[[["容易理解","easyToUnderstand","thumb-up"],["確實解決了我的問題","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["難以理解","hardToUnderstand","thumb-down"],["資訊或程式碼範例有誤","incorrectInformationOrSampleCode","thumb-down"],["缺少我需要的資訊/範例","missingTheInformationSamplesINeed","thumb-down"],["翻譯問題","translationIssue","thumb-down"],["其他","otherDown","thumb-down"]],["上次更新時間：2025-08-17 (世界標準時間)。"],[[["\u003cp\u003eThis document outlines data integration methods for BigQuery, including extract, load, and transform (ELT) and extract, transform, and load (ETL).\u003c/p\u003e\n"],["\u003cp\u003eThe ELT approach involves extracting and loading data into BigQuery, then transforming it within the platform, and it eliminates the need for other data processing tools.\u003c/p\u003e\n"],["\u003cp\u003eThe ETL approach involves transforming data before loading it into BigQuery, which is useful if you have existing data transformation processes, or to reduce BigQuery resource usage.\u003c/p\u003e\n"],["\u003cp\u003eBigQuery allows for data to be exported after processing to other systems, which is known as reverse ETL, and supports several methods of export.\u003c/p\u003e\n"],["\u003cp\u003eBigQuery provides tools such as Dataform, workflows, and data preparation for transforming data.\u003c/p\u003e\n"]]],[],null,["# Introduction to loading, transforming, and exporting data\n=========================================================\n\nThis document describes the data integration approaches to load and transform\ndata in BigQuery using the extract, load, and transform (ELT) or the\nextract, transform, load (ETL) processes. It also describes exporting data from\nBigQuery to apply insights in other systems, known as *reverse\nETL*.\n\nDeciding between ELT or ETL\n---------------------------\n\nIt's common to transform your data before or after loading it into\nBigQuery. A fundamental decision is whether to transform the\ndata before loading it into BigQuery (extract-transform-load or ETL\napproach) or load the raw data into BigQuery and perform transformations using\nBigQuery (extract-load-transform or ELT approach).\n\nThe following chart shows the various options for data integration into\nBigQuery - either using ELT or ETL.\n\nIn general, we recommend the ELT approach to most customers. The ELT workflow\nsplits the complex data integration in two manageable parts - extract \\& load, then\ntransform. Users can choose from a variety data load methods\nthat suit their needs. Once their data is loaded into BigQuery,\nusers familiar with SQL can develop transformation pipelines with tools such as\nDataform.\n\nThe following sections describe each workflow in further detail.\n\nLoading and transforming data\n-----------------------------\n\nIt's common to transform your data before or after loading it into\nBigQuery. The two common approaches to data integration, ETL and\nELT, are described in the following sections.\n\n### ELT data integration approach\n\nWith the extract-load-transform (ELT) approach, you perform data integration in\ntwo discrete steps:\n\n- Extract and load data\n- Transform data\n\nFor example, you can extract and load data from a JSON file source into a\nBigQuery table. Then, you can use pipelines to extract and\ntransform fields into target tables.\n\nThe ELT approach can simplify your data integration workflow in the following\nways:\n\n- Eliminates the need for other data processing tools\n- Splits the often complex data integration process into two manageable parts\n- Fully utilizes BigQuery's capabilities to prepare, transform, and optimize your data at scale\n\n#### Extracting and loading data\n\nIn the ELT data integration approach, you extract data from a data source and\nload it into BigQuery using any of the supported\n[methods of loading or accessing external data](/bigquery/docs/loading-data#methods).\n\n#### Transforming data in BigQuery\n\nAfter loading the data into BigQuery, you can prepare and\ntransform the data with the following tools:\n\n- To collaboratively build, test, document, and schedule advanced SQL data transformation pipelines, use [Dataform](/dataform/docs).\n- For smaller data transformation workflows executing SQL code, Python notebooks, or data preparations on a schedule, use [BigQuery pipelines](/bigquery/docs/pipelines-introduction).\n- To clean your data for analysis, use AI-augmented [data preparation](/bigquery/docs/data-prep-introduction).\n\nEach of these tools is powered by the\n[Dataform API](/dataform/reference/rest).\n\nFor more information, see\n[Introduction to transformations](/bigquery/docs/transform-intro).\n\n### ETL data integration approach\n\nIn the extract-transform-load (ETL) approach, you extract and\ntransform data before it reaches BigQuery. This approach is\nbeneficial if you have an existing process in place for data transformation, or\nif you aim to reduce resource usage in BigQuery.\n\n[Cloud Data Fusion](/data-fusion/docs/concepts/overview) can help facilitate your ETL process. BigQuery also works with\n[3rd-party partners that transform and load data into BigQuery](/bigquery/docs/bigquery-ready-partners#etl-data-integration).\n\nExporting data\n--------------\n\nAfter you process and analyze data in BigQuery, you can export\nthe results to apply them in other systems. BigQuery supports the\nfollowing exports:\n\n- Exporting query results to a local file, Google Drive, Google Sheets\n- Exporting tables or query results to Cloud Storage, Bigtable, Spanner, and Pub/Sub\n\nThis process is referred to as reverse ETL.\n\nFor more information, see [Introduction to data export in BigQuery](/bigquery/docs/export-intro).\n\nWhat's next\n-----------\n\n- Learn more about [loading data in BigQuery](/bigquery/docs/loading-data).\n- Learn more about [transforming data in BigQuery](/bigquery/docs/transform-intro).\n- Learn more about [exporting data in BigQuery](/bigquery/docs/export-intro)."]]
