip-invalid \
    --ignore-unknown

Best Practices

Query Development

1. Always dry-run first: Use bigquery dry-run to estimate costs
2. Use templates: Create templates for repeated queries
3. Validate before running: Check syntax and cost before execution
4. Use text format for exploration: --format text for human-readable tables
5. Use JSON for automation: --format json for machine processing
6. Skip confirmations in scripts: Use --yes flag for automation

Cost Management

1. Dry run expensive queries: Always estimate with bigquery dry-run
2. Monitor bytes processed: Check query cost estimates before running
3. Use partition pruning: Filter on partitioned columns in WHERE clauses
4. Limit result sets: Use LIMIT for exploratory queries
5. Use templates: Standardize queries to avoid mistakes
6. Leverage external tables: Avoid copying data when querying directly from GCS

Authentication

1. Check auth first: Run bigquery auth check before operations
2. Use service accounts: For automation and CI/CD
3. Verify scopes: Ensure all required BigQuery scopes are granted
4. Re-authenticate when needed: bigquery auth login if check fails

Template Management

1. Use descriptive names: Make templates easy to find
2. Document parameters: Include parameter descriptions in templates
3. Validate before use: Run bigquery templates validate before execution
4. Search before creating: Check if similar template exists
5. Version control templates: Store template definitions in git

Data Loading

1. Choose the right method:
   • Use insert for <1000 rows (streaming insert API, immediate availability)
   • Use load for >10MB files or >1000 rows (load jobs with GCS upload)
2. Use JSONL for streaming: Newline-delimited JSON is ideal for streaming pipelines
3. Stream from stdin: Use --data - to pipe data from applications or transformations
4. Validate before loading: Use --dry-run flag to test configurations
5. Handle bad records: Set --max-bad-records for messy data
6. Choose write disposition: replace for full refresh, append for incremental
7. Use external tables: For data that changes frequently in GCS (no data copying)
8. Use appropriate formats: CSV for simple data, JSON/JSONL for complex, Parquet/Avro for large datasets

MCP Server

1. Use stdio for local: Prefer stdio mode for local MCP clients
2. Use HTTP for remote: Use HTTP mode for networked deployments
3. Secure HTTP endpoints: Put HTTP server behind authentication/firewall
4. Monitor server logs: Check for errors and performance issues
5. Set appropriate port: Choose non-conflicting port for HTTP mode

LSP Integration

1. Configure per-project: Set up LSP for SQL files in your editor
2. Use schema completion: Leverage auto-complete for table/column names
3. Check diagnostics: Fix errors and warnings shown inline
4. Format queries: Use LSP formatting for consistent style

Configuration

Environment Variables

# Set default project
export GOOGLE_CLOUD_PROJECT=my-project

# Set credentials (for service accounts)
export GOOGLE_APPLICATION_CREDENTIALS=/path/to/service-account.json

# Add to ~/.zshrc or ~/.bashrc for persistence
echo 'export GOOGLE_CLOUD_PROJECT=my-project' >> ~/.zshrc

Authentication Methods

User Credentials (interactive):

bigquery auth login
# Opens browser for Google authentication

Service Account (automation):

export GOOGLE_APPLICATION_CREDENTIALS=/path/to/sa-key.json
bigquery auth check

Application Default Credentials (gcloud):

gcloud auth application-default login
bigquery auth check

Troubleshooting

Issue: "Not authenticated" or "Permission denied"

Solution: Check authentication and scopes

# Check current auth status
bigquery auth check

# Re-authenticate if needed
bigquery auth login

# Verify gcloud is set to correct project
gcloud config get-value project

# Set project if needed
gcloud config set project my-project

Issue: "Table not found"

Solution: Use fully qualified table names

# Wrong - missing project/dataset
bigquery query "SELECT * FROM table"

# Correct - fully qualified
bigquery query "SELECT * FROM my-project.my-dataset.my-table"

# Or use backticks for reserved words
bigquery query "SELECT * FROM \`my-project.my-dataset.my-table\`"

Issue: "Query too expensive"

Solution: Check cost with dry-run and optimize

# Check estimated cost
bigquery dry-run "SELECT * FROM large_table WHERE date >= '2025-01-01'"

# Optimize with partition filters
bigquery dry-run "
  SELECT * FROM large_table
  WHERE _PARTITIONDATE = '2025-01-15'
"

Issue: "Template not found"

Solution: Search for templates and verify name

# List all templates
bigquery templates list

# Search for template
bigquery templates search "keyword"

# Use exact template name
bigquery templates run exact-template-name

Issue: "The specified bucket does not exist"

Cause: bigquery tables load with a local file requires GCS staging bucket configuration.

Solutions:

1. Preferred for small datasets (<1000 rows): Use tables insert instead (no GCS required)

   bigquery tables insert my-project.dataset.table \
     --data /tmp/data.jsonl \
     --format json
   

2. For larger datasets: Upload to GCS first, then load

   gsutil cp /tmp/large-file.jsonl gs://my-bucket/
   bigquery tables load my-project.dataset.table \
     gs://my-bucket/large-file.jsonl \
     --format json
   

3. Last resort: Configure GCS staging bucket in BigQuery CLI config (requires additional setup)

Issue: "Invalid schema"

Solution: Check schema format for external tables

# Schema format: column:type,column:type,...
bigquery tables create-external my-project.dataset.table \
  --source-uri gs://bucket/file.csv \
  --format csv \
  --schema "id:INTEGER,name:STRING,created_at:TIMESTAMP"

# Or use autodetect
bigquery tables create-external my-project.dataset.table \
  --source-uri gs://bucket/file.csv \
  --format csv \
  --autodetect

Issue: "MCP server not responding"

Solution: Check server mode and connectivity

# For stdio mode, ensure client is using stdio transport
bigquery mcp stdio

# For HTTP mode, check port and firewall
bigquery mcp http --port 8080

# Test HTTP endpoint
curl http://localhost:8080

Issue: "LSP not starting in editor"

Solution: Verify LSP configuration and binary path

# Check bigquery is in PATH
which bigquery

# Test LSP manually
bigquery lsp

# Verify editor configuration points to correct command
# Neovim: check cmd = { "bigquery", "lsp" }
# VS Code: check "command": "bigquery", "args": ["lsp"]

Quick Reference

# Authentication
bigquery auth check                          # Check auth status
bigquery auth login                          # Login with gcloud

# Queries
bigquery query "SELECT ..."                  # Execute query
bigquery query --yes "SELECT ..."            # Skip confirmation
bigquery query --format text "SELECT ..."    # Table output
bigquery dry-run "SELECT ..."                # Estimate cost

# Datasets
bigquery datasets list PROJECT               # List datasets

# Tables
bigquery tables list PROJECT.DATASET                          # List tables
bigquery tables describe PROJECT.DATASET.TABLE               # Show schema
bigquery tables insert TABLE --json '{"id": 1}'              # Insert rows (inline)
bigquery tables insert TABLE --data file.jsonl --format json # Insert from JSONL
cat data.jsonl | bigquery tables insert TABLE --data -       # Stream insert
bigquery tables load TABLE file.csv                          # Load data (bulk)
bigquery tables load TABLE gs://bucket/file.csv              # Load from GCS
bigquery tables extract TABLE gs://bucket/output.csv         # Extract to GCS
bigquery tables create-external TABLE --source-uri ...       # External table
bigquery tables update-external TABLE --source-uri ...       # Update external

# Templates
bigquery templates list                              # List templates
bigquery templates search "keyword"                  # Search templates
bigquery templates validate TEMPLATE                 # Validate template
bigquery templates run TEMPLATE --param key=value    # Run template

# MCP Server
bigquery mcp stdio                   # Start MCP (stdio mode)
bigquery mcp http                    # Start MCP (HTTP mode)
bigquery mcp http --port 3000        # Custom port

# LSP Server
bigquery lsp                         # Start LSP server

Integration Examples

CI/CD Pipeline

#!/bin/bash
# daily-etl.sh

# Authenticate with service account
export GOOGLE_APPLICATION_CREDENTIALS=/secrets/sa-key.json
bigquery auth check || exit 1

# Run daily ETL template
bigquery templates run daily-etl \
  --param date=$(date +%Y-%m-%d) \
  --yes \
  --format json > /tmp/etl-result.json

# Check result
if [ $? -eq 0 ]; then
  echo "ETL completed successfully"
else
  echo "ETL failed"
  exit 1
fi

Data Quality Checks

#!/bin/bash
# check-data-quality.sh

# Run data quality template
RESULT=$(bigquery templates run data-quality-check \
  --param table=my-project.dataset.table \
  --yes \
  --format json)

# Parse result and check quality metrics
INVALID_ROWS=$(echo $RESULT | jq '.invalid_rows')

if [ "$INVALID_ROWS" -gt 100 ]; then
  echo "Data quality check failed: $INVALID_ROWS invalid rows"
  exit 1
else
  echo "Data quality check passed"
fi

Scheduled Reporting

#!/bin/bash
# generate-report.sh

# Generate weekly report
bigquery templates run weekly-revenue-report \
  --param week_start=$(date -d "last monday" +%Y-%m-%d) \
  --param week_end=$(date -d "next sunday" +%Y-%m-%d) \
  --yes \
  --format json > /reports/weekly-$(date +%Y-%m-%d).json

# Upload to GCS
gsutil cp /reports/weekly-*.json gs://reports-bucket/

Summary

Primary commands:

• bigquery auth {check,login} - Authentication management
• bigquery query - Execute SQL with cost awareness
• bigquery dry-run - Estimate query costs
• bigquery datasets list - List datasets
• bigquery tables {list,describe,insert,load,extract,create-external,update-external} - Table operations
• bigquery templates {list,search,validate,run} - Named templates
• bigquery mcp {stdio,http} - MCP server modes
• bigquery lsp - LSP server

Key features:

• Cost-aware query execution with confirmation prompts
• Named query templates with parameter substitution
• Streaming insert API for real-time data (<1000 rows)
• Bulk load jobs for large datasets (>10MB or >1000 rows)
• JSONL streaming support with stdin (--data -)
• Data extraction to Cloud Storage (CSV, JSON, Avro, Parquet)
• External table support for GCS data
• MCP server with stdio and HTTP modes
• LSP integration for editor support

Best practices:

• Always check authentication first with auth check
• Use dry-run to estimate costs before expensive queries
• Create templates for frequently-used queries
• Use --yes flag for automation and CI/CD
• Use insert for <1000 rows, load for larger datasets
• Use JSONL format for streaming pipelines
• Stream from stdin with --data - for data transformations
• Use external tables to avoid data duplication
• Configure MCP for natural language query capabilities
• Set up LSP in your editor for SQL development

MCP Integration:

• Semantic search across datasets
• Natural language to SQL translation
• Schema discovery and exploration
• Template execution via MCP tools
• Available in both stdio and HTTP modes