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Join marketing, finance, CRM, and product data inside the same warehouse.
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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 impressions, reach, engagements, and engagement rate by post.
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.
Create the TikTok source, then register Azure SQL with its server host, database name, username, and password. Link both through a datastream and target the "profiles" table in the job.
Select concrete TikTok fields such as User's followers count, User's Following count, User Like count, and Day of the month. You can rename the chosen columns, preview the result, and write them to Azure SQL tables such as accounts, profiles, and posts.
Configure the initial TikTok period separately from the recurring schedule for "profiles". The source API determines how much history is available.
Choose an eligible date field as the partition for TikTok. Catchr can then replace the matching period in "profiles"; without it, later runs append rows and may create duplicates.
With TikTok data, an Azure SQL query can summarize impressions, reach, engagements, and engagement rate by post. Use User's followers count and User's Following count in an Azure SQL view to create a reusable reporting layer for client analysis.
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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