Registry indexed
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
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".
Source documentation, not instructions for this website. Review permissions before running any commands.
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.
| 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 |
brew install duckdb on macOS)az login)INSTALL delta; INSTALL azure; (one-time)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).
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.
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)
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}")
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.
For data freshness checks, quality validation, schema discovery, cross-table joins, and row count audits, see references/common-patterns.md.
references/common-patterns.md -- Data freshness, quality, schema discovery, cross-joinsreferences/in-notebook-setup.md -- Full notebook setup with auto-discovery and write-back patternsname: 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".
---
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
Skill source recorded
Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.
Review before install: Avoid automatic install
Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.
Check the source for dependencies, API keys and third-party costs. A public repository does not mean every service is free.
Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.
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Quality
73/100
Strong
Trust
65/100
Sandbox only
Audit
78/100
Needs review
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
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