Connect Exchange Rates Api to BigQuery
Send Exchange Rates Api exchange rates to BigQuery on a schedule. Refresh as often as every 15 minutes.
BigQuery
dataset exchange_rates
exchange_rates
How to connect Exchange Rates Api and BigQuery
5 steps. You can preview the Exchange Rates Api data in Coupler.io before anything reaches BigQuery.
Step 1 of 5
Connect Exchange Rates Api
Enter your Exchange Rates Api access key. You only do this once per account.
Step 2 of 5
Choose what to export
Pick the Exchange Rates Api data you want in BigQuery.
Step 3 of 5
Fine-tune the export
Optional settings for this Exchange Rates Api export.
Step 4 of 5
Connect BigQuery
Upload the JSON key file from your Google Cloud service account, then enter the dataset and table for the Exchange Rates Api data.
Step 5 of 5
Choose a write mode and schedule
Every run either replaces or appends the data in BigQuery. Run it as often as every 15 minutes.
Each run overwrites exchange_rates with fresh Exchange Rates Api data.
Coupler.io made it easy to create dashboards and reports from different data sources. The sync is reliable and the user experience is amazing.
Take your data analytics to the next level
Sign Up with Google
Sign Up with MICROSOFT
SIGN UP WITH EMAIL
By signing up to Coupler.io, you agree to our Privacy Policy and Terms of Use.
Build your Exchange Rates Api integrations with these destinations
Get the most of Exchange Rates Api to BigQuery integration
Gather data
Understand data
Automate data
Act on data
1 data type, including Exchange rates.
With your Exchange Rates Api access key, entered once when you add the connection.
Yes. If the table doesn't exist in the dataset you choose, Coupler.io creates it on the first run and fills it with Exchange Rates Api data.
Refresh the source field list in Coupler.io and select the new column. If you define the BigQuery table schema manually, add the column there too.
On a schedule you choose, from monthly down to every 15 minutes, depending on your Coupler.io plan.
Yes. Add more sources to the same data flow, for example QuickBooks or Xero, and join or append them before the data reaches BigQuery.
