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
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On this page
  • What is Table to Storage?
  • ✅ Supported file types
  • Databases
  • Export files
  • ⚙️ How it works
  • 📋 How to deploy a Table to Storage data operation

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  1. Data Pipeline Operations

Export data with Tables to Storage

Learn how to export data located in a BigQuery table into CSV/JSON files using a Table to Storage operation.

PreviousTable to Table SQL and DDL filesNext[V3] Table to Storage configuration file

Last updated 1 year ago

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What is Table to Storage?

A Table to Storage (TTS) data pipeline operation allows you to export your data from a BigQuery table to a CSV/JSON file in a Google Cloud Storage bucket so you can leverage them with other tools, such as a warehouse management system.

✅ Supported file types

Databases

  • Google BigQuery

Export files

  • CSV file in Google Cloud Storage

  • JSON file in Google Cloud Storage

Please note that all BigQuery export limits apply to table-to-storage data operations. See BigQuery for more information.

In particular:

  • You can export up to 1 GB of table data to a single file. If you are exporting more than 1 GB of data, the data is loaded into multiple files. When you export data to multiple files, the size of the files will vary.

  • When you export data in format, (integer) data types are encoded as JSON strings to preserve 64-bit precision when the data is read by other systems.

  • Lorsque vous exportez une table au format JSON, les symboles <, > et & sont convertis à l'aide de la notation Unicode \uNNNN, où N est un chiffre hexadécimal. Par exemple, profit&loss devient profit\u0026loss. Cette conversion Unicode est effectuée pour éviter les failles de sécurité.

⚙️ How it works

When the Table to Storage Tailer workflow is triggered by an event (usually a BigQuery table update):

  • The SQL query you specify will be executed to extract the relevant data from the source BigQuery table.

  • The data will be exported to a CSV file or a JSON file located in the GCS bucket you specified.

📋 How to deploy a Table to Storage data operation

  1. Create a working folder as you want, and create a JSON file for your data operation inside.

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

    cd "[path to your working folder]"
  3. To deploy the data operation, run the following command:

    tailer deploy configuration your-configuration.json
  4. Access the GCS bucket to check your output file (CSV or JSON).

Access your tailer folder (created during ).

Create a to determine what data to extract.

Prepare your JSON configuration file. Refer to this page to learn about all the .

Create a that will define how to trigger it.

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

For your workflow to be executed, you either need to run the data operation that is set to trigger it in your Workflow data operation (previous step in your data pipeline), or to launch it manually from .

💡
documentation
JSON
INT64
installation
SQL file
parameters
Workflow configuration file
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