06:00 UTC
Shopify
Selected accounts
Configured job
Snowflake
Destination table
Join marketing, finance, CRM, and product data inside the same warehouse.
Metrics availables
Dimensions availables
Shopify
3 advertising accounts
Connected
Google Ads
2 advertising accounts
Connected
Google Analytics 4
1 web property
Connected
HubSpot
1 CRM portal
Connected
Add the account host, user, and database schema. Use a password for compatible users or a PEM private key for service accounts and stricter policies.
Choose the fields, initial history, partition field, and recurring schedule for the job. Catchr refreshes the configured import window in your PostgreSQL table.
This example summarizes revenue, orders, units sold, and average order value by sales channel.
Each connector has its own entities, fields, historical limits, and reporting uses. Open a source page for the details that belong to that platform.
Answers about PostgreSQL setup, available source data, schedules, table updates, and SQL use cases.
Connect Shopify as a Catchr source. Add your existing Snowflake database, create a datastream, and configure a job that writes the selected fields to the "customers" table.
Select concrete Shopify fields such as Average Total Sales, Cost of Goods Sold, Discount, and Day of the month. You can rename the chosen columns, preview the result, and write them to Snowflake tables such as stores, customers, and products.
Catchr first processes the historical range configured for Shopify. Later runs update the "customers" table using the saved frequency and rolling import window.
Catchr uses the selected partition field to refresh the relevant Shopify period inside "customers". Leaving it blank changes the job to append-only behavior.
With Shopify data, a Snowflake query can summarize revenue, orders, units sold, and average order value by sales channel. A Snowflake view comparing Average Total Sales and Cost of Goods Sold can feed agency dashboards or downstream BI models.
Yes. Connect an existing Snowflake database first. The Catchr job creates and populates the destination table you configure inside that database.
Yes. Create a separate datastream and job for each source, write each export to its own table, then combine the data downstream with SQL, views, or your BI tool.
Our teams is always here to responds to any question you could have about our data connector.
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