netbox-migration

NetBox Migration

Your knowledge of NetBox import mechanisms may be outdated. Available import methods, bulk operation limits, and Diode entity types evolve between releases. Prefer retrieval over pre-trained knowledge.

Retrieval Sources

Source	URL / Method	Use for
NetBox REST API docs	https://netboxlabs.com/docs/netbox/integrations/rest-api/	Bulk create/update endpoints
Diode docs	https://netboxlabs.com/docs/diode/	High-volume ingestion
pynetbox repo	https://github.com/netbox-community/pynetbox	Python SDK for scripted imports
NetBox MCP server	If configured — verify imported data landed correctly	Post-migration validation

This skill teaches how to think about and execute data migrations into NetBox. It covers the full lifecycle: analyzing messy source data, cleaning it, choosing an import strategy, loading it in the right order, and verifying everything landed correctly.

This is NOT about NetBox version upgrades. This is about getting data from spreadsheets, legacy tools, or other CMDBs into NetBox for the first time (or re-importing).

When to Use This Skill

• Given a spreadsheet, CSV, or data dump to import into NetBox
• Migrating from another CMDB/IPAM (phpIPAM, Device42, RackTables, ServiceNow, InfoBlox)
• Planning a NetBox deployment and need to populate initial data
• Consolidating data from multiple sources into NetBox as source of truth

Quick-Start Decision Tree

How much data?

• < 100 objects → NetBox CSV import UI
• 100–10K objects → pynetbox scripts
• 10K+ objects or ongoing sync → Diode SDK

What's the source?

• Spreadsheet → Clean first, then pynetbox or CSV import
• Legacy IPAM tool → Check for existing migration tools, else export→transform→import
• Network discovery data → Import basics, then use Discovery to enrich
• Multiple sources → Establish primary source per data type, reconcile conflicts

One-time or ongoing?

• One-time → pynetbox scripts with get-or-create pattern
• Ongoing sync → Diode SDK with scheduled runs
• Hybrid → Diode for bulk, pynetbox for relationship fixup

The Five Phases

Every migration follows this arc. Don't skip phases — the most common failure is jumping straight to import without assessment and cleaning.

Phase 1: Assessment

Goal: Understand what you have, what NetBox needs, and the gap between them.

1. Inventory your sources — List every spreadsheet, database, tool, wiki page, and person's head that holds infrastructure data. Identify which source is authoritative for each data type.

2. Map fields to NetBox models — For each source field, identify the corresponding NetBox model and field. See references/source-mapping.md for common mappings by source system.

3. Identify what to keep vs leave behind — Not everything migrates. NetBox has specific models; data that doesn't fit goes into custom fields, comments, or gets dropped. The principle: start with what NetBox requires, not what your spreadsheet has.

4. Determine scope — Migrate in phases. Start with the physical hierarchy (sites, racks, devices), then IPAM, then connections, then enrichment. Don't try to do everything at once.

5. Check for existing tools — phpIPAM has ipam-migrator, RackTables has racktables2netbox. Search GitHub before writing custom scripts.

Tricky mappings to watch for:

• "Location" in source may mean site, location-within-site, or region in NetBox
• "Device type" in source usually means role, not NetBox's DeviceType (which is hardware model)
• Source has one IP per device → you need to create an Interface, assign the IP, then set it as primary_ip
• "Cisco Catalyst 9300-48P" → split into Manufacturer("Cisco") + DeviceType(model="Catalyst 9300-48P")
• Location hierarchy "Building A, Floor 3, Room 301" → nested Location objects

For NetBox's data model and relationships, see netbox-data-modeling.

Phase 2: Data Cleaning

Principle: Garbage in, garbage out. Clean BEFORE importing — fixing data in NetBox after import is harder than fixing it in a spreadsheet.

The top 10 cleaning tasks:

1. Normalize names — Pick a convention (lowercase-hyphenated recommended) and apply it everywhere. "NYC-DC1", "New York DC 1", "nyc dc1" → nyc-dc1.

2. Standardize manufacturers — "Cisco", "cisco", "Cisco Systems", "CISCO" → one canonical name. Case-sensitivity matters — especially with Diode's reconciler.

3. Standardize device types — "C9300-48P", "Catalyst 9300 48P", "WS-C9300-48P" → one model string per hardware model.

4. Deduplicate — Same device with different names across sources. Same IP on multiple records. Pick the authoritative record.

5. Remove stale data — Devices decommissioned years ago still in spreadsheets. Mark as decommissioning or remove.

6. Validate IPs — Must be proper CIDR notation. Add /32 for host addresses. 10.0.0.1/24 is an IP address on a /24, not a prefix — know the difference.

7. Fill required fields — Every Device needs: name, device_type, role, site. Every IP needs address in CIDR. Check NetBox's required fields per model.

8. Map status values — Source "production"/"live"/"up" → NetBox active. Source "down"/"retired" → NetBox offline or decommissioning. Valid choices: active, planned, staged, offline, decommissioning, inventory.

9. Handle multi-value cells — "10.0.1.1, 10.0.1.2" in one cell → split into separate records.

10. Remove formatting artifacts — Excel leading zeros stripped, trailing spaces, BOM characters, merged cells creating blanks, "N/A" and "unknown" values → decide skip or placeholder.

Community wisdom: "Fill a few records manually in NetBox, export them, then build your CSV to match that format." NetBox's CSV export headers don't match import headers — always test with a small sample first.

Phase 3: Import

Choose your strategy based on the decision framework in references/import-patterns.md.

Strategy	Best For	Dependency Ordering
CSV Import (UI)	< 1K objects, simple types	Manual — one type at a time
pynetbox	1K–100K, complex logic	Manual — you control the order
Diode SDK	10K+, ongoing sync	Automatic — reconciler handles deps
Custom Scripts	Patterned data, in-NetBox transforms	Manual — Django ORM

Regardless of strategy, respect dependency order. Objects must exist before other objects can reference them. See references/dependency-order.md for the complete 11-tier ordering.

Summary of dependency tiers:

1. Tenants, tags, manufacturers, RIRs, device roles, rack roles
2. Regions, site groups
3. Sites
4. Locations (within sites)
5. Racks (within locations)
6. Device types + component templates (need manufacturer)
7. Devices (need type, role, site) — components auto-created from templates
8. Interfaces (custom ones beyond templates)
9. VRFs, VLANs, prefixes, IP addresses → assign to interfaces
10. Cables, circuits, providers
11. Virtual machines, clusters, config contexts, custom field values

The circular dependency problem: A device's primary_ip requires: create device → create interface (auto from template) → create IP → assign IP to interface → update device's primary_ip. This requires two passes — first create everything, then set primary IPs.

Diode eliminates most ordering concerns. It creates missing dependencies automatically. If you ingest a Device referencing a non-existent Site, Diode creates the Site first. For large migrations, this is a significant advantage. See netbox-diode.

Key import rules:

• Always use get-or-create patterns — Makes scripts idempotent and re-runnable
• Test in staging first — Use a separate NetBox instance for dry runs
• Import in phases — Sites/racks first, verify, then devices, verify, then IPAM
• Reference objects by name/slug in CSV, not database IDs
• DeviceType/ModuleType use YAML format, not CSV (they include component templates)
• Bulk create with pynetbox: nb.dcim.devices.create([{...}, {...}]) — batch in chunks of ~100 (a recommended size for throughput/memory, not a NetBox-enforced limit)
• Exclude config_context from API queries during bulk operations (massive performance impact)
• Reading back large sets for validation: on NetBox 4.6+ prefer cursor pagination (?start=<id>&limit=N) over deep ?offset= scans — offset slows linearly at high offsets. See netbox-api-integration.

For API patterns, see netbox-api-integration.

Phase 4: Validation

Three validation layers — all three are required for a complete migration.

Layer 1: Structural (NetBox accepted it)

• Object counts match source: devices, IPs, prefixes, sites, VLANs
• No import errors or skipped records
• Required fields populated on all objects

Layer 2: Relational (connections correct)

• Every device has correct site, device type, manufacturer, and role
• Every IP assigned to the correct interface on the correct device
• Primary IPs set on devices that should have them
• Rack positions match source layout
• VLAN-to-prefix associations correct
• VRF assignments match routing design
• Prefix hierarchy makes sense (containers contain child prefixes)

Layer 3: Operational (matches live network)

• Devices in NetBox respond on the network
• IPs in NetBox match actual device configurations
• No devices on network missing from NetBox
• Interface states match reality
• This layer uses Discovery and Assurance — see Phase 5

See references/validation-checklist.md for the complete checklist.

Validation script pattern:

import pynetbox
nb = pynetbox.api("https://netbox.example.com", token="...")

# Count verification
source_device_count = 500  # from your source data
assert nb.dcim.devices.count() >= source_device_count

# Spot-check critical devices
for name in ["core-rtr-01", "fw-01", "dns-01"]:
    d = nb.dcim.devices.get(name=name)
    assert d, f"Missing: {name}"
    assert d.primary_ip, f"No primary IP: {name}"
    assert d.site, f"No site: {name}"

# Find orphaned IPs
for ip in nb.ipam.ip_addresses.all():
    if not ip.assigned_object:
        print(f"Unassigned: {ip.address}")

Phase 5: Post-Migration

Migration isn't done when the data is loaded. It's done when NetBox matches reality and is established as the source of truth.

Run Discovery to fill gaps

Spreadsheets never have everything. Use the Orb Agent to discover what's missing:

• Network scanning (NMAP) — Find IPs not in NetBox, identify rogue devices
• Device discovery (NAPALM/SNMP) — Pull real interface configs, VLANs, MAC addresses, OS versions
• LLDP/CDP neighbor data — Discover cable connections automatically

Discovery fills the gaps that source data always has: interfaces, actual IP assignments, MAC addresses, real platform versions, cable topology. See netbox-discovery.

Run Assurance to verify correctness

Define rules for what "correct" looks like, then check compliance:

• Every active device has a primary IP
• Every active device has at least one interface
• Every rack has a site and location
• Every prefix has a VRF (if multi-VRF environment)
• No duplicate IPs in the same VRF

See netbox-assurance.

Establish ongoing processes

• Schedule regular Discovery runs to detect drift
• Review Assurance reports weekly
• Establish change process: changes go to NetBox first, then implement on network
• Integrate with automation (Ansible/Terraform pull from NetBox as source of truth)

Completeness Criteria

A migration is done when:

1. ✅ All source data is represented in NetBox (every device, IP, prefix, VLAN)
2. ✅ Relationships are correct (IPs → interfaces → devices → racks → sites)
3. ✅ Hierarchy is structured (regions → sites → locations → racks)
4. ✅ IPAM is organized (prefix hierarchy, VRF scoping, aggregates cover all space)
5. ✅ Naming is consistent (one convention, no duplicates, no ambiguity)
6. ✅ Discovery confirms reality matches (no phantom devices, no missing devices)
7. ✅ Assurance rules pass (compliance checks green)
8. ✅ Source system can be decommissioned (NetBox is now authoritative)

Anti-Patterns

1. Export ≠ import format — NetBox CSV export headers differ from import headers. Always test with a sample.
2. Case sensitivity — "Site A" and "site a" create duplicates, especially via Diode.
3. Interfaces auto-created — DeviceType templates create interfaces when a device is created. Don't re-create them.
4. Circular dependency (primary IP) — Requires two-pass: create device → create IP → assign back.
5. CSV encoding — Use UTF-8 without BOM, LF line endings.
6. Slug conflicts — Auto-generated slugs may collide. Provide explicit slugs when names are similar.
7. Device name uniqueness — Per site+tenant, not globally. Same name in different sites is fine.
8. Large CSV imports are slow — For > 1K objects, switch to API or Diode.
9. Don't migrate everything at once — Phase it. Sites → devices → IPAM → connections.
10. "Feeling overwhelmed" — Normal. Focus on what NetBox has models for. Ignore data that doesn't map.

References

• references/source-mapping.md — Field mappings by source system
• references/dependency-order.md — Complete 11-tier import order
• references/validation-checklist.md — Post-import verification checklist
• references/import-patterns.md — Code examples for each import strategy