GPT Answer-
Certainly! To achieve this, you can follow these steps using Azure Data Factory:
Lookup Activity to Retrieve Table Names:
- Create a Lookup activity in your pipeline that executes the query
SELECT TABLE_NAME FROM all_tables WHERE owner = 'Schema_Name'
.- The output of this lookup activity will be a list of table names.
- After the Lookup activity, use a **Set Variable activity** to store the output of the lookup (i.e., the list of table names) in a pipeline variable. - Set the variable value to `@{activity('Lookup1').output.value}` (replace `'Lookup1'` with the actual name of your Lookup activity). **Create a CSV File**: - In your Azure Data Lake Storage (ADLS) Gen2, create a dummy CSV file (e.g., `table_names.csv`) with a single column (e.g., `TableName`).
Copy Data Activity:
- Use a Copy Data activity to write the table names from the pipeline variable into the CSV file.
- Configure the source dataset to use the pipeline variable (the list of table names).
- Set the sink dataset to point to the ADLS Gen2 location where you want to store the CSV file (
table_names.csv
).
- In the Copy Data activity, map the source column (table names) to the sink column (`TableName` in the CSV file). - Ensure that the schema of the CSV file matches the data type of the table names. **Execute the Pipeline**: - Trigger your pipeline to execute. - The Copy Data activity will write the table names to the specified CSV file in ADLS Gen2.
- Set the sink dataset to point to the ADLS Gen2 location where you want to store the CSV file (
Remember to replace placeholders like 'Schema_Name'
, 'Lookup1'
, and adjust the dataset configurations according to your specific setup. This approach allows you to dynamically retrieve table names and store them in a CSV file for each schema in ADLS Gen21234.