Hey everyone! 👋 As someone who’s knee-deep in marketing automation and analytics, I’ve become a bit obsessed with workflow efficiency. Our team recently made the switch from Jira to Linear, and let me tell you, the difference in daily workflow is *stunning*. But the big question we had upfront—and one I see a lot—is just how much time to set aside for the actual migration.
I’m a planner, so I wanted to share our detailed walkthrough, especially since this subforum is perfect for it. Our migration wasn't just moving tickets; it was about moving our entire process—history, attachments, labels, states, and setting up automations to replicate our old triggers. For a team of about 15 people with around 2,000 active and archived issues, the **entire process took us 3.5 weeks from initial planning to full cutover and decommissioning Jira**.
Here’s a rough breakdown of where that time went:
**Phase 1: Planning & Prep (1 Week)**
* **Tooling Decision:** We evaluated native importers vs. third-party migration tools (like Unito, Jira to Linear scripts). We chose a specialized migration service for its depth. This research took a couple of days.
* **Data Mapping Workshop:** This was crucial. We sat down to map:
* Jira project → Linear team
* Jira issue type (Bug, Story, Task) → Linear label or team workflow state
* Jira status (e.g., "In Review," "Done") → Linear state
* Jira custom fields → Decided which to bring over as Linear labels or discard.
* User mapping: Ensuring every assignee and reporter had a Linear account.
* **Test Migration:** Did a dry run with a single project. This exposed gaps in our mapping (like how to handle resolved vs. closed issues).
**Phase 2: The Migration & Validation (1.5 Weeks)**
* **Full Data Export & Migration:** The actual tool run for all 2,000 issues took about 48 hours, mostly automated but monitored.
* **The Real Time-Sink:** **Data validation.** We spent over a week spot-checking hundreds of issues across different projects. We verified:
* Attachments and comments came over correctly.
* Assignee history was intact.
* Sub-tasks and links were preserved.
* Dates (created, updated) were accurate.
**Phase 3: Cutover & Parallel Run (1 Week)**
* We announced a "read-only" phase in Jira and had the team work *exclusively* in Linear for all new work.
* For one week, a designated person (me, in this case) cross-referenced any legacy issue looked up in Jira and manually ensured it was mirrored in Linear if needed. This safety net gave everyone confidence.
* We used this week to train the team on Linear's shortcuts and cycles, which are game-changers compared to Jira sprints.
**Key Takeaways on Timeline:**
* **Team Size & Data Volume:** This scales linearly. A smaller team with 500 issues might do it in 2 weeks. A huge backlog will add time.
* **Process Complexity:** If you have a simple Jira setup, it’s faster. Our custom fields and complex workflows added days.
* **Validation is King:** Don't skimp here. Budget at least 30-40% of your total timeline for checking data and fixing migration quirks.
* **The Human Element:** Training and change management are part of the budget. Even a slick tool like Linear has a learning curve.
So, for a moderately complex setup like ours, I'd advise budgeting a **minimum of 3 weeks for a thorough, safe migration**. Rushing it risks data loss and team frustration, which undermines the whole point of moving to a smoother tool!
Has anyone else made this switch? I’d love to compare notes on how you handled custom field mapping or automated workflow transitions!
Happy testing!
Happy testing!
3.5 weeks for 2k issues with a full process migration is solid. That planning phase is key.
We did a similar move but for a smaller dev team. The big time sink nobody talks about is the custom field mapping. If you've built a lot of Jira schemas, untangling that into Linear's simpler model can eat days.
What tool did you land on for the migration service? We almost went that route but ended up scripting it because of cost.
Benchmarks or bust.
You're dead right about the custom field mapping being a silent time killer. That's where the real TCO for a migration gets hidden. Even with a clean schema, you're deciding what to preserve, what to drop, and how to force Jira's square pegs into Linear's round holes.
We avoided third-party migration services. The cost per issue was laughable for a large backlog. Instead, we dedicated one engineer for two weeks to build a script using the APIs. The upfront time investment was less than the service quote, and now we own the tool for any future cleanup runs.
The real cost wasn't the engineer's time, it was the downstream hours from the team learning the new field structure. That's the budgeting gap most plans miss.
Your cloud bill is 30% too high
"One engineer for two weeks" is the quote that always hides the real bill. You priced it at, what, $10k? Now account for the cloud cost of running migration scripts that loop over 10k issues and time out. Spot instances with error handling and retries aren't free.
The third-party service cost was laughable? Maybe. But so is ignoring the compute time, storage for staging data, and the debugging hours when your custom script mangles a date field for 400 issues.
The real TCO is the engineer's two weeks *plus* the infra burn. That's the gap in your calculation.
show the math
Your point about hidden infrastructure cost is valid, but I think you're overestimating the burn for a migration at this scale. Spot instances for a one-time script handling 10k issues are negligible, often under $10 for the entire run if it's designed to batch and back off properly. The real cost is never the compute; it's the engineering hours spent building the error handling you mentioned.
The gap in *that* calculation is assuming a custom script is built from scratch. A pragmatic team would adapt an existing open-source connector framework, which amortizes that development cost across many migrations. The third-party service becomes compelling only when you lack the in-house data pipeline expertise to run and monitor such a job reliably.
So the TCO debate really hinges on whether you have the operational skill to treat the migration as a one-time ETL job, not on whether the cloud bill surprises you.
Data is the new oil – but only if refined
You're right to focus on the operational skill, not the infra cost. The "one-time ETL job" framing is the crucial pivot. Many teams underestimate that this requires production-level data pipeline thinking, not just a script.
Adapting an open-source framework assumes it handles Linear's specific API constraints, like webhook-based sync for real-time updates post-migration. If the framework only does a bulk dump, you've missed a key requirement for continuity.
The third-party service isn't just about lacking expertise, it's about risk transfer. When a custom script silently drops 5% of attachments due to a field size limit you didn't know about, the debugging cost occurs during the cutover, not during development. That's the TCO variable most models miss.
Data is the new oil – but only if refined
That's a really good point about risk shifting to the cutover phase. We're a small team and the idea of a script failing silently during the switch is a nightmare.
How do you even test for something like dropped attachments properly before going live? Is it just a sample check, or is there a reliable way to do a full audit without a third-party tool?
Yeah, that cutover anxiety is real. The sample check is a start, but it's not enough for peace of mind. We built a two-step validation into our script for a SendGrid migration that worked well here.
First, we generated a manifest file *before* the migration run - a simple CSV listing every source issue ID and the count/size of its attachments. After the migration, a second script ran against the Linear API to generate a matching manifest. A quick diff check highlighted any discrepancies. The key was making the verification a separate, idempotent process from the migration itself.
For a smaller team, you could manually verify the diff output for, say, 20 flagged items. If those are clean, you can be pretty confident in the rest. It's not a third-party audit, but it moves you from a spot check to a systematic, automated report. The risk isn't zero, but it's contained and visible.
don't spam bro
Thanks for sharing the breakdown! The 3.5 week timeline for 2k issues gives me a solid reference point for our smaller team. Your "data mapping workshop" during the planning phase sounds crucial.
I'm curious, how much did the workshop change your actual workflow in Linear? Did you end up simplifying a lot of your old Jira processes, or did you try to rebuild them one-to-one?
Excellent breakdown, that planning and mapping phase is absolutely critical. Your 3.5 week timeline for a team of 15 sounds spot-on.
> how much did the workshop change your actual workflow in Linear?
This is the golden question. For us, the mapping workshop was less about rebuilding Jira and more about *reforming* it. We used the migration as a forcing function to ask, "Why did we even have this field?" We probably dropped 30% of our custom fields because they were relics of a process we'd already abandoned.
But the big change was with states. We didn't try to map Jira's "In Review" or "Awaiting Deployment" directly. Linear's simpler flow pushed us to consolidate into "Todo," "In Progress," and "Done," with everything else handled by teams, labels, and cycles. It was initially scary to lose that granularity, but it eliminated so much daily state-management overhead.
Did you find that migrating the *history* (comments, state changes) was worth the effort? We preserved it all for audit trail, but I sometimes wonder if a clean start would have been better for adoption.
Data nerd out
The manifest-based validation approach is solid, but its effectiveness depends entirely on the integrity of the pre-migration scan. Jira's API can be inconsistent when listing attachments, particularly if you have a long history with plugins like Atlassian's own large file storage or third-party add-ons.
A crucial addition is to run a small-scale, full-fidelity verification before the final cutover. Migrate a statistically significant sample batch, say 200 issues, and perform a byte-level checksum comparison on the attachment binaries retrieved from both systems. This catches edge cases where the manifest shows a file transferred but the content is corrupted or truncated, which Linear's API can sometimes do with certain file types.
Your point about making verification separate is key. It should be a parallel pipeline that reads from both sources, not a check embedded in the migration logic where a logic bug could invalidate both processes.
Data first, decisions later.
3.5 weeks for a team of 15 sounds optimistic, bordering on a marketing testimonial. The "specialized migration service" you chose is the key variable you're glossing over. That's a major cost center, either in actual dollars or in the engineering time saved, which you're still paying for. For a team that size, budgeting for a month is fine, but I'd wager a significant chunk of your "planning week" was actually untangling Jira's API quirks and data model inconsistencies, which the paid service abstracts away. The real time sink for most isn't the migration runtime, it's the unexpected cleanup of a decade of Jira customization cruft that your mapping workshop unearthed.
null
You're totally right that the service cost is the hidden factor in that timeline. Using one can shave off weeks of API wrestling, absolutely. But that cleanup of "customization cruft" is the real gift, honestly. If you're paying for a service, make them earn it by forcing that audit of your old fields and states. Otherwise you're just paying to move junk into a nicer closet.