---
title: "Google BigQuery Source"
slug: "google-bigquery-source"
description: "Easy data replication with Dataddo. Replicate data in just a few steps with our secure data pipelines. Send data from Google BigQuery to any destination."
tags: ["Database as a source", "How-to guide", "Data source"]
updated: 2024-04-01T14:43:45Z
published: 2024-04-01T14:43:45Z
---

> ## Documentation Index
> Fetch the complete documentation index at: https://docs.dataddo.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Google BigQuery as a Source

**Google BigQuery** is a cloud-based data warehouse and analytics platform provided by Google Cloud. It uses a columnar storage format, which allows for fast query performance and efficient data compression, making it well-suited for handling large-scale analytical workloads on vast amounts of data.

## Authorize the Connection to Google Big Query

In order to connect Google BigQuery as a ***data source***, you will need to first [authorize the connection to your Google BigQuery](/docs/google-bigquery#authorize-connection-to-google-bigquery).

## Create a Google BigQuery Data Source

1. On the **Sources** page, click on the [**Create Source**](https://app.dataddo.com/sources/new) button and select the connector from the list.
2. From the drop-down menu, choose your **account**.
          **Didn't find your account?**

          

Click on **Add new Account** at the bottom of the drop-down and follow the on-screen prompts. You can also go to the **Authorizers** tab and click on [**Add New Service**](https://app.dataddo.com/service/new).
3. **Name** your data source and select your
  1. **Google BigQuery account**
  2. **Project**
  3. **Dataset**
  4. **Table**
  5. **Columns**
4. On the **Configuration** tab, decide if you want to **Use Read Session Mode**. (For more information on the difference see [the section on Limitations](/docs/google-bigquery-source#read-session-mode-limitations).)
  1. **Yes**: An auto-generated SQL statement will be prepared for you. You can use a `WHERE` clause to limit the data.
  2. **No**: Fill in your [custom SQL statement](/docs/google-bigquery-source#sql-statement-examples).
5. Configure your **snapshotting preferences**. Choose your [**sync frequency**](https://docs.dataddo.com/docs/extraction#extraction-frequency) or the exact synchronization time under **Show advanced settings**.
          **DATADDO TIP**

          

If you need to **load historical data**, please refer to the [**Data Backfilling**](https://docs.dataddo.com/docs/data-backfilling) article.
6. Preview your data by clicking on the **Test Data** button in the top right corner. You can **adjust the date range** for a more specific time frame.
7. Click on **Save** and **congratulations, your new data source is ready!**

          **WARNING**

          

Dataddo relies on your query statements to accurately retrieve the right data. By choosing specific statements, you guide Dataddo's actions on your data.

Every command in your query statement directly affects your database. **Special attention is required when using commands like`DELETE` or `UPDATE`**, which are more impactful than a simple `SELECT`.

### SQL Statement Examples

1. Extract **all data** from the `clients` table:

```
SELECT * FROM clients
```
2. Extract the **name, email, company** columns from the `clients` table:

```
SELECT name,email,company FROM clients
```
3. Extract **data from specific rows (rows 240,000 to 800,000)** from **all columns** from the `clients` table:

```
SELECT * FROM clients  
LIMIT 2400000,  
800000
```
4. Extract **all data from columns for a specific country** from the `clients` table:

```
SELECT * FROM clients WHERE countryCode = 'USA'
```
5. Extract **all data** from **all columns** if a **job title contains a specific position** from the `clients` table:

```
SELECT * FROM clients WHERE jobTitle = '%Manager%' OR company= 'Dataddo'
```

---

## Limitations

### Read Session Mode Limitations

If you decide to use an auto-generated SQL statement, a `READ SESSION` mode will be applied, this implies the following advantages and disadvantages.

| Advantages | Disadvantages |
| --- | --- |
| **Speed**: Up to 5 times faster data extraction as large amounts of data are read in a highly efficient manner. | **No complex queries**: Since you can only specify a `WHERE` clause, more complex operations (e.g. joins, aggregations, and subqueries) won't be possible. |
| **Limit data**: Specify a `WHERE` clause to limit data to a specific date range or extract records with specific values in a column. | **Limitation with views**: As a 'view' is essentially a saved SQL query (aka it generates the data on-the-fly), it can't be executed a `READ SESSION` mode. |

Please keep these trade-offs in mind when deciding between using an auto-generated or a custom SQL query.

## Troubleshooting

### Context Deadline Exceeded Error

**ERROR CODE**

```
rpc error: code = DeadlineExceeded desc = context deadline exceeded
```

This issue may be caused by extracting data over an **extended timeframe**. Use `WHERE` or `LIMIT` clauses in your SQL query to manage the size and scope of the data extraction.

1. Use `WHERE` to specify the date range.

```
SELECT * FROM your_table
WHERE date_column BETWEEN '202X-01-01' AND '202X-01-31';
```
2. Use `LIMIT` to specify the specify the maximum number of records to return.

```
SELECT * FROM your_table
LIMIT 1000;
```
3. Combine `WHERE` and `LIMIT` for more precise control. The query in this example will return the first 1000 records where the date is after January 1, 202X.

```
SELECT * FROM your_table
WHERE date_column > '202X-01-01'
LIMIT 1000;
```

## Related Articles

Now that you have successfully created a ***data source***, see how you can connect your data to a dashboarding app or a data storage.

**Sending Data to Dashboarding Apps**

- [Simple Data Integration to Dashboards](https://docs.dataddo.com/docs/simple-data-integration-to-dashboards)
- [Data Backfilling to Dashboarding Apps](https://docs.dataddo.com/docs/data-backfilling-to-dashboarding-apps)

**Sending Data to Data Storages**

- [Batch Ingestion to Data Warehouses](https://docs.dataddo.com/docs/ingestion-to-data-warehouses)
- [Data Backfilling to Storages](https://docs.dataddo.com/docs/data-backfilling-to-storages)

**Other Resources**

- [Troubleshooting](https://docs.dataddo.com/docs/troubleshooting)
- [Extraction Logs](https://docs.dataddo.com/docs/extraction-logs)
- [Data Duplication](https://docs.dataddo.com/docs/data-duplication)