# Load data with Storage to Tables

## :bulb: What is Storage to Tables?

A Storage to Tables (STT) data pipeline operation allows you to load data files from a Google Cloud Storage (GCS) bucket into one or several BigQuery databases.

{% hint style="warning" %}
Note that the uniqueness of the configuration is checked against the GCS bucket name AND directory combination. This means that you can have only **one configuration per bucket/directory combination**, as any new configuration will overwrite the previous one.
{% endhint %}

## ✅ Supported file types

### **Source data files**

* CSV and any delimited flat files
* New line delimited JSON files
* These two file types can be compressed using gzip

### **Databases**

* Google BigQuery

## ⚙️ How it works

Every time a new file matching the specified rule appears in a given directory of a Google Cloud Storage bucket:

* it will be removed from the source directory,
* if options have been set accordingly, the file will be copied to an archive directory located in the same storage, inside a folder named with the date contained in the filename,
* the file data will be loaded into a BigQuery table matching its filename template for each database specified.

## 🤖 Automated metadata

Automatic metadata feature will add specific columns during the ingestion process related to the inpput source.

The added columns are:

```
tlr_ingestion_timestamp_utc (TIMESTAMP)
tlr_input_file_source_type (STRING)
tlr_input_file_name (STRING)
tlr_input_file_full_resource_name (STRING)
```

<figure><img src="/files/9dV6V0Ty962zlQLkyLYy" alt=""><figcaption></figcaption></figure>

## **📋 How to deploy a Storage to Tables data operation**

1. Access your **tailer** folder (created during [installation](/getting-started/install-tailer-sdk.md)).
2. Create a working folder as you want, and create a JSON file for your data operation inside.
3. Prepare your JSON configuration file. Refer to this page to learn about all the [parameters](/data-pipeline-operations/load-data-with-storage-to-tables/storage-to-tables-configuration-file.md).
4. Prepare a DDL file for each database table. Refer to this page to learn about all the [parameters](/data-pipeline-operations/load-data-with-storage-to-tables/storage-to-tables-ddl-files.md).
5. Access your working folder by running the following command:

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

   ```
   tailer deploy your-file.json
   ```
7. Log in to [Tailer Studio](https://studio.tailer.ai) to check the status and details of your data operation.
8. Access your output table(s), and archive folder, if any, to check the result of the data operation.


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.tailer.ai/data-pipeline-operations/load-data-with-storage-to-tables.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.