Faster mainframe data movement, proven at enterprise scale
A Fortune 500 retailer needed to move its largest mainframe datasets, some with more than 200 million records, to Google Cloud against a fixed business deadline. The retailer and its partners already owned established data movement tools. The existing approach could not move the data fast enough.
This customer white paper from VirtualZ Computing documents how three organizations evaluated PropelZ against their own technical and operational requirements. Testing showed roughly twice the throughput of the retailer’s existing managed file transfer approach, with every transfer validated for accuracy and completeness. The retailer, the outsourcer, and the integrator each tested it. All three approved it.
What the white paper covers
- Why existing data movement workflows struggled with the retailer’s largest datasets and available batch windows.
- How transfers estimated to take 12–14 hours in the final day before cutover were measured at 2–4 hours with PropelZ.
- How change data capture, combined with other measures, helped the team move about 95% of the data ahead of cutover.
- What each organization needed to validate, including data integrity, cloud delivery options, security, and fit with existing mainframe operations.
For teams evaluating how to move mainframe data to the cloud, this paper provides a practical account of what was tested, what the results showed, and why all three organizations said yes.
