dataflows-save-as-authoring-cli

name

dataflows-save-as-authoring-cli

description

Copy, clone, duplicate, rebind, and execute Gen1 dataflow save-as upgrade operations via CLI (az rest / curl) against Power BI REST and Fabric REST APIs. Covers Gen1 to Gen2.1 upgrade save-as, cross-workspace Gen1-to-Gen2.1 copy flows, connection rebinding, output changes, readiness snapshots, and SaveAsNativeArtifact execution. Use when the user wants to: (1) discover Gen1 dataflows, (2) assess save-as readiness, (3) upgrade Gen1 into Gen2.1, (4) create a Gen2.1 copy from a Gen1 dataflow in another workspace, (5) rebind connections or validate saved data. For creating/editing Gen2 dataflows from scratch, previewing candidate M, binding ordinary authoring connections, or copying Gen2 dataflows, use `dataflows-authoring-cli`. Triggers: "save Gen1 dataflow", "upgrade dataflow", "Gen1 Gen2 readiness", "Gen1 Gen2 save-as", "saveAsNativeArtifact", "copy Gen1 dataflow", "duplicate Gen1 dataflow", "clone Gen1 dataflow", "rebind Gen1 dataflow connections".

Update Check — ONCE PER SESSION (mandatory) The first time this skill is used in a session, run the check-updates skill before proceeding.

• GitHub Copilot CLI / VS Code: invoke the check-updates skill.
• Claude Code / Cowork / Cursor / Windsurf / Codex: compare local vs remote package.json version.
• Skip if the check was already performed earlier in this session.

CRITICAL NOTES

1. To find the workspace details (including its ID) from workspace name: list all workspaces and, then, use JMESPath filtering
2. To find the item details (including its ID) from workspace ID, item type, and item name: list all items of that type in that workspace and, then, use JMESPath filtering

GENERATION BOUNDARY -- HARD STOP (mandatory) This skill executes save-as only from Gen1 to Gen2.1. For an execution request whose source is Gen2, or whose source generation is not established, state that no public save-as or in-place upgrade endpoint is available and stop before API calls. This does not block a read-only readiness scan whose purpose is to discover and classify Gen1 candidates. Do not interpret "choose the closest endpoint and proceed" as approval to export a definition and create a copy, do not invoke the authoring skill automatically, and do not mutate anything. Ask the user to clarify the intended outcome and explicitly approve any separately documented alternative.

dataflows-save-as-authoring-cli — Dataflow Save-As Gen1 → Gen2.1 CI/CD via CLI

A save-as companion for creating upgraded Gen2.1 copies from Power BI Gen1 dataflows using readiness assessment and guarded execution.

We currently cannot perform an in-place migration of your dataflow. We can use save-as to create an upgraded Gen2.1 copy while preserving the original Gen1 dataflow.

Table of Contents

Task	Reference	Notes
Finding Workspaces and Items in Fabric	COMMON-CLI.md § Finding Workspaces and Items	Mandatory — READ link first
Fabric Topology & Key Concepts	COMMON-CORE.md § Fabric Topology
Environment URLs	COMMON-CORE.md § Environment URLs
Authentication & Token Acquisition	COMMON-CORE.md § Authentication	Wrong audience = 401
Core Control-Plane REST APIs	COMMON-CORE.md § Core REST APIs	Pagination, LRO polling, rate limits
Tool Selection Rationale	COMMON-CLI.md § Tool Selection
Authentication Recipes	COMMON-CLI.md § Auth Recipes	az login flows and token acquisition
Fabric Control-Plane API via az rest	COMMON-CLI.md § az rest	Always pass --resource
Gotchas & Troubleshooting (CLI)	COMMON-CLI.md § Gotchas	az rest audience, shell escaping
Quick Reference	COMMON-CLI.md § Quick Ref	Token audience / tool matrix
Dataflow Definition Structure

---

Tool Stack

Tool	Role	Install
az CLI	Primary: Auth (az login), REST API calls (az rest) against both Fabric and Power BI APIs.	Pre-installed in most dev environments
jq	Parse and filter JSON responses (dataflow lists, risk signal extraction).	Pre-installed or trivial
base64	Decode dataflow definitions for inspection.	Built into bash / [Convert]::ToBase64String() in PowerShell

Agent check — verify before first operation:

az --version 2>/dev/null || echo "INSTALL: https://aka.ms/install-azure-cli"
jq --version 2>/dev/null || echo "INSTALL: apt-get install jq OR brew install jq"

---

Authentication & API Audiences

This skill uses two distinct API audiences. Using the wrong audience returns 401.

API	Audience (--resource)	Use For
Fabric Items API	https://api.fabric.microsoft.com	List Gen2 dataflows (Fabric-native), workspace discovery
Power BI REST API	https://analysis.windows.net/powerbi/api	Gen1 dataflow discovery, saveAsNativeArtifact, data sources, upstream dataflows, Admin API scanning

# Fabric Items API — list Gen2 dataflows in a workspace
az rest --method get \
  --resource "https://api.fabric.microsoft.com" \
  --url "https://api.fabric.microsoft.com/v1/workspaces/$WS_ID/dataflows"

# Power BI REST API — list all dataflows (Gen1 + Gen2) in a workspace
az rest --method get \
  --resource "https://analysis.windows.net/powerbi/api" \
  --url "https://api.powerbi.com/v1.0/myorg/groups/$WS_ID/dataflows"

# Power BI Admin API — list all dataflows tenant-wide (requires admin role)
az rest --method get \
  --resource "https://analysis.windows.net/powerbi/api" \
  --url "https://api.powerbi.com/v1.0/myorg/admin/dataflows"

---

Phase 1 — Awareness & Readiness

Goal: "Should I use save-as, and what will happen when I create a Gen2.1 copy?"

Agentic Workflow: Discover → Assess → Classify → Report

Follow this sequence for every save-as assessment:

1. Discover — Scan workspace(s) to inventory all dataflows, identifying Gen1 vs Gen2
2. Assess — For each Gen1 dataflow, check seven risk signals
3. Classify — Assign a readiness category: ✅ Safe / ⚠️ Manual followups / ❌ Blocked
4. Report — Output a Save-As Readiness Snapshot (markdown table + JSON)

Step 1: Discover — Identify Gen1 Dataflows

The Power BI REST API returns a generation property (value 1 or 2) on each dataflow. This is the preferred detection method — a single API call per workspace.

Non-Admin Path (per workspace)

WS_ID="<workspaceId>"
RESOURCE_PBI="https://analysis.windows.net/powerbi/api"

# List all dataflows — the `generation` property distinguishes Gen1 from Gen2
ALL_DATAFLOWS=$(az rest --method get \
  --resource "$RESOURCE_PBI" \
  --url "https://api.powerbi.com/v1.0/myorg/groups/$WS_ID/dataflows" \
  --query "value[].{id:objectId, name:name, generation:generation, modelUrl:modelUrl, configuredBy:configuredBy}" -o json)

# Filter Gen1 dataflows
echo "$ALL_DATAFLOWS" | jq '[.[] | select(.generation == 1)]'

# Filter Gen2 dataflows
echo "$ALL_DATAFLOWS" | jq '[.[] | select(.generation == 2)]'

Tip: A modelUrl pointing to dfs.core.windows.net additionally indicates BYOSA (customer-managed storage) — a save-as blocker.

Admin Path (tenant-wide)

Requires Fabric administrator role or service principal with Tenant.Read.All scope. Rate limited to 200 requests/hour.

RESOURCE_PBI="https://analysis.windows.net/powerbi/api"

# List ALL dataflows in the tenant
ADMIN_DATAFLOWS=$(az rest --method get \
  --resource "$RESOURCE_PBI" \
  --url "https://api.powerbi.com/v1.0/myorg/admin/dataflows" \
  --query "value[].{id:objectId, name:name, workspaceId:workspaceId, modelUrl:modelUrl, configuredBy:configuredBy}" \
  -o json)

# Filter Gen1 dataflows — those with a modelUrl indicate CDM/Gen1 storage
# Note: Admin API may not expose the `generation` property; use modelUrl as fallback
echo "$ADMIN_DATAFLOWS" | jq '[.[] | select(.modelUrl != null and .modelUrl != "")]'

Note: The Admin API supports $filter, $top, and $skip for pagination on large tenants.

Step 2: Assess — Check Risk Signals

For each Gen1 dataflow found, evaluate seven risk signals. See risk-assessment-guide.md for detailed API calls.

#	Risk Signal	Detection Method	Impact
1	Incremental refresh	Check dataflow definition for incremental refresh policy configuration	⚠️ Schedule migrates in disabled state; must re-enable and validate
2	BYOSA / Custom ADLS Gen2 storage	Check modelUrl — if points to customer storage account (not Power BI managed)	❌ Data stays in old storage; Gen2 CI/CD uses Fabric-managed storage
3	Power Automate / API triggers	Check for external orchestration referencing the Gen1 dataflow ID	⚠️ All integrations must update to new Gen2 artifact ID
4	Downstream pipeline dependencies	Check Fabric pipelines for dataflow activity references	⚠️ Pipeline activities reference dataflow by ID; must re-bind
5	Linked / computed entities	Inspect dataflow definition for entity references to other dataflows	⚠️/❌ Cross-dataflow references may break if source dataflows are not saved first
6	DirectQuery connections	Inspect data source types in definition	❌ DirectQuery not supported in Gen2 CI/CD dataflows
7	Caller is not owner / insufficient role	Compare configuredBy against az account show --query user.name -o tsv — or attempt call and catch DataflowUnauthorizedError	❌ saveAsNativeArtifact requires the caller to be the dataflow owner or have Contributor/Admin in the source workspace; Viewer/Member without ownership cannot execute save-as

Step 3: Classify — Readiness Categories

Category	Criteria	Action
✅ Safe	No risk signals detected	Create a Gen2.1 save-as copy with saveAsNativeArtifact
⚠️ Manual followups	Risk signals 1, 3, 4, or 5 (non-blocking)	Execute save-as, then remediate flagged issues
❌ Blocked	Risk signals 2, 6, or 7 (blocking)	Cannot execute save-as until blocker is resolved

Tip — detect ownership before save-as: The configuredBy field in the dataflow list response contains the owner's email. Compare it against the currently logged-in user (az account show --query user.name -o tsv). If they don't match and your workspace role is below Contributor, flag the dataflow as ❌ Blocked (signal 7) and escalate to the owner.

Step 4: Report — Save-As Readiness Snapshot

Markdown Output (terminal)

## Save-As Readiness Snapshot
| Workspace | Dataflow | Type | Readiness | Risk Signals | Recommendation |
|---|---|---|---|---|---|
| Sales Analytics | SalesETL | Gen1 | ✅ Safe | None | Save as Gen2.1 copy now |
| Sales Analytics | CustomerLoad | Gen1 | ⚠️ Manual | Incremental refresh, Pipeline dep | Save as Gen2.1 copy, then re-enable schedule & update pipeline |
| Finance | FinanceDaily | Gen1 | ❌ Blocked | BYOSA storage | Resolve storage dependency first |

JSON Output (automation)

{
  "snapshotDate": "2025-04-13T10:00:00Z",
  "summary": { "total": 3, "safe": 1, "manual": 1, "blocked": 1 },
  "dataflows": [
    {
      "workspaceName": "Sales Analytics",
      "workspaceId": "...",
      "dataflowName": "SalesETL",
      "dataflowId": "...",
      "type": "Gen1",
      "readiness": "safe",
      "riskSignals": [],
       
       
    
  

Save JSON to file: pipe to jq '.' > readiness-snapshot.json

---

Execute with Guardrails

Goal: Invoke save-as and capture outcomes safely.

Gen1 → Gen2.1 CI/CD: saveAsNativeArtifact API

POST https://api.powerbi.com/v1.0/myorg/groups/{groupId}/dataflows/{gen1DataflowId}/saveAsNativeArtifact

This is a Preview API. It creates a new Gen2.1 CI/CD artifact copy while preserving the original Gen1 dataflow.

WS_ID="<workspaceId>"
GEN1_ID="<gen1DataflowId>"

# Write body to a temp file — az rest wraps inline --body in an envelope
# on some platforms, causing "saveAsRequest is a required parameter" errors.
cat > /tmp/save-as-body.json <<'EOF'
{
  "displayName": "MyDataflow_Gen2CICD",
  "description": "Saved as Gen2.1 copy from Gen1",
  "includeSchedule": true,
  "targetWorkspaceId": "<targetWorkspaceId>"
}
EOF

az rest --method post \
  --resource "https://analysis.windows.net/powerbi/api" \
  --url "https://api.powerbi.com/v1.0/myorg/groups/$WS_ID/dataflows/$GEN1_ID/saveAsNativeArtifact" \
  --headers "Content-Type=application/json" \
  --body @/tmp/save-as-body.json

Gotcha — inline body: Passing JSON inline via --body '{...}' can cause az rest to wrap the payload in an extra envelope, resulting in "saveAsRequest is a required parameter" errors. Always use file-based body (--body @file.json) for this endpoint.

Gotcha — Windows az.cmd: On Windows, omit -o json from saveAsNativeArtifact calls — the flag produces "A value that is not valid (json) was specified for the outputFormat parameter" when routed through az.cmd. Capture output without -o json and parse with ConvertFrom-Json in PowerShell, or pipe to jq in bash.

Gotcha — not idempotent (duplicate artifacts on retry): saveAsNativeArtifact creates a new artifact every time it is called. If a batch is interrupted and re-run, you will end up with multiple copies in the target workspace. To make retries safe: (1) check whether a Gen2 artifact with the intended name already exists before calling, or (2) include a timestamp in displayName and treat each run as a distinct artifact.

Gotcha — owner permissions: You must be the dataflow owner or have Contributor/Admin in the source workspace to call saveAsNativeArtifact. If you are only a Viewer or Workspace Member who does not own the dataflow, the API returns DataflowUnauthorizedError. Ask the dataflow owner or a workspace admin to run the save-as operation for those dataflows.

Request parameters:

Parameter	Type	Required	Description
displayName	string (max 200)	No	Name for new artifact. Auto-generated with _copy1 suffix if omitted
description	string (max 4000)	No	Description. Copied from source if omitted
includeSchedule	boolean	No	Copy refresh schedule in disabled state
targetWorkspaceId	string (uuid)	No	Target workspace. Same workspace if omitted

Response: 200 OK with SaveAsNativeDataflowResponse:

• artifactMetadata — full metadata of the new Gen2 CI/CD artifact (including objectId, provisionState)
• errors[] — non-fatal warning codes (save-as succeeds even if these occur):
  • FailedToCopySchedule — schedule could not be copied
  • SetDataflowOriginFailed — origin tracking not set
  • ConnectionsUpdateFailed — connection strings could not be updated to Fabric format

Gen2 → Gen2 CI/CD: In-Place Upgrade

NOT YET AVAILABLE — This API is not available in the current public surface. This skill will be updated when the endpoint is published. Do not attempt to call a non-existent endpoint.

Post-Save-As Validation Checklist

Run these checks after save-as before any Gen1 cleanup:

• Confirm artifactMetadata.provisionState reaches Active.
• Review errors[] in SaveAsNativeDataflowResponse and create follow-up tasks for each warning.
• Confirm the new artifact exists in the target workspace and has expected name/description.
• Verify dependent orchestration (pipelines, flows, API callers) is updated to the new artifact ID.
• Only trigger refresh when the user explicitly approves.

---

Must/Prefer/Avoid

MUST DO

• Always pass --resource to az rest — use the correct audience per the API table above. Wrong audience = silent 401.
• Always include --headers "Content-Type=application/json" on POST calls to the Power BI REST API.
• Use file-based body for saveAsNativeArtifact — pass --body @file.json instead of inline JSON. Inline --body '{...}' can cause az rest to wrap the payload in an extra envelope, producing "saveAsRequest is a required parameter" errors.
• On Windows, omit -o json on saveAsNativeArtifact calls — use ConvertFrom-Json in PowerShell or pipe to jq instead. The -o json flag fails with "A value that is not valid (json)" error when routed through az.cmd.
• Verify you are the dataflow owner or Contributor before save-as — saveAsNativeArtifact returns DataflowUnauthorizedError for non-owners who are only Workspace Members or Viewers.
• Check for existing Gen2 artifacts before retrying — saveAsNativeArtifact is not idempotent; interrupted batch runs create duplicate copies on retry. Either verify the target name is absent before calling, or use a unique timestamped displayName per run.
• Scan before save-as — always run the readiness scan before execution.
• Never refresh without explicit user consent — the Gen2 CI/CD artifact schedule is created in disabled state for safety.
• Check errors[] in saveAsNativeArtifact response — save-as may succeed with non-fatal warnings.
• Verify provisionState is Active after save-as — poll the artifact metadata until terminal state.
• Preserve the original Gen1 dataflow — saveAsNativeArtifact leaves the Gen1 intact. Do not delete it until post-save-as validation passes.

PREFER

• Admin API for tenant-wide scanning — more efficient than workspace-by-workspace for large tenants.
• JSON output for automation — markdown is for human review, JSON for scripting and CI/CD integration.
• Topological save-as order — save upstream dataflows (with linked entities) before downstream consumers.
• Readiness-first execution — do not skip readiness assessment before save-as.
• generation property for Gen1 detection — the Power BI REST API returns generation: 1 or generation: 2 on each dataflow. Use this single-API-call approach over cross-API comparison.
• modelUrl check as secondary signal — dataflows with a modelUrl pointing to dfs.core.windows.net are Gen1 using customer storage (BYOSA blocker).

AVOID

• Saving blocked (❌) dataflows — resolve BYOSA storage and DirectQuery issues first.
• Batch save-as without review — always review the readiness snapshot before saving multiple dataflows.
• Hardcoded workspace/dataflow IDs — resolve dynamically via REST API.
• Ignoring non-fatal errors — FailedToCopySchedule, ConnectionsUpdateFailed, etc. require manual follow-up.
• Calling the Gen2→Gen2 CI/CD in-place save-as API — it does not exist yet. Do not fabricate endpoints.
• Deleting Gen1 artifacts without explicit user consent — never delete Gen1 dataflows unless the user explicitly approves deletion, even after post-save-as validation confirms data integrity.
• Using -o json with saveAsNativeArtifact on Windows — omit the flag; capture raw output and parse it explicitly.
• Re-running batch save-as scripts without idempotency checks — always verify the target artifact doesn't already exist to avoid duplicate copies in the workspace.

---

Examples

Example 1: Full Phase 1 — Readiness Scan for a Single Workspace

Prompt: "Scan my Sales workspace for Gen1 dataflows and show me a readiness snapshot"

Agent response:

# Step 1 — Resolve workspace ID
WS_ID=$(az rest --method get \
  --resource "https://api.fabric.microsoft.com" \
  --url "https://api.fabric.microsoft.com/v1/workspaces" \
  --query "value[?displayName=='Sales'].id" -o tsv)

# Step 2 — List all dataflows with generation property
ALL_DFS=$(az rest --method get \
  --resource "https://analysis.windows.net/powerbi/api" \
  --url "https://api.powerbi.com/v1.0/myorg/groups/$WS_ID/dataflows" -o json)

# Step 3 — Filter Gen1 dataflows by generation property
GEN1_DFS=$(echo "$ALL_DFS" | jq '[.value[] | select(.generation == 1) |
  {id: .objectId, name: .name, generation: .generation, modelUrl: .modelUrl}]')

echo "Gen1 dataflows found: $(echo $GEN1_DFS | jq length)"
echo "$GEN1_DFS" | jq '.'

Example 2: Save-As a Gen1 Dataflow to Gen2.1

Prompt: "Migrate or upgrade the SalesETL Gen1 dataflow in my Sales workspace"

Agent response:

WS_ID="<workspaceId>"
GEN1_ID="<gen1DataflowId>"

# Write body to file (avoids az rest envelope wrapping)
cat > /tmp/save-as-body.json <<'EOF'
{"displayName": "SalesETL_Gen2CICD", "includeSchedule": true}
EOF

# Execute save-as
RESPONSE=$(az rest --method post \
  --resource "https://analysis.windows.net/powerbi/api" \
  --url "https://api.powerbi.com/v1.0/myorg/groups/$WS_ID/dataflows/$GEN1_ID/saveAsNativeArtifact" \
  --headers "Content-Type=application/json" \
  --body @/tmp/save-as-body.json)

# Check result
echo "$RESPONSE" | jq '{
  newId: .artifactMetadata.objectId,
  state: .artifactMetadata.provisionState,
  errors: .errors
}'

Example 3: Tenant-Wide Admin Scan

Prompt: "Show me all Gen1 dataflows across the entire tenant"

Agent response:

# Requires Fabric administrator role
ALL_DFS=$(az rest --method get \
  --resource "https://analysis.windows.net/powerbi/api" \
  --url "https://api.powerbi.com/v1.0/myorg/admin/dataflows" -o json)

# Gen1 dataflows have a modelUrl
echo "$ALL_DFS" | jq '[.value[] | select(.modelUrl != null and .modelUrl != "") |
  {id: .objectId, name: .name, workspace: .workspaceId, 
   owner: .configuredBy, storage: .modelUrl}]'