Tailer Documentation
  • What is Tailer Platform?
  • Getting Started
    • Prepare your local environment for Tailer
    • Install Tailer SDK
    • Set up Google Cloud Platform
    • Encrypt your credentials
  • [Tutorial] Create a first data pipeline
    • Introduction
    • Prepare the demonstration environment
    • Copy files from one bucket to another
    • Load files into BigQuery tables
    • Prepare data
    • Build predictions
    • Export data
    • Congratulations!
    • [Video] Automatic Script
      • SQL script file
      • DDL script file
      • Tables to Tables script file
      • Launch configuration and furthermore
  • Data Pipeline Operations
    • Overview
    • Set constants with Context
      • Context configuration file
    • Move files with Storage to Storage
      • Storage to Storage configuration file
    • Load data with Storage to Tables
      • Storage to Tables configuration file
      • Storage to Tables DDL files
    • Stream incoming data with API To Storage
      • API To Storage configuration file
      • API To Storage usage examples
    • Transform data with Tables to Tables
      • Tables to Tables configuration file
      • Table to Table SQL and DDL files
    • Export data with Tables to Storage
      • [V3] Table to Storage configuration file
      • Table to Storage SQL file
      • [V1-V2: deprecated] Table to Storage configuration file
    • Orchestrate processings with Workflow
      • [V2] Workflow configuration file
      • [V1: deprecated] Workflow configuration file
    • Convert XML to CSV
      • Convert XML to CSV configuration file
    • Use advanced features with VM Launcher
      • Process code with VM Launcher
        • VM Launcher configuration file for code processing
      • Encrypt/Decrypt data with VM Launcher
        • VM Launcher configuration file for data encryption
        • VM Launcher configuration file for data decryption
    • Monitoring and Alerting
      • Monitoring and alerting parameters
    • Asserting Data quality with Expectations
      • List of Expectations
    • Modify files with File Utilities
      • Encrypt/Decrypt data with File Utilities
        • Configuration file for data encryption
        • Configuration file for data decryption
    • Transfer data with GBQ to Firestore
      • Table to Storage: configuration file
      • Table to Storage: SQL file
      • VM Launcher: configuration file
      • File-to-firestore python file
  • Tailer Studio
    • Overview
    • Check data operations' details
    • Monitor data operations' status
    • Execute data operations
    • Reset Workflow data operations
    • Archive data operations
    • Add notes to data operations and runs
    • View your data catalog
    • Time your data with freshness
  • Tailer API
    • Overview
    • Getting started
    • API features
  • Release Notes
    • Tailer SDK Stable Releases
    • Tailer Beta Releases
      • Beta features
      • Beta configuration
      • Tailer SDK API
    • Tailer Status
Powered by GitBook
On this page
  • What is Tables to Tables?
  • ✅ Supported databases
  • ⚙️ How it works

Was this helpful?

Edit on GitHub
  1. Data Pipeline Operations

Transform data with Tables to Tables

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

PreviousAPI To Storage usage examplesNextTables to Tables configuration file

Last updated 3 years ago

Was this helpful?

What is Tables to Tables?

A Tables to Tables (TTT) data pipeline operation allows you to automate the execution of one or several BigQuery tasks in order to extract, transform and load data from tables to other tables.

A table can be the source and the destination of the same task.

✅ Supported databases

  • Google BigQuery (source and target)

⚙️ How it works

When a Tables to Tables Tailer data operation is triggered by an event (for example a Storage to Tables data operation success) 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 ).

  2. Create a working folder as you want.

  3. Create SQL and JSON files corresponding to your .

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

  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 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 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 .

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

💡
installation
workflow tasks
parameters
Workflow configuration file
Tailer Studio
Tailer Studio