Can you route the same mainframe dataset to AWS, Google Cloud, Azure, and IBM Cloud at once? VirtualZ Computing CTO Vince Re explains how PropelZ — VirtualZ's no-code tool for moving and replicating IBM Z mainframe data to the cloud — sends a single dataset to multiple cloud platforms simultaneously from one configuration, so enterprises use each provider's strengths instead of locking into one.

Vince Re makes the case for treating multi-cloud as a mainframe data strategy rather than a vendor decision: distributing the same z/OS data across providers for resilience, geographic compliance, and cost flexibility, without building and maintaining a separate integration for each cloud.

In this episode of Skyward Data, the podcast from VirtualZ Computing:

- How to route one mainframe dataset to AWS, Azure, Google Cloud, and IBM Cloud simultaneously from a single PropelZ configuration - Geographic distribution that optimizes performance while meeting regional compliance requirements - Cost optimization by matching workloads to the best pricing across providers - Real applications: cross-continent disaster recovery and per-cloud analytics, such as Google Cloud for analytics, Azure for enterprise integration, and AWS for scale - Moving from "choosing a cloud" to orchestrating several, without managing separate pipelines

PropelZ writes mainframe data directly to Amazon S3, Azure Blob, and Google Cloud Storage with no code and no homegrown pipelines, and is proven at 56,000 records per second, mainframe to cloud. It's part of VirtualZ Computing's no-code portfolio for enterprise mainframe data, alongside Lozen (live in-place data access), FlowZ (cloud storage for backup and archive), and Zaac (cloud and SAN as native z/OS storage). Topics: multi-cloud mainframe strategy, IBM Z, z/OS, mainframe data replication, AWS, Azure, Google Cloud, IBM Cloud, disaster recovery, no-code data integration, hybrid cloud.

Listen to more Skyward Data episodes: https://virtualzcomputing.com/podcasts/