How it works
ChartMogul writes the data as Parquet files to a Google Cloud Storage bucket you own, then loads them into its own BigQuery dataset, with a prefix and location you choose. Each load replaces the previous tables.
Connecting. You create a Google Cloud service account with the BigQuery Job User and BigQuery Data Owner roles, give it access to the bucket, and upload its JSON key to ChartMogul. You need to be an Admin in ChartMogul.
Before you connect
- Use a separate bucket if you already send ChartMogul data to Google Cloud Storage.
- You can restrict access to ChartMogul's IP addresses with VPC Service Controls.
Setting it up
In ChartMogul, go to Settings > Destinations and add BigQuery. The BigQuery guide walks through each step.
What ChartMogul exports
Every warehouse and storage destination gets the same two sets of tables.
Your source data
Customers, custom attributes, tags, contacts and their custom attributes, invoices and line items, transactions, subscription events, plans, opportunities and their custom attributes, notes and call logs, tasks, currency rates, and data sources.
ChartMogul’s calculations
Customers with their MRR, ARR, and status, subscriptions, MRR and CMRR movements, MRR per customer per month, MRR and quantity per customer per month per plan, MRR intervals, and plan groups.
When it runs
Daily, weekly, or monthly, and you can also send data on demand. Field-level schemas are in the developer docs.