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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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Harga belum dikonfirmasi★ 887 Star GitHubDirektori diperbarui · 2 Sep 2026agent-skill

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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

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 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
DuckDB Delta Extension
djouallah/Fabric_Notebooks_Demo -- Original notebook-attachment approach

Lihat sumber ↗

Metadata berkas

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".

Lihat teks asli

---
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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Lisensi: GPL-3.0

Dependency or permission surface needs review
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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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Mulai dengan tugas kecil

1Baca sumber dan pastikan masukan, keluaran, dependensi, serta izin.
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Periksa dependensi, kunci API, dan biaya layanan pihak ketiga pada sumber. Repositori publik tidak berarti semua layanan gratis.

Sumber dan catatan penggunaan

Terindeks

Metadata dan tinjauan bersifat saran. Popularitas, penemuan sumber, dan keberhasilan eksekusi adalah fakta berbeda.

Repositori sumber: data-goblin/power-bi-agentic-development
Lisensi: GPL-3.0
Versi: 1.0.0
Push GitHub terakhir: 8 Agu 2026
Direktori diperbarui: 2 Sep 2026
Jalur instruksi: plugins/etl/skills/using-duckdb/SKILL.md @ f8495e767930

Versi dilaporkan dalam metadata direktori; periksa rilis sumber.

Kualitas

73/100

Kuat

Kepercayaan

65/100

Hanya sandbox

Audit

78/100

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Buka audit lengkap Lihat laporan evaluasi

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

Verified installs: —
Hasil: —

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Detail lainnya

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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"
  }
}

Untuk kreator

Sumber listing

Diindeks Registry

Dapat diklaim

Listing ini diindeks dari sumber publik dan belum ditandai resmi hingga klaim pemelihara disetujui.

Kreator: data-goblin
Sumber: data-goblin/power-bi-agentic-development
Diindeks oleh: Indeks komunitas OpenAgentSkill

Atribusi menautkan ke repositori publik atau profil kreator. Kreator dapat mengklaim listing untuk memperbarui sinyal kepemilikan.

Klaim skill ini

Klaim pemilik

Klaim listing skill ini

Listing Diindeks Registry ini dikaitkan dengan data-goblin, tetapi belum ditandai resmi. Klaim untuk menambahkan sinyal pemilik terverifikasi dan membuat pembaruan peluncuran, pemasangan, serta audit berikutnya lebih tepercaya.

Kit berbagi

Kit backlink kreator

Tambahkan badge bukti ke README Anda

Tampilkan listing kanonis, sinyal kepercayaan dan audit saat ini, serta bukti Agent-Proven nyata di tempat pengembang mengevaluasi repositori.

[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/data-goblin-using-duckdb?metric=listed&label=Listed)](https://www.openagentskill.com/skills/data-goblin-using-duckdb?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Trust](https://www.openagentskill.com/api/badge/data-goblin-using-duckdb?metric=trust&label=Trust)](https://www.openagentskill.com/skills/data-goblin-using-duckdb?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Audit](https://www.openagentskill.com/api/badge/data-goblin-using-duckdb?metric=audit&label=Audit)](https://www.openagentskill.com/skills/data-goblin-using-duckdb/audit)
[![Agent Proven](https://www.openagentskill.com/api/badge/data-goblin-using-duckdb?metric=proven&label=Agent%20Proven)](https://www.openagentskill.com/skills/data-goblin-using-duckdb?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)

Pratinjau badge Buka audit Kit kreator

Sinyal komunitas

Bagikan apakah skill ini bermanfaat untuk alur kerja Agent Anda. Masukan gabungan meningkatkan peringkat dari waktu ke waktu.