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using-duckdb

Query Fabric lakehouse and warehouse data using DuckDB, either locally or inside a Fabric notebook. Automatically invoke when the user mentions "DuckDB", "query Delta tables locally", or asks to "attach DuckDB to a lakehouse", "query OneLake data", "explore lakehouse data", "data

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Prix non confirmé★ 887 Stars GitHubRegistre mis à jour · 2 sept. 2026agent-skill

Vue d’ensemble

Query Fabric lakehouse and warehouse data using DuckDB, either locally or inside a Fabric notebook. Automatically invoke when the user mentions "DuckDB", "query Delta tables locally", or asks to "attach DuckDB to a lakehouse", "query OneLake data", "explore lakehouse data", "data freshness check", "validate data quality", "use DuckDB in Fabric".

Lire la documentation complète

Documentation source, pas des instructions pour ce site. Vérifiez les permissions avant d’exécuter des commandes.

Using DuckDB with Fabric

Query Delta Lake tables and raw files in OneLake using DuckDB. Works both locally (CLI/Python) and inside Fabric notebooks. Read-only; for writes, use the executing-spark skill.

Two Modes

ModeWhere it runsAuthBest for
LocalDeveloper machineAzure CLI (az login)Exploration, validation, ad-hoc analysis
In-notebookFabric Spark containernotebookutils.credentials.getToken('storage')Combining DuckDB speed with Spark write-back

Local: Prerequisites

  • DuckDB installed (brew install duckdb on macOS)
  • Azure CLI authenticated (az login)
  • Extensions installed: INSTALL delta; INSTALL azure; (one-time)

Local: Querying Delta Tables

WS_ID=$(fab get "Workspace.Workspace" -q "id" | tr -d '"')
LH_ID=$(fab get "Workspace.Workspace/LH.Lakehouse" -q "id" | tr -d '"')

duckdb -c "
LOAD delta; LOAD azure;
CREATE SECRET (TYPE azure, PROVIDER credential_chain, CHAIN 'cli');

SELECT * FROM delta_scan(
  'abfss://${WS_ID}@onelake.dfs.fabric.microsoft.com/${LH_ID}/Tables/schema/table'
) LIMIT 10;
"

The CHAIN 'cli' parameter uses Azure CLI credentials. Without it, DuckDB tries managed identity first (fails on local machines).

Local: Querying Raw Files

BASE="abfss://${WS_ID}@onelake.dfs.fabric.microsoft.com/${LH_ID}/Files"

duckdb -c "
LOAD azure;
CREATE SECRET (TYPE azure, PROVIDER credential_chain, CHAIN 'cli');

SELECT * FROM read_csv('${BASE}/data.csv') LIMIT 10;
SELECT * FROM read_parquet('${BASE}/facts.parquet') LIMIT 10;
SELECT * FROM read_json('${BASE}/events/*.json');
"

Glob patterns (*, **) work for reading multiple files.

In-Notebook: Attaching DuckDB to a Lakehouse

Inside a Fabric notebook, DuckDB can query lakehouse Delta tables directly using a storage token. This approach is faster than Spark SQL for analytical queries on single-node data.

import duckdb
import time

# Get storage token from notebook context
token = notebookutils.credentials.getToken('storage')

# Create DuckDB connection
con = duckdb.connect(f'temp_{time.time_ns()}.duckdb')
con.sql('SET enable_object_cache=true')

# Register OneLake secret
con.sql(f"""
    CREATE OR REPLACE SECRET onelake (
        TYPE AZURE,
        PROVIDER ACCESS_TOKEN,
        ACCESS_TOKEN '{token}'
    )
""")

# Query Delta tables
workspace = "<workspace-id>"
lakehouse = "<lakehouse-name>"
path = f"abfss://{workspace}@onelake.dfs.fabric.microsoft.com/{lakehouse}.Lakehouse/Tables"

df = con.sql(f"""
    SELECT * FROM delta_scan('{path}/schema/table_name') LIMIT 100
""").df()
print(df)
Auto-Discovering Tables

Dynamically find all Delta tables in a lakehouse:

tables = con.sql(f"""
    SELECT DISTINCT split_part(file, '_delta_log', 1) as table_path
    FROM glob('{path}/*/*/*_delta_log/*.json')
""").df()['table_path'].tolist()

for t in tables:
    view_name = t.split('/')[-1]
    con.sql(f"CREATE OR REPLACE VIEW {view_name} AS SELECT * FROM delta_scan('{t}')")
    print(f"Created view: {view_name}")

OneLake Path Format

abfss://<workspace-id>@onelake.dfs.fabric.microsoft.com/<item-id>/Tables/<schema>/<table>
abfss://<workspace-id>@onelake.dfs.fabric.microsoft.com/<item-id>/Files/<path>
Item typeID source
Lakehousefab get "ws/LH.Lakehouse" -q "id"
Warehousefab get "ws/WH.Warehouse" -q "id"
SQL Databasefab get "ws/DB.SQLDatabase" -q "id"

Cross-item joins work in a single DuckDB query; use different abfss:// paths.

Common Patterns

For data freshness checks, quality validation, schema discovery, cross-table joins, and row count audits, see references/common-patterns.md.

References

Voir la source ↗
Métadonnées du fichier
name: using-duckdb
description: Query Fabric lakehouse and warehouse data using DuckDB, either locally or inside a Fabric notebook. Automatically invoke when the user mentions "DuckDB", "query Delta tables locally", or asks to "attach DuckDB to a lakehouse", "query OneLake data", "explore lakehouse data", "data freshness check", "validate data quality", "use DuckDB in Fabric".
Voir le texte original
---
name: using-duckdb
description: Query Fabric lakehouse and warehouse data using DuckDB, either locally or inside a Fabric notebook. Automatically invoke when the user mentions "DuckDB", "query Delta tables locally", or asks to "attach DuckDB to a lakehouse", "query OneLake data", "explore lakehouse data", "data freshness check", "validate data quality", "use DuckDB in Fabric".
---

# Using DuckDB with Fabric

Query Delta Lake tables and raw files in OneLake using DuckDB. Works both locally (CLI/Python) and inside Fabric notebooks. Read-only; for writes, use the `executing-spark` skill.

## Two Modes

| Mode | Where it runs | Auth | Best for |
|------|--------------|------|----------|
| **Local** | Developer machine | Azure CLI (`az login`) | Exploration, validation, ad-hoc analysis |
| **In-notebook** | Fabric Spark container | `notebookutils.credentials.getToken('storage')` | Combining DuckDB speed with Spark write-back |

## Local: Prerequisites

- DuckDB installed (`brew install duckdb` on macOS)
- Azure CLI authenticated (`az login`)
- Extensions installed: `INSTALL delta; INSTALL azure;` (one-time)

## Local: Querying Delta Tables

```bash
WS_ID=$(fab get "Workspace.Workspace" -q "id" | tr -d '"')
LH_ID=$(fab get "Workspace.Workspace/LH.Lakehouse" -q "id" | tr -d '"')

duckdb -c "
LOAD delta; LOAD azure;
CREATE SECRET (TYPE azure, PROVIDER credential_chain, CHAIN 'cli');

SELECT * FROM delta_scan(
  'abfss://${WS_ID}@onelake.dfs.fabric.microsoft.com/${LH_ID}/Tables/schema/table'
) LIMIT 10;
"
```

The `CHAIN 'cli'` parameter uses Azure CLI credentials. Without it, DuckDB tries managed identity first (fails on local machines).

## Local: Querying Raw Files

```bash
BASE="abfss://${WS_ID}@onelake.dfs.fabric.microsoft.com/${LH_ID}/Files"

duckdb -c "
LOAD azure;
CREATE SECRET (TYPE azure, PROVIDER credential_chain, CHAIN 'cli');

SELECT * FROM read_csv('${BASE}/data.csv') LIMIT 10;
SELECT * FROM read_parquet('${BASE}/facts.parquet') LIMIT 10;
SELECT * FROM read_json('${BASE}/events/*.json');
"
```

Glob patterns (`*`, `**`) work for reading multiple files.

## In-Notebook: Attaching DuckDB to a Lakehouse

Inside a Fabric notebook, DuckDB can query lakehouse Delta tables directly using a storage token. This approach is faster than Spark SQL for analytical queries on single-node data.

```python
import duckdb
import time

# Get storage token from notebook context
token = notebookutils.credentials.getToken('storage')

# Create DuckDB connection
con = duckdb.connect(f'temp_{time.time_ns()}.duckdb')
con.sql('SET enable_object_cache=true')

# Register OneLake secret
con.sql(f"""
    CREATE OR REPLACE SECRET onelake (
        TYPE AZURE,
        PROVIDER ACCESS_TOKEN,
        ACCESS_TOKEN '{token}'
    )
""")

# Query Delta tables
workspace = "<workspace-id>"
lakehouse = "<lakehouse-name>"
path = f"abfss://{workspace}@onelake.dfs.fabric.microsoft.com/{lakehouse}.Lakehouse/Tables"

df = con.sql(f"""
    SELECT * FROM delta_scan('{path}/schema/table_name') LIMIT 100
""").df()
print(df)
```

### Auto-Discovering Tables

Dynamically find all Delta tables in a lakehouse:

```python
tables = con.sql(f"""
    SELECT DISTINCT split_part(file, '_delta_log', 1) as table_path
    FROM glob('{path}/*/*/*_delta_log/*.json')
""").df()['table_path'].tolist()

for t in tables:
    view_name = t.split('/')[-1]
    con.sql(f"CREATE OR REPLACE VIEW {view_name} AS SELECT * FROM delta_scan('{t}')")
    print(f"Created view: {view_name}")
```

## OneLake Path Format

```
abfss://<workspace-id>@onelake.dfs.fabric.microsoft.com/<item-id>/Tables/<schema>/<table>
abfss://<workspace-id>@onelake.dfs.fabric.microsoft.com/<item-id>/Files/<path>
```

| Item type | ID source |
|-----------|-----------|
| Lakehouse | `fab get "ws/LH.Lakehouse" -q "id"` |
| Warehouse | `fab get "ws/WH.Warehouse" -q "id"` |
| SQL Database | `fab get "ws/DB.SQLDatabase" -q "id"` |

Cross-item joins work in a single DuckDB query; use different `abfss://` paths.

## Common Patterns

For data freshness checks, quality validation, schema discovery, cross-table joins, and row count audits, see **`references/common-patterns.md`**.

## References

- **`references/common-patterns.md`** -- Data freshness, quality, schema discovery, cross-joins
- **`references/in-notebook-setup.md`** -- Full notebook setup with auto-discovery and write-back patterns
- [DuckDB Azure Extension](https://duckdb.org/docs/extensions/azure.html)
- [DuckDB Delta Extension](https://duckdb.org/docs/extensions/delta.html)
- [djouallah/Fabric_Notebooks_Demo](https://github.com/djouallah/Fabric_Notebooks_Demo/blob/main/Attach_LH/Attach_Lakehouse_v2.ipynb) -- Original notebook-attachment approach

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Licence: GPL-3.0

  • Dependency or permission surface needs review
  • Permission surface may require sandboxing
  • Quality score needs review
  • Permission surface needs review: secrets or environment access, shell or command execution
  • Dependency/runtime risk: command execution surface, credential or environment access
  • Permission surface: secrets or environment access, shell or command execution
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Source et conseils d’utilisation

Répertorié

Métadonnées et examens sont indicatifs. Popularité, découverte et exécution réussie sont des faits distincts.

Dépôt source
data-goblin/power-bi-agentic-development
Licence
GPL-3.0
Version
1.0.0
Dernier push GitHub
8 août 2026
Registre mis à jour
2 sept. 2026
Chemin des instructions
plugins/etl/skills/using-duckdb/SKILL.md @ f8495e767930

Version déclarée dans le registre ; vérifiez les versions de la source.

Qualité

73/100

Solide

Confiance

65/100

Sandbox uniquement

Audit

78/100

Revue nécessaire

Ouvrir l’audit completVoir le rapport d’évaluation
  • Dependency or permission surface needs review
  • Permission surface may require sandboxing
  • Quality score needs review
  • Permission surface needs review: secrets or environment access, shell or command execution
  • Dependency/runtime risk: command execution surface, credential or environment access
  • Permission surface: secrets or environment access, shell or command execution
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      "Trust: 73/100 Strong shortlist",
      "Audit: 78/100 Needs review",
      "Safety: 34/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "data-goblin-using-duckdb (using-duckdb)",
      "install_command": "npx skills add data-goblin/power-bi-agentic-development --skill using-duckdb",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "data-goblin-using-duckdb",
      "task": "Use using-duckdb in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/data-goblin-using-duckdb",
    "api": "https://www.openagentskill.com/api/agent/skills/data-goblin-using-duckdb",
    "audit": "https://www.openagentskill.com/skills/data-goblin-using-duckdb/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=data-goblin-using-duckdb&task=Use%20using-duckdb%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20using-duckdb%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20using-duckdb%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/data-goblin-using-duckdb/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/data-goblin-using-duckdb"
  }
}

Pour le créateur

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Créateur
data-goblin
Indexé par
Index communautaire OpenAgentSkill

L’attribution renvoie au dépôt public ou au profil du créateur. Les créateurs peuvent revendiquer la fiche pour mettre à jour les signaux de propriété.

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