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Join marketing, finance, CRM, and product data inside the same warehouse.
Metrics availables
Dimensions availables
Enter the Azure SQL server host, database, and login details. Catchr verifies the destination before any marketing data is exported.
Choose the fields, initial history, partition field, and recurring schedule for the job. Catchr refreshes the configured import window in your AzureSQL table.
This example summarizes pipeline value, created deals, won deals, and win rate by owner.
Open the dedicated route for each platform. Its field catalog, reporting grain, historical constraints, and warehouse use cases should be specific to that source.
Answers about AzureSQL setup, available source data, schedules, table updates, and SQL use cases.
Start with a connected Zoho CRM source and an existing Azure SQL database. Create a datastream between them, then configure the accounts, fields, schedule, and "deals" table.
Build the Zoho CRM schema from available records such as accounts, contacts, companies, and leads, plus the supported account and date dimensions. Preview the selected columns before the Azure SQL job is saved.
Set an initial fetch period for Zoho CRM, then choose the frequency and lookback window for the job writing to "deals". Catchr rewrites that selected period on each run.
The partition field tells Catchr which Zoho CRM rows in "deals" belong to the period being refreshed. If you omit it, every run appends records rather than updating them.
With Zoho CRM data, an Azure SQL query can summarize pipeline value, created deals, won deals, and win rate by owner. A query built around accounts and contacts 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.
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