Making Atlassian Cloud Migrations Predictable

Cloud migrations have two sides that both demand serious attention. There is the data transfer itself -- moving issues, pages, attachments, users, and configuration from Data Center to Cloud -- and there is everything that comes before and around it. Neither side is simple.

The Cloud Migration Assistant handles the mechanics of moving data across, and Atlassian continues to invest in this area with programs like FastShift. But "handles the mechanics" does not mean you press a button and walk away. The assistant transfers data. It does not guarantee that every issue, every page, every attachment, every permission grant arrives intact and complete on the other side. Getting to zero data loss -- genuinely zero -- is the migration expert's real job, and it takes hands-on validation at every step. Content that silently fails to transfer, users that lose access because a group mapping broke, attachments that time out on large files, pages where macros render as blank boxes -- these things happen during the transfer itself, not just before or after it.

The other side is the assessment: understanding what you actually have, how tangled the configuration is, what will not migrate automatically, and how much effort the whole thing takes. When an assessment is thorough, the migration becomes predictable. You know the number of workflows that need manual rebuilding. You know which marketplace apps have no Cloud equivalent. You know the real licensed user count, not the inflated one from the wrong database table. You know how many terabytes of attachments are sitting there and which individual files might cause timeouts.

When the assessment is shallow -- which is the default -- the timeline keeps shifting because new things keep turning up, and the validation work balloons because you did not know what to check for.

This guide is about doing the assessment properly. It covers three ways to gather data: direct SQL queries against the Jira or Confluence database, REST API calls you can run from your own machine, and ScriptRunner Groovy scripts you can run from the ScriptRunner console if you have it installed. Between these three approaches, you can extract everything you need without relying on someone else's tooling or waiting for a vendor engagement.

The MAGIC Framework

Before gathering any data, it helps to know what you are looking for. The MAGIC framework organises the assessment into five areas. Each one addresses a different category of risk, and each one can be rated Low, Medium, or High complexity based on the data you collect.