Launch configuration and furthermore

Learn how to extract, transform and load Google BigQuery data using the Tables to Tables operation.

⚙️ How it works

When a Tables to Tables Tailer data operation is triggered by an event (for example a Storage to Tables data operation successful run) or scheduled to start:

• A number of workflow tasks (SQL queries and JSON table creation/copy tasks) are run in the order set in the task_dependencies parameter of the data operation configuration file.

• You obtain one or several BigQuery tables containing the reorganized data.

📋 How to deploy a Tables to Tables data operation

1. Access your tailer folder (created during installation).

2. Create a working folder as you want.

3. Create SQL and JSON files corresponding to your workflow tasks.

4. Prepare your JSON configuration file to gather all this information. Refer to this page to learn about all its parameters.

5. Determine how to launch your Tables to Tables data operation: either use the schedule_interval parameter in the JSON configuration file, and/or create a Workflow configuration file that will define how to trigger it.

6. Access your working folder by running the following command:

   cd "[path to your working folder]"

7. To deploy the data operation, run the following command:

   tailer deploy your-file.json

8. Log in to Tailer Studio to check the status and details of your data operation.

9. For your workflow to be executed, you either need to run the data operation corresponding to the previous step of your data pipeline (per your Workflow configuration file), or to launch it manually from Tailer Studio.

10. Access your output table(s) in BigQuery to check the result of the data operation.

💡Modify scripts for other use cases

The subject taken with the Iowa dataset aggregates all sales from a year at each iteration to account for new sales by the week. Here are some examples to better address a need or use case:

• Proceed to a daily aggregation with a daily CRON trigger after receiving the sales file (applicable by the week, month or quarter)

• Modify the script to calculate only the current week and avoid calculating old dates

• Link the output data table to a datastudio visualization