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Introduction to loading, transforming, and exporting data | BigQuery

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This document describes the data integration approaches to load and transform data in BigQuery using the extract, load, and transform (ELT) or the extract, transform, load (ETL) processes. It also describes exporting data from BigQuery to apply insights in other systems, known as reverse ETL.

It's common to transform your data before or after loading it into BigQuery. A fundamental decision is whether to transform the data before loading it into BigQuery (extract-transform-load or ETL approach) or load the raw data into BigQuery and perform transformations using BigQuery (extract-load-transform or ELT approach).

The following chart shows the various options for data integration into BigQuery - either using ELT or ETL.

In general, we recommend the ELT approach to most customers. The ELT workflow splits the complex data integration in two manageable parts - extract & load, then transform. Users can choose from a variety data load methods that suit their needs. Once their data is loaded into BigQuery, users familiar with SQL can develop transformation pipelines with tools such as Dataform.

The following sections describe each workflow in further detail.

It's common to transform your data before or after loading it into BigQuery. The two common approaches to data integration, ETL and ELT, are described in the following sections.

With the extract-load-transform (ELT) approach, you perform data integration in two discrete steps:

• Extract and load data
• Transform data

For example, you can extract and load data from a JSON file source into a BigQuery table. Then, you can use pipelines to extract and transform fields into target tables.

The ELT approach can simplify your data integration workflow in the following ways:

• Eliminates the need for other data processing tools
• Splits the often complex data integration process into two manageable parts
• Fully utilizes BigQuery's capabilities to prepare, transform, and optimize your data at scale

In the ELT data integration approach, you extract data from a data source and load it into BigQuery using any of the supported methods of loading or accessing external data.

After loading the data into BigQuery, you can prepare and transform the data with the following tools:

• To collaboratively build, test, document, and schedule advanced SQL data transformation pipelines, use Dataform.
• For smaller data transformation workflows executing SQL code, Python notebooks, or data preparations on a schedule, use BigQuery pipelines.
• To clean your data for analysis, use AI-augmented data preparation.

Each of these tools is powered by the Dataform API.

For more information, see Introduction to transformations.

In the extract-transform-load (ETL) approach, you extract and transform data before it reaches BigQuery. This approach is beneficial if you have an existing process in place for data transformation, or if you aim to reduce resource usage in BigQuery.

Cloud Data Fusion can help facilitate your ETL process. BigQuery also works with 3rd-party partners that transform and load data into BigQuery.

After you process and analyze data in BigQuery, you can export the results to apply them in other systems. BigQuery supports the following exports:

• Exporting query results to a local file, Google Drive, Google Sheets
• Exporting tables or query results to Cloud Storage, Bigtable, Spanner, and Pub/Sub

This process is referred to as reverse ETL.

For more information, see Introduction to data export in BigQuery.

• Learn more about loading data in BigQuery.
• Learn more about transforming data in BigQuery.
• Learn more about exporting data in BigQuery.

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.

Last updated 2025-08-07 UTC.

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