Project Management migration

Migrate from ftrack to Jira

Field-level mapping, validation, and rollback between ftrack and Jira. We move data and schema; workflows are rebuilt natively in Jira.

ftrack logo

ftrack

Source

Jira

Destination

Jira logo

Compatibility

67%

8 of 12

objects map 1:1 between ftrack and Jira.

Complexity

BStandard

Timeline

2-4 weeks

Rollback included Accuracy guarantee Field-level validation

Overview

What this migration involves

ftrack and Jira are fundamentally different production-tracking platforms with incompatible data models. ftrack uses a three-level media-workflow hierarchy (Project > Sequence > Shot > Task) with integrated asset versioning and browser-based review. Jira uses a two-level issue hierarchy (Project > Epic > Story/Task/Subtask) without native media-review or asset-versioning objects. We bridge this gap by collapsing ftrack's Sequence and Shot tiers into Jira Epics and Stories, preserving Task-to-Subtask lineage, and mapping Asset publish history as Jira issue links or custom fields. Review sessions and frame annotations migrate as Comments with metadata; the actual annotation layers cannot transfer. We do not migrate ftrack Locations (studio-specific file paths carry no meaning in Jira Cloud), expression custom attributes (evaluated in ftrack UI but raw strings in API), or any Workflows, Schedules, or API pipelines—these require admin rebuild post-migration.

Field-level fidelity

Every standard and custom field arrives verified.

Schema-aware mapping

AI proposes the map; you confirm before any record moves.

Relationships preserved

Parent–child, lookups, and ownership stay linked.

Full activity history

Calls, emails, meetings — with original timestamps.

Attachments & notes

Documents, uploads, and inline notes move with the record.

Why teams make this switch

Two sides of the same decision

Leaving

ftrack logo

ftrack

What's pushing teams away

  • Initial setup requires significant API scripting and custom pipeline integration, which strains smaller teams without a dedicated pipeline TD.
  • Regular ftrack updates occasionally break existing integrations and custom scripts, creating maintenance overhead that frustrates users.
  • Project navigation inside third-party integrations is described as poor, making it difficult to browse or update ftrack data from within DCC tools.
  • Notes posted in the webplayer sometimes attach to the wrong task level, requiring producers to manually verify and reassign them.
  • Storage configuration and Location management is complex for studios without a dedicated infrastructure engineer.

Choosing

Jira logo

Jira

What's pulling them in

  • Industry-standard tool with deep Git integration and sprint reporting that engineering teams already know, reducing onboarding friction for new hires.
  • Highly customizable workflows and status schemes let business teams model complex approval chains without writing code.
  • Strong ecosystem of Atlassian Marketplace apps means specialized capabilities like time tracking or portfolio management are one install away.
  • Free tier with up to 10 users and unlimited issues gives small teams a no-cost entry point to validate the platform before committing budget.
  • Visibility features — boards, backlog grooming, sprint reports, and dashboards — give leadership a shared view of what is planned, in progress, blocked, and done.

Object mapping

How ftrack objects map to Jira

Each row shows how a ftrack object lands in Jira, including any object-level transformations, lookup resolution, or schema-design dependencies.

Typical mapping — final map is confirmed during the sample migration step.

ftrack

Project

maps to

Jira

Project

1:1
Fully supported

ftrack Projects map directly to Jira Projects. Each Project in ftrack contains the full hierarchy (Sequences, Shots, Tasks) that we decompose and redistribute into Jira Epics and Stories. Project-level custom attributes migrate as Jira Project properties or custom fields scoped to the Project. We create Jira Projects via the Jira REST API before any child issue migration begins, using the Project key from the ftrack Project name as the Jira Project key.

ftrack

Sequence

maps to

Jira

Epic

1:many
Fully supported

ftrack Sequences map to Jira Epics. Each Sequence contains one or more Shots that become Jira Stories linked to the Epic via the Epic Link field. We preserve the Sequence name as the Epic summary and Sequence-level custom attributes as Epic-level custom fields. The Sequence > Shot parent-child relationship becomes Epic > Story in Jira's issue hierarchy.

ftrack

Shot

maps to

Jira

Story

1:1
Fully supported

ftrack Shots map to Jira Stories. The Shot's parent Sequence becomes the Story's Epic Link. Shot status, thumbnail reference (as a URL in a custom field), and Shot-level custom attributes migrate as typed Jira custom fields. We flag Shot records that contain no child Tasks as they represent terminal deliverables with no Jira Subtask equivalent.

ftrack

Task

maps to

Jira

Subtask

1:1
Fully supported

ftrack Tasks map to Jira Subtasks linked to the parent Story (derived from the Shot). Task assignees map to Jira Subtask Assignee; Task status maps to Subtask Status with name-matching against the Jira Status scheme. Task due dates migrate as Due Dates; Task notes migrate as Jira Comments with the original author preserved as the Comment author if a matching Jira User exists.

ftrack

Asset

maps to

Jira

Attachment or custom field

1:1
Fully supported

ftrack Assets represent published files or builds linked to a Shot context. Jira has no native Asset object. We map Asset publish records as Jira Issue Links of type 'Relates' attached to the parent Story, with the Asset name and version stored in a custom field on the linked issue. For Asset metadata that functions as structured data (render farm output specs, resolution, codec), we create Jira custom fields typed to match the data format.

ftrack

Asset Version

maps to

Jira

Issue Link (Relates)

1:many
Fully supported

Multiple ftrack Asset Versions for a single Asset (sequential publishes) map to multiple Jira Issue Link records, each attached to the same parent Story. Version number, publish date, and publishing user migrate as link metadata fields. Jira does not have a version-on-version history object; this flattened representation preserves the publish sequence for audit without native version tracking.

ftrack

Note

maps to

Jira

Comment

1:1
Fully supported

ftrack Notes attached to any entity (Project, Shot, Task) migrate to Jira Comments. We resolve the target object by traversing the ftrack note's context_id against the hierarchy and linking to the corresponding Jira Issue. A known ftrack behavior is that notes posted via the webplayer sometimes attach to the wrong parent object; we detect mismatched notes by comparing context_id against the resolved task hierarchy and re-associate them with the correct parent before writing to Jira.

ftrack

Location

maps to

Jira

Text field (documentation only)

1:1
Fully supported

ftrack Locations define studio-specific file storage paths including cloud and on-premises configurations. Jira has no storage-location concept. We export Location configuration as a JSON metadata document delivered alongside the migration, listing each Location name, path pattern, and associated Assets. The migration team and studio infrastructure admin review this document post-migration; file path references do not transfer into Jira's data model.

ftrack

Review Session

maps to

Jira

Comment with metadata

lossy
Fully supported

ftrack Review Sessions with frame annotations link to an Asset Version. Jira has no native interactive media review capability. We migrate review session metadata (session name, participant list, date range) as a Jira Comment on the related Story, storing annotation count and version reference as structured text. Actual annotation overlays cannot transfer because Jira does not support frame-level drawing tools on media attachments.

ftrack

Custom Attribute (standard)

maps to

Jira

Custom Field

lossy
Fully supported

ftrack custom attributes on any entity type (Project, Shot, Task, Asset) map to Jira custom fields scoped to the appropriate issue type context. We handle the type mapping: text attributes to Jira Text Field, date attributes to Jira Date Field, number attributes to Jira Number Field, and dropdown-style attributes to Jira Single Select. Hierarchical custom attributes (inherited from parent) return raw null values in the ftrack API; we query the parent entity for the effective value and write the resolved value to Jira.

ftrack

Custom Attribute (expression)

maps to

Jira

Flagged for rebuild

1:1
Fully supported

Expression custom attributes in ftrack are UI-evaluated formulas (e.g., calculated due dates, derived status). The ftrack API returns the non-evaluated expression string, not the computed value. We flag all expression attribute fields during pre-migration scope and document them in the handoff deliverable. These values must be recalculated or replaced with Jira calculated fields (if supported) or manual entries post-migration.

ftrack

User / Assignee

maps to

Jira

User

1:1
Fully supported

ftrack Users assigned to Tasks map to Jira User records resolved by email address. We pre-provision Jira Users before record migration begins. Assignees without a Jira User account are placed in a reconciliation queue; the customer's admin provisions accounts before migration resumes. Active and inactive status is preserved where Jira supports inactive-user records.

Gotchas + challenges

What specifically takes care here

Platform-specific issues from each side, plus the pair-specific challenges that don't show up on either platform's page on its own.

ftrack logo

ftrack gotchas

Medium

Notes attach to wrong task level in webplayer

Medium

Hierarchical custom attributes return raw values in API

Low

Expression custom attributes not evaluated by API

High

Import wizard does not delete records

Jira logo

Jira gotchas

High

Unsupported workflow validators silently skipped during migration

High

Custom fields converted to flat text labels when migrating to non-Jira platforms

Medium

Historical status-change timestamps lost when exporting without a Marketplace plugin

Medium

Attachment import failures from oversized files and JQL reference corruption

Medium

Points-based API rate limits enforced on Jira Cloud apps from March 2026

Pair-specific challenges

  • Sequence and Shot hierarchy requires flattening into Jira Epics and Stories

    ftrack's four-level hierarchy (Project > Sequence > Shot > Task) has no direct Jira equivalent. Jira supports only three levels: Epic, Story, and Subtask. We resolve this by mapping Sequences to Epics and Shots to Stories, with Tasks becoming Subtasks. The consequence is that teams relying on Sequence-level rollup reports (aggregate progress across all Shots in a Sequence) must use Jira's Epic progress tracking instead, which has different calculation mechanics. Jira does not support a four-level issue hierarchy natively; any requirement for a fixed Sequence > Shot > Task > Sub-task structure requires custom Jira scripting or a third-party Jira app that is outside standard migration scope.

  • Media review and frame annotations do not transfer to Jira

    ftrack's core differentiator is interactive browser-based media review with frame-level annotation drawing tools. Jira has no equivalent feature—media attachments display as file previews but no annotation layer is available. Review session metadata (participant list, session date, annotation count) migrates as a Comment, and attachment file references can move as Jira attachments, but the annotation overlay data is lost. Teams that rely on ftrack Review for client and stakeholder feedback must plan a post-migration review workflow using a separate tool such as Frame.io, SyncSketch, or Dropbox Media if client annotation is required.

  • ftrack Locations carry studio-specific path logic that Jira cannot consume

    ftrack Locations define file storage paths across studio infrastructure (on-premises NAS, cloud buckets, cross-site transfer rules). Jira has no storage-location concept—files attached to Jira issues are stored in Jira Cloud's managed storage or a connected cloud provider (AWS S3, Google Drive) without studio-specific routing. Location configuration migrates as a JSON reference document only. Studios must re-establish file access patterns and render farm output paths using Jira attachments or a connected DAM system post-migration.

  • Expression custom attributes return raw formula strings not evaluated values

    Expression attributes in ftrack (calculated fields such as derived deadlines or formula-based statuses) are evaluated by the ftrack UI but the ftrack API returns the raw expression string without evaluation. We flag every expression attribute during pre-migration scope and exclude them from standard data migration. These fields require manual value recalculation or replacement with Jira calculated fields after migration.

  • Jira API rate limits constrain batch import throughput for large migrations

    Jira Cloud enforces API rate limits that vary by tier (typically 0-10 requests per minute for anonymous access, higher for authenticated requests with OAuth or API tokens). Large ftrack migrations with thousands of issues require batch chunking, exponential backoff, and parent-record lookup resolution in sequence. We manage this by sequencing imports by dependency (Projects first, then Epics, then Stories, then Subtasks), chunking batches at 50-100 issues, and retrying with backoff on 429 responses. Migrations without this handling fail silently or timeout when hitting rate limits mid-import.

Migration approach

Six steps for a successful ftrack to Jira data migration

  1. Discovery and workspace audit

    We audit the source ftrack workspace via the REST API, enumerating all Projects, Sequences, Shots, Tasks, Assets, Asset Versions, Notes, Review Sessions, and custom attribute definitions. We capture the full hierarchy depth per Project, the count of distinct custom attribute keys and their types (text, number, date, hierarchical, expression), the number of Location records, and the total issue count estimate. This output is the migration scope document that defines the Jira schema design required.

  2. Jira schema design and custom field provisioning

    We design the Jira destination schema in a Sandbox or development Jira site before any data moves. This includes creating Jira Projects (key derived from ftrack Project name), defining the Issue Type scheme (Epic, Story, Subtask), creating all custom fields typed to match ftrack attribute types, configuring Status schemes and workflows, and scoping custom field contexts to the appropriate issue types. The Epic Link field on Stories is configured to reference the relevant Project's Epics. If Jira's native time tracking is required, we enable it per Project during this phase.

  3. Sandbox validation migration

    We run a full migration into the Jira Sandbox using a representative subset of ftrack data (at least one Project from each Sequence-count tier). The customer's Project Manager or Producer spot-checks 20-30 randomly sampled Jira issues against the ftrack source for field accuracy, hierarchy correctness, and assignee resolution. We correct any mapping errors before production migration begins. This step also reveals any Jira workflow constraints (validation rules, required fields, conditional logic) that need admin-level adjustment before the production run.

  4. User provisioning and assignee reconciliation

    We extract every distinct assignee from ftrack Tasks and match by email against the Jira destination User table. Jira Users must be provisioned and active before record migration begins because Jira Subtasks require an OwnerId. Assignees without a Jira account are listed in a reconciliation report for the customer's Jira admin to provision. Reviewers and stakeholders listed on ftrack Review Sessions are handled similarly—matched by email or flagged for manual account creation.

  5. Production migration in dependency order

    We execute production migration in strict dependency sequence: Jira Projects and Epics first (establishing the hierarchy anchors), then Stories derived from Shots, then Subtasks derived from Tasks, then Asset metadata as Issue Links, then Comments from Notes (with misattributed note correction applied), then Review Session metadata as Comments. Each phase emits a row-count reconciliation report. We chunk API calls to stay within Jira rate limits and retry with exponential backoff on 429 responses.

  6. Cutover, delta sync, and review workflow planning

    We freeze ftrack writes during the cutover window, run a final delta migration for any records modified after the last batch, then confirm Jira as the system of record. We deliver the ftrack Locations reference document and the expression attribute flag list as structured handoff artifacts. We do not rebuild ftrack Workflows or Schedules in Jira; these are documented by trigger and action for the customer's admin to configure in Jira Automation or Jira Flow. We do not configure media review tooling in Jira as that is outside migration scope.

Platform deep dives

Context on both ends of the pair

ftrack logo

ftrack

Source

Strengths

  • End-to-end production tracking from planning through review and final delivery in a single platform.
  • Interactive browser-based media review with annotation tools that clients and stakeholders can access without a full ftrack seat.
  • Python API with JSON-schema-based dynamic schemas that adapt to each workspace's custom entity types and attributes.
  • Locations feature for managing multi-site storage and automated file transfer across studio infrastructure.
  • Custom attribute system allowing studios to extend any entity type with project-specific fields.

Weaknesses

  • API performance degrades with deeply linked queries and large unfocused data fetches, requiring careful query optimization.
  • Hierarchical and expression custom attributes are not fully supported in the API, returning raw rather than evaluated values.
  • Initial deployment and ongoing maintenance require dedicated pipeline TD resources or significant scripting investment.
  • Webplayer note posting can attach comments to the wrong hierarchical level, creating data integrity issues.
  • Enterprise tier pricing is opaque and requires a sales contact, making it hard to budget for large studio deployments.
Jira logo

Jira

Destination

Strengths

  • Deeply customizable workflows and status schemes with no hard limits on workflow complexity or number of custom statuses.
  • Strong agile ceremony support: sprint planning, backlog grooming, velocity tracking, and burndown charts for Scrum teams.
  • Industry-standard developer tool with native Git integration linking commits, pull requests, and deployments to issues.
  • Large Atlassian Marketplace with thousands of plugins extending time tracking, portfolio management, and reporting capabilities.
  • Free tier available for up to 10 users with unlimited issues, enabling evaluation before committing to a paid plan.

Weaknesses

  • Excessive configurability creates a steep learning curve; cross-team consistency is hard to maintain without strict governance.
  • Performance degrades with large backlogs, complex custom fields, and heavily nested issue hierarchies.
  • Reporting requires additional configuration or paid plugins; out-of-the-box analytics are limited for business users.
  • Jira lacks native sprint management, requiring Jira Software for true agile team features.
  • Teams outside engineering resist adoption due to UI complexity, leaving the all-in-one promise unfulfilled for cross-functional organizations.

Complexity grading

How hard is this migration?

Standard Project Management migration. 3 of 8 objects need a mapping; the rest are 1:1.

B

Overall complexity

Standard migration

Derived from compatibility, mapping clarity, API constraints, and data volume across ftrack and Jira.

  • Object compatibility

    B

    3 of 8 objects need a mapping; the rest are 1:1.

  • Field mapping clarity

    C

    Field mapping is derived from defaults — final spec confirmed during the sample migration.

  • Timeline complexity

    B

    8-object category — typical timelines run 2–7 days end-to-end.

  • API constraints

    B

    ftrack: Not publicly documented; ftrack advises optimizing queries to avoid server-side resource strain.

  • Data volume sensitivity

    B

    ftrack doesn't expose a bulk API — REST + parallelization used for high-volume runs.

Estimator

Estimate your ftrack to Jira migration cost

Rule-based pricing — no per-record fees, no manual quotes. Migrations over 2M records are scoped individually.

Step 1

What are you migrating?

Pick a category, then your source and destination platforms.

Category

FAQ

Frequently asked questions about ftrack to Jira data migrations

Answers to the questions buyers ask most during ftrack to Jira migration scoping. Not seeing yours? Book a call.

Can't find your answer?

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Most ftrack to Jira migrations complete in two to four weeks for Studios with under 1,000 Tasks across 5 or fewer Projects and no complex custom attribute schemas. Migrations with deep hierarchies (more than 3 Sequences per Project), active Asset Version histories, or multiple custom attribute types move to five to eight weeks. Timeline drivers include hierarchy flattening design, Jira custom field provisioning, assignee reconciliation, and any post-migration review workflow planning that the customer requires before going live.

Adjacent paths

Related migrations to explore

Ready when you are

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