Connect BigQuery as a governed serverless warehouse source for AI answers. Answerplane understands Standard SQL, nested and repeated fields, and query cost controls so teams can inspect verifiable analytics.
14-day trial / no credit card / 150 governed questions included
Scope Google BigQuery, inspect the plan, and publish governed answers
Provide Google Cloud project ID and service account credentials (JSON key file). Answerplane supports OAuth 2.0 and service account authentication.
Answerplane introspects your BigQuery datasets, tables, views, and understands nested schemas, partitioning, and clustering configurations.
Ask 'What's the conversion rate by channel?' or 'Show me nested user preferences'. Then inspect the optimized BigQuery SQL, estimated cost path, and source context.
Optimized for Google BigQuery's unique capabilities
Builds BigQuery Standard SQL with proper syntax including STRUCT, ARRAY types, nested queries, and BigQuery-specific functions like APPROX_COUNT_DISTINCT.
Plan governed answers over complex nested and repeated fields. Generated SQL uses UNNEST, array functions, and struct field access.
Optimized for BigQuery's serverless, fully-managed architecture. Understands partitioning, clustering, and query cost optimization.
Supports BigQuery's real-time data ingestion and federated queries across Cloud Storage, Cloud SQL, and Bigtable.
See how governed questions become inspectable Google BigQuery plans
"Show top 10 products by revenue"
SELECT
product_name,
SUM(revenue) AS total_revenue,
COUNT(DISTINCT order_id) AS order_count
FROM `project.dataset.orders`
WHERE DATE(order_timestamp) >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
GROUP BY product_name
ORDER BY total_revenue DESC
LIMIT 10;Explanation: BigQuery Standard SQL with backtick-quoted table names and DATE functions
"Unnest array of order items"
SELECT
order_id,
customer_id,
item.product_id,
item.quantity,
item.price
FROM `project.dataset.orders`,
UNNEST(items) AS item
WHERE item.quantity > 5;Explanation: UNNEST to flatten repeated (array) fields in BigQuery
"Query nested struct fields"
SELECT
user_id,
user_info.name AS user_name,
user_info.email AS user_email,
user_info.address.city AS city,
user_info.address.country AS country
FROM `project.dataset.users`
WHERE user_info.address.country = 'US';Explanation: Dot notation for accessing nested STRUCT fields
"Calculate approximate count of distinct users efficiently"
SELECT
DATE(event_timestamp) AS event_date,
event_type,
APPROX_COUNT_DISTINCT(user_id) AS approx_unique_users
FROM `project.dataset.events`
WHERE event_timestamp >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 7 DAY)
GROUP BY event_date, event_type
ORDER BY event_date DESC;Explanation: BigQuery's APPROX_COUNT_DISTINCT for efficient approximate counting
"Use window function for running total"
SELECT
order_date,
daily_revenue,
SUM(daily_revenue) OVER (
ORDER BY order_date
ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
) AS cumulative_revenue
FROM (
SELECT
DATE(order_timestamp) AS order_date,
SUM(order_total) AS daily_revenue
FROM `project.dataset.orders`
GROUP BY order_date
)
ORDER BY order_date;Explanation: Window function for cumulative aggregation
"Create array of distinct values with ARRAY_AGG"
SELECT
customer_id,
customer_name,
ARRAY_AGG(DISTINCT product_category ORDER BY product_category) AS categories_purchased,
COUNT(DISTINCT order_id) AS order_count
FROM `project.dataset.orders`
GROUP BY customer_id, customer_name
HAVING COUNT(DISTINCT product_category) > 1
ORDER BY order_count DESC;Explanation: BigQuery ARRAY_AGG for creating arrays from grouped rows
These are just a few examples. Answerplane keeps the plan, query, and provenance visible before teams save or embed an answer.
Your Google BigQuery data is protected with enterprise-grade security
Read-only BigQuery roles enforced (BigQuery Data Viewer)
Service account credentials encrypted at rest with AES-256
Supports OAuth 2.0 for secure user authentication
Query execution timeouts prevent excessive costs
Respects BigQuery column-level security and row-level security
VPC Service Controls supported for additional isolation
Multi-tenant organization-level isolation
Query history tracked via Cloud Audit Logs
Everything you need to know about using Answerplane with Google BigQuery
Go to Settings > Databases > Add Database, select Google BigQuery, and provide your GCP project ID and service account JSON key file. The service account needs BigQuery Data Viewer role.
Yes. Answerplane understands STRUCT and ARRAY types and builds reviewable BigQuery plans with UNNEST for arrays, dot notation for struct fields, and explicit nested-data handling.
Yes. Answerplane builds BigQuery plans that account for partitioning (_PARTITIONTIME, _PARTITIONDATE) and clustering so cost-sensitive filters are visible before execution.
Yes. You can connect GCP projects with access to BigQuery public datasets and review governed answer plans over those datasets. Useful for demos and data exploration without changing the source model.
Answerplane builds BigQuery plans with partition-aware WHERE clauses, APPROX_COUNT_DISTINCT where appropriate, and explicit column selection instead of SELECT * on large tables.
Yes. If you've created BigQuery external tables pointing to Cloud Storage files such as Parquet, Avro, CSV, or JSON, Answerplane can plan governed answers over them.
Answerplane focuses on standard SQL queries. For BigQuery ML (CREATE MODEL, ML.PREDICT), you may need to write those queries manually as they're specialized.
Yes. We enforce read-only access, encrypt credentials at rest, and rely on your source permissions as the system of record. We do not ingest or replicate source contents wholesale; saved chats, dashboards, exports, uploads, and materialized artifacts are retained only when you use those features.
Yes. Use fully qualified table names (project.dataset.table) in your questions, and Answerplane will draft cross-project plans when your service account has appropriate permissions.
Answerplane can plan answers over BigQuery tables with streaming inserts in real time. There is no special configuration needed beyond the approved source permissions.
Still have questions?
Contact our teamLet teams ask questions, inspect plans, and publish trusted Google BigQuery answers with provenance attached.
14-day trial / no credit card / 150 governed questions included