How to load data from Snowflake to BigQuery

Learn how to use Airbyte to synchronize your Snowflake data into BigQuery within minutes.

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Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Snowflake connector in Airbyte

Connect to Snowflake or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up BigQuery for your extracted Snowflake data

Select BigQuery where you want to import data from your Snowflake source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the Snowflake to BigQuery in Airbyte

This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.

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How to Sync Snowflake to BigQuery Manually

1. Log in to Snowflake:

Use the Snowflake web interface or connect through a client tool using your credentials.

2. Select the Data to Export:

Determine which tables or data you want to export from Snowflake.

3. Export the Data to a File:

  • Use the `COPY INTO <location>` command to export the data to a file format supported by both Snowflake and BigQuery, such as CSV or Parquet.
  • Choose a staging area that Snowflake has access to, such as an Amazon S3 bucket, Azure Blob Storage, or Google Cloud Storage (GCS).

Example command to export data to a CSV file in a GCS bucket:

```sql

COPY INTO 'gcs://<your-bucket-name>/path/to/export/data.csv'

FROM <your_table>

FILE_FORMAT = (TYPE = 'CSV' FIELD_DELIMITER = ',' SKIP_HEADER = 1);

```

1. Set Up Google Cloud Storage:

If you haven't already, create a GCS bucket where you will temporarily store the exported data.

2. Configure Permissions:

Ensure that you have the necessary permissions to read and write to the GCS bucket.

Transfer Files from Snowflake Staging Area to GCS:

   - If you exported the data directly to GCS, you could skip this step.

   - If the data is in a different location, use `gsutil` or the Google Cloud Console to transfer the files to your GCS bucket.

Example using `gsutil`:

```bash

gsutil cp s3://<your-s3-bucket>/path/to/export/*.csv gs://<your-gcs-bucket>/path/to/import/

```

1. Access BigQuery:

Open the BigQuery console or use the `bq` command-line tool to interact with BigQuery.

2. Create a Dataset:

If you don't have an existing dataset, create one where you will import the data.

3. Load Data into BigQuery:

Use the BigQuery Data Transfer Service or the `bq load` command to import the data from the GCS bucket into your BigQuery dataset.

Example using the `bq load` command:

```bash

bq load --source_format=CSV <your_dataset>.<your_table> gs://<your-gcs-bucket>/path/to/import/data.csv

```

4. Verify the Import:

Confirm that the data has been imported correctly into BigQuery by querying the tables.

1. Remove Temporary Files:

Delete the exported data files from the GCS bucket if they are no longer needed to avoid additional storage costs.

2. Review Security Settings:

Ensure that any temporary access permissions granted for the transfer are revoked.

How to Sync Snowflake to BigQuery Manually - Method 2:

FAQs

ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.

Snowflake Data Cloud is a cloud-based data warehousing and analytics platform that allows organizations to store, manage, and analyze large amounts of data in a secure and scalable manner. It provides a single, integrated platform for data storage, processing, and analysis, eliminating the need for multiple tools and systems. Snowflake Data Cloud is built on a unique architecture that separates compute and storage, allowing users to scale up or down as needed without affecting performance. It also offers a range of features such as data sharing, data governance, and machine learning capabilities, making it a comprehensive solution for modern data management and analytics.

Snowflake Data Cloud provides access to a wide range of data types, including:

1. Structured Data: This includes data that is organized in a specific format, such as tables, columns, and rows. Examples of structured data include customer information, financial data, and inventory records.
2. Semi-Structured Data: This type of data is partially organized and may not fit into a traditional relational database structure. Examples of semi-structured data include JSON, XML, and CSV files.
3. Unstructured Data: This includes data that does not have a specific format or organization, such as text documents, images, and videos.
4. Time-Series Data: This type of data is organized based on time stamps and is commonly used in industries such as finance, healthcare, and manufacturing.
5. Geospatial Data: This includes data that is related to geographic locations, such as maps, GPS coordinates, and satellite imagery.
6. Machine Learning Data: This type of data is used to train machine learning models and includes features and labels that are used to predict outcomes.

Overall, Snowflake Data Cloud provides access to a wide range of data types, making it a versatile tool for data analysis and management.

This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps: 
1. Set up Snowflake Data Cloud to BigQuery as a source connector (using Auth, or usually an API key)
2. Choose a destination (more than 50 available destination databases, data warehouses or lakes) to sync data too and set it up as a destination connector
3. Define which data you want to transfer from Snowflake Data Cloud to BigQuery and how frequently
You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud. 

ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.

ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.

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