Data Overview
When you connect a MySQL database to Coupler.io, you're exporting data directly from a table or view. The structure and content of the exported data depends entirely on your MySQL schema.
What gets exported
You select a single table or view, and Coupler.io exports all rows and columns from that table (unless you apply filters). Each column in your MySQL table becomes a column in your destination spreadsheet, database table, or AI destination.
Supported data types
MySQL's common data types are fully supported:
INT, BIGINT, FLOAT, DECIMAL
42, 3.14
Numbers
VARCHAR, CHAR, TEXT
"Hello World"
Text
DATE, DATETIME, TIMESTAMP
2024-01-15, 2024-01-15 10:30:00
Date/time
BOOLEAN
TRUE, FALSE
1 or 0
JSON
{"key": "value"}
Text (JSON string)
Filtering and transformations
You can apply filters directly in Coupler.io to export only the rows you need — for example, orders from the last 30 days, or customers in a specific region. Use the date picker to set dynamic date ranges that refresh with each run.
If you need to combine data from multiple MySQL tables, use Join or Append transformations:
Join — connect a customers table with an orders table using a shared ID
Append — stack data from multiple similar tables into one export
Common export scenarios
Export a customer list to Google Sheets — create a live spreadsheet that updates daily with new customer records, automatically filtered by status or signup date.
Send sales data to BigQuery — schedule hourly exports of your orders table to BigQuery for advanced analytics and historical tracking.
Analyze data in Claude — export your metrics table and send it directly to Claude for instant analysis and insights via the AI destination.
Monitor performance in Looker Studio — export a metrics table and connect it to a Looker Studio dashboard that refreshes daily.
Use cases by role
Export raw transaction data — send your complete transactions table to BigQuery or a data warehouse for deeper analysis, filtering by date range or transaction type.
Build live dashboards — export aggregated metrics from MySQL to Google Sheets, then connect them to Looker Studio for real-time monitoring.
Combine multiple data sources — join your MySQL customer table with transaction data, then append results from a secondary database for a unified view.
Track campaign performance — export your campaigns table daily to Google Sheets, filtered by status or date range, for quick performance reviews.
Send lead lists to Claude — export your leads table directly to Claude to analyze lead quality, identify high-value segments, or draft outreach messaging.
Monitor subscriber growth — export your subscribers table to Looker Studio and watch subscription trends update daily.
Automate reporting — schedule daily exports of your key metrics table to Excel or Google Sheets for stakeholder reports.
Track inventory — export your inventory table hourly to monitor stock levels and alert the team to low-stock items.
Audit database changes — export your audit log or activity table regularly to maintain compliance and track system changes.
Export financial data — send your transactions, invoices, or account tables to BigQuery or Excel for reconciliation and reporting.
Analyze trends — export historical data (filtered by date) and send it to Claude for instant trend analysis and forecasting insights.
Consolidate data — join multiple finance tables (revenues, expenses, payables) and append data from different business units into one unified export.
Platform-specific notes
AWS RDS — whitelist Coupler.io IPs in your security group's inbound rules (port 3306, TCP)
Google Cloud SQL — add Coupler.io IPs to your authorized networks list
Azure Database for MySQL — configure firewall rules to allow Coupler.io's IPs
DigitalOcean Managed Databases — add Coupler.io IPs to your firewall
Hosted providers (Bluehost, SiteGround, etc.) — contact support to whitelist IPs, or ask your provider how to enable remote database access
Views — Coupler.io supports exporting from views as well as base tables
Large tables — tables with millions of rows may require splitting into smaller date ranges or using filters to avoid timeouts
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