Connect Bigcommerce to Azure SQL

Start with a connected Bigcommerce source and an existing Azure SQL database. Create a datastream between them, then configure the accounts, fields, schedule, and "customers" table.

Select concrete Bigcommerce fields such as Order Products Base Cost Price, Order Products Base Price, Order Products Base Total, and Day of the month. You can rename the chosen columns, preview the result, and write them to Azure SQL tables such as stores, customers, and products.

Set an initial fetch period for Bigcommerce, then choose the frequency and lookback window for the job writing to "customers". Catchr rewrites that selected period on each run.

The partition field tells Catchr which Bigcommerce rows in "customers" belong to the period being refreshed. If you omit it, every run appends records rather than updating them.

With Bigcommerce data, an Azure SQL query can summarize revenue, orders, units sold, and average order value by sales channel. A query built around Order Products Base Cost Price and Order Products Base Price can support trend reviews, account comparisons, and operational reporting.

Yes. Connect an existing Azure SQL  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.