This page gives you an overview of the ways in which Mainframe Connector supports your data migration, and the advantages of each approach.
You can run Mainframe Connector in the following configurations based on your requirements:
- Transcode mainframe data locally on the mainframe, and then migrate it to Google Cloud.
- Transcode mainframe data on Google Cloud using Cloud Run.
- Transcode mainframe data on Google Cloud in standalone mode using Cloud Run.
- Transfer mainframe data to Cloud Storage using a virtual tape library (VTL), and then transcode the data on Google Cloud.
The following sections discuss these configurations in detail.
Move locally transcoded mainframe data to Google Cloud
You can transcode mainframe data locally on the mainframe to the Optimized Row Columnar (ORC) format, which is supported by BigQuery. In this configuration, Mainframe Connector helps you manage a complete extract, transform, and load (ETL) pipeline entirely from IBM z/OS, as shown in the following figure.
For more information, see Move data transcoded locally on the mainframe to Google Cloud.
Transcode mainframe data remotely on Google Cloud using Cloud Run
Transcoding data locally on a mainframe is a CPU-intensive process that results in high million instructions per second (MIPS) consumption. To avoid this, you can delegate the transcoding of mainframe data to a Cloud Run service on Google Cloud, as shown in the following figure. This frees up your mainframe for business critical tasks and also reduces MIPS consumption.
For more information, see Transcode mainframe data remotely on Google Cloud.
Run Mainframe Connector in standalone mode
Mainframe Connector version 5.13.0 and later supports running Mainframe Connector as a standalone job on Google Cloud. This feature lets you run Mainframe Connector as a containerized batch job, for example, as a Cloud Run job, Google Kubernetes Engine job, or within a Docker container. This option helps you avoid installing Mainframe Connector locally on your mainframe, and makes it easier for you to integrate your Mainframe queued sequential access method (QSAM) file parsing to existing extract, transform, and load (ETL) workflows.
When you use the standalone version of the Mainframe Connector, you must set up the ETL workflow that loads the QSAM file to Google Cloud by yourself. For more information, see Run Mainframe Connector in standalone mode.
Transcode mainframe data moved to Google Cloud using a virtual tape library
If you want to transfer very large volumes of data (around 500+ GB daily) to Google Cloud, and don't want to use your mainframe for this effort, you can deploy a hardware device in your data center to transfer data directly from the mainframe storage system to Cloud Storage using a VTL and 10G ethernet. As the hardware device receives the data directly from the mainframe storage system using a VTL, the data transfer process between the mainframe and Cloud Storage doesn't use the mainframe at all, thereby freeing it up for business critical tasks. Data transcoding is performed by a Cloud Run service on Google Cloud, as shown in the following figure.
For more information, see Transcode mainframe data moved to Google Cloud using virtual tape library.