[[["易于理解","easyToUnderstand","thumb-up"],["解决了我的问题","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["很难理解","hardToUnderstand","thumb-down"],["信息或示例代码不正确","incorrectInformationOrSampleCode","thumb-down"],["没有我需要的信息/示例","missingTheInformationSamplesINeed","thumb-down"],["翻译问题","translationIssue","thumb-down"],["其他","otherDown","thumb-down"]],["最后更新时间 (UTC)：2025-09-03。"],[],[],null,["# Choose your data migration journey\n\nThis page gives you an overview of the ways in which Mainframe Connector\nsupports your data migration, and the advantages of each approach.\n\nYou can run Mainframe Connector in the following configurations based\non your requirements:\n\n- [Transcode mainframe data locally on the mainframe, and then migrate it to Google Cloud](#local-transcoding).\n- [Transcode mainframe data on Google Cloud using Cloud Run](#remote-transcoding).\n- [Transcode mainframe data on Google Cloud in standalone mode using Cloud Run](#standalone-transcoding).\n- [Transfer mainframe data to Cloud Storage using a virtual tape library (VTL), and then transcode the data on Google Cloud](#use-vtl).\n\nThe following sections discuss these configurations in detail.\n\nMove locally transcoded mainframe data to Google Cloud\n------------------------------------------------------\n\nYou can transcode mainframe data locally on the mainframe to the\n[Optimized Row Columnar (ORC)](https://orc.apache.org/) format, which is\nsupported by BigQuery. In this configuration, Mainframe Connector\nhelps you manage a complete [extract, transform, and load (ETL)](https://en.wikipedia.org/wiki/Extract,_transform,_load) pipeline entirely from [IBM z/OS](https://www.ibm.com/products/zos),\nas shown in the following figure.\nLocal transcoding\n\nFor more information, see [Move data transcoded locally on the mainframe to Google Cloud](/mainframe-connector/docs/local-transcoding).\n\nTranscode mainframe data remotely on Google Cloud using Cloud Run\n-----------------------------------------------------------------\n\nTranscoding data locally on a mainframe is a CPU-intensive process that results\nin high million instructions per second (MIPS) consumption. To avoid this, you\ncan delegate the transcoding of mainframe data to a Cloud Run service\non Google Cloud, as shown in the following figure. This frees up your mainframe\nfor business critical tasks and also reduces MIPS consumption.\nRemote transcoding\n\n\u003cbr /\u003e\n\nFor more information, see [Transcode mainframe data remotely on Google Cloud](/mainframe-connector/docs/remote-transcoding).\n\nRun Mainframe Connector in standalone mode\n------------------------------------------\n\nMainframe Connector version 5.13.0 and later supports running\nMainframe Connector as a standalone job on Google Cloud. This feature\nlets you run Mainframe Connector as a containerized batch job,\nfor example, as a Cloud Run job, Google Kubernetes Engine job, or within a\nDocker container. This option helps you avoid installing\nMainframe Connector locally on your mainframe, and makes it easier for\nyou to integrate your Mainframe queued sequential access method (QSAM) file\nparsing to existing extract, transform, and load (ETL) workflows.\n\nWhen you use the standalone version of the Mainframe Connector, you\nmust set up the ETL workflow that loads the QSAM file to Google Cloud by\nyourself. For more information, see [Run Mainframe Connector in standalone mode](/mainframe-connector/docs/standalone-mode).\n\nTranscode mainframe data moved to Google Cloud using a virtual tape library\n---------------------------------------------------------------------------\n\nIf you want to transfer very large volumes of data (around 500+ GB daily) to\nGoogle Cloud, and don't want to use your mainframe for this effort, you can\ndeploy a hardware device in your data center to transfer data directly from the\nmainframe storage system to Cloud Storage using a VTL and 10G ethernet. As\nthe hardware device receives the data directly from the mainframe storage system\nusing a VTL, the data transfer process between the mainframe and Cloud Storage\ndoesn't use the mainframe at all, thereby freeing it up for business critical\ntasks. Data transcoding is performed by a Cloud Run service on\nGoogle Cloud, as shown in the following figure.\nUsing VTL connection to move mainframe data to Google Cloud\n\nFor more information, see [Transcode mainframe data moved to Google Cloud using virtual tape library](/mainframe-connector/docs/vtl-transcoding).\n\nWhat's Next\n-----------\n\n- [Mainframe Connector architecture](/mainframe-connector/docs/architecture)\n- [Get started with Mainframe Connector](/mainframe-connector/docs/get-started)"]]
