Data Overview

With BigQuery as a source in Coupler.io, you have complete flexibility to query any data in your BigQuery datasets using standard SQL. Unlike pre-built data sources with fixed entities, BigQuery lets you write custom queries to extract exactly what you need.

How BigQuery works in Coupler.io

You provide a SQL query, and Coupler.io runs it against your BigQuery datasets and returns the results. The data structure depends entirely on your SQL — you control which tables, columns, and rows are included.

Example queries

Basic table export:

SELECT * FROM project_id.dataset_name.table_name

Filtered and aggregated data:

SELECT
  DATE(order_date) as date,
  product_category,
  COUNT(*) as order_count,
  SUM(revenue) as total_revenue
FROM project_id.dataset_name.orders
WHERE order_date >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
GROUP BY date, product_category
ORDER BY date DESC

Joining multiple tables:

SELECT
  o.order_id,
  c.customer_name,
  p.product_name,
  o.quantity,
  o.order_date
FROM project_id.dataset_name.orders o
JOIN project_id.dataset_name.customers c ON o.customer_id = c.customer_id
JOIN project_id.dataset_name.products p ON o.product_id = p.product_id
WHERE o.order_date >= CURRENT_DATE()

Common metric combinations

Here are patterns you might combine in a single query:

• Sales metrics — Order count, revenue, average order value, customer acquisition cost
• Inventory metrics — Stock levels by warehouse, reorder points, turnover rates
• Performance metrics — Page views, conversion rates, user retention, churn rate
• Financial metrics — Gross profit, COGS, margin percentage, cash flow

Use cases by role

Marketers

Campaign performance reporting — Query your marketing database to pull conversion rates, cost per acquisition, and ROI by campaign. Export daily or weekly summaries to Google Sheets for stakeholder reviews.

Audience segmentation — Use SQL to segment customers by behavior, demographics, or purchase history, then sync the results to a sheet for email list management or ad audience uploads.

Attribution tracking — Join customer interactions across multiple tables (web events, email opens, social clicks) to build attribution reports that feed into Looker Studio dashboards.

Finance teams

Budget vs. actual reporting — Query budget data and actual spend from separate BigQuery tables, join them, and automatically export variance reports to Google Sheets each month.

Consolidated P&L statements — Aggregate revenue, COGS, and operating expenses across business units using GROUP BY, then push to a master financial sheet.

Cash flow forecasting — Pull historical payment and receivables data, calculate aging buckets with CASE statements, and export to a rolling forecast sheet.

Sales operations

Pipeline tracking — Query deal data by stage and rep, calculate win rates and average deal size, and sync weekly to a sheet for sales leaders.

Territory performance — Join sales rep, account, and revenue data to build territory P&L reports; export to Looker Studio for real-time visibility.

Quota vs. actual — Pull YTD sales by rep against quota targets, calculate attainment %, and automatically email summaries via ChatGPT-generated insights.

Platform-specific notes

• SQL dialect — BigQuery uses standard SQL with some Google-specific functions. View the full reference.
• Project and dataset references — Always include your project ID and dataset name in table references: project_id.dataset_name.table_name
• Query limits — Large queries may take time to run. Consider using LIMIT clauses during testing and filtering by date ranges in production queries.
• Permissions — Your Service Account must have read access to all datasets referenced in your query. If a query fails with "Access Denied", verify the Service Account has the BigQuery Data Viewer role on that dataset.
• Billing — BigQuery charges for bytes scanned. Optimize queries by selecting only needed columns and filtering early to reduce costs.
• Nested data — BigQuery supports nested/repeated fields. You may need to use UNNEST() to flatten arrays in your queries.