---
name: fabric-validate-4ee76a09fde2
description: Validate pipeline output using Great Expectations — row counts, null PKs, duplicates, schema drift, referential integrity, and business metric sanity. DQ notebooks run after ingestion notebooks and are the sole place where data quality is enforced. Produces a structured PASS/FAIL report.
---

# fabric-validate

## Separation of concerns — non-negotiable

DQ notebooks are **separate from ingestion notebooks**. Never mix DQ logic into a bronze/silver/gold ingestion notebook.

| Notebook | Naming convention |
|---|---|
| Bronze DQ | `dq_bronze_<source>.py` |
| Silver DQ | `dq_silver_<source>.py` |
| Gold DQ | `dq_gold_<model>.py` |

## MUST

- Run checks independently — never rely on or import the ingestion notebook's logic
- Use **Great Expectations** (`great_expectations>=1.0`) for all DQ checks
- Produce a structured PASS/FAIL result; raise `RuntimeError` on failure so Fabric pipeline stops
- Threshold values live in the `# %% [parameters]` cell — never hardcoded, never loaded from YAML at runtime
- Check both the current batch AND row count delta vs previous run

## AVOID

- Loading `config/thresholds.yaml` or any file from disk at runtime — Fabric cannot reach local repository config files at notebook runtime
- Writing to or modifying Bronze/Silver/Gold tables from the DQ notebook — read-only
- Mixing ingestion writes with DQ assertions in the same notebook

## Notebook cell structure

```
# %% [parameters]      — threshold values, table path, batch_id to check
# %% [contract]        — DQContract @dataclass (columns to check, pk, thresholds)
# %% [imports]         — great_expectations, pyspark, os
# %% [load]            — read the Bronze/Silver table into a Spark DataFrame
# %% [expectations]    — build GX suite
# %% [run]             — execute and assert
# %% [report]          — print structured summary
```

## Great Expectations Pattern (GX v1)

```python
# %% [parameters]
BATCH_ID_FILTER: str = ""     # blank = check entire table
ROW_COUNT_MIN: int = 1
NULL_PK_MAX: int = 0
DUPLICATE_PK_MAX: int = 0
NULL_ENVELOPE_MAX: int = 0
ROW_COUNT_DROP_PCT_MAX: float = 20.0  # vs previous run

# %% [contract]
from dataclasses import dataclass, field

@dataclass(frozen=True)
class DQContract:
    table: str
    pk_columns: list[str]
    required_envelope: list[str]
    non_null_columns: list[str]        # business columns that must not be null

CONTRACT = DQContract(
    table="raw_orders",
    pk_columns=["order_id"],
    required_envelope=["_ingest_timestamp", "_source_system", "_batch_id", "_ingest_date"],
    non_null_columns=["order_id"],
)

# %% [imports]
import great_expectations as gx
from pyspark.sql import SparkSession
from pyspark.sql import functions as F

spark = SparkSession.builder.getOrCreate()

# %% [load]
df = spark.read.table(CONTRACT.table)
if BATCH_ID_FILTER:
    df = df.filter(F.col("_batch_id") == BATCH_ID_FILTER)
total_rows = df.count()
print(f"[LOAD] {CONTRACT.table}: {total_rows} rows")

# %% [expectations]
context = gx.get_context(mode="ephemeral")

datasource = context.data_sources.add_spark("spark_ds")
asset = datasource.add_dataframe_asset("table_asset")
batch_def = asset.add_batch_definition_whole_dataframe("batch")

suite = gx.ExpectationSuite(name=f"dq_{CONTRACT.table}")

# Row count
suite.add_expectation(
    gx.expectations.ExpectTableRowCountToBeBetween(min_value=ROW_COUNT_MIN)
)

# Null checks — PK and required business columns
for col in CONTRACT.pk_columns + CONTRACT.non_null_columns:
    suite.add_expectation(
        gx.expectations.ExpectColumnValuesToNotBeNull(column=col)
    )

# Envelope columns present and not null
for col in CONTRACT.required_envelope:
    suite.add_expectation(
        gx.expectations.ExpectColumnToExist(column=col)
    )
    suite.add_expectation(
        gx.expectations.ExpectColumnValuesToNotBeNull(column=col)
    )

context.suites.add(suite)

vd = context.validation_definitions.add(
    gx.ValidationDefinition(name="dq_run", data=batch_def, suite=suite)
)

# %% [run]
result = vd.run(batch_parameters={"dataframe": df})

passed = result["success"]
results_list = result["results"]

# %% [report]
print(f"\n=== DQ Report: {CONTRACT.table} ===")
print(f"total rows : {total_rows}")
print(f"overall    : {'PASS' if passed else 'FAIL'}")
print()
for r in results_list:
    status = "PASS" if r["success"] else "FAIL"
    expectation = r["expectation_config"]["type"]
    print(f"  [{status}] {expectation}")

if not passed:
    raise RuntimeError(
        f"DQ checks failed for {CONTRACT.table} — see report above."
    )
```

## Security DQ Checks

Include these checks in DQ notebooks for tables that receive external or user-generated data (OWASP Data Security Top 10):

| Risk | OWASP | Check | GX Expectation |
|---|---|---|---|
| Unexpected volume spike | DATA3 | Row count within expected ceiling | `ExpectTableRowCountToBeBetween(max_value=EXPECTED_MAX)` |
| PII masking verification | DATA7 | Masked column follows format | `ExpectColumnValuesToMatchRegex(column="email", regex=r"^[A-Z]{3}\*+@.*$")` |
| Audit envelope integrity | DATA5/DATA9 | Envelope columns non-null and in range | `ExpectColumnValuesToBeBetween(column="_ingest_timestamp", ...)` |
| Schema drift | DATA9 | Column set matches contract | `ExpectColumnToExist` for each contract field |

Add `EXPECTED_MAX` and masking regex defaults to the `# %% [parameters]` cell. Set `EXPECTED_MAX = 0` to skip the ceiling check for unbounded tables.

## Standard Expectations Reference

| Check | GX Expectation class |
|---|---|
| Row count ≥ N | `ExpectTableRowCountToBeBetween(min_value=N)` |
| Column not null | `ExpectColumnValuesToNotBeNull(column=c)` |
| Column exists | `ExpectColumnToExist(column=c)` |
| Values in set | `ExpectColumnValuesToBeInSet(column=c, value_set={...})` |
| Values in range | `ExpectColumnValuesToBeBetween(column=c, min_value=0)` |
| No duplicates on key | `ExpectColumnValuesToBeUnique(column=c)` |
| Match regex | `ExpectColumnValuesToMatchRegex(column=c, regex=r"...")` |

## Thresholds (parameter cell defaults)

| Parameter | Default | Meaning |
|---|---|---|
| `ROW_COUNT_MIN` | 1 | Fail if table is empty |
| `NULL_PK_MAX` | 0 | No null primary keys allowed |
| `DUPLICATE_PK_MAX` | 0 | No duplicate keys allowed |
| `NULL_ENVELOPE_MAX` | 0 | All lineage columns must be populated |
| `ROW_COUNT_DROP_PCT_MAX` | 20.0 | Fail if row count drops >20% vs previous run |


---
Source attribution: scardoso-lu
Original project: https://github.com/scardoso-lu/fabric-skills-settings/blob/33534eb185fda573125308a0129457bd65419d13/server/skills/fabric-validate/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Dave AI.

## Upstream license notice

MIT License

Copyright (c) 2026 Silvio Cardoso

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Dave AI resource metadata

Creator / publisher: scardoso-lu
Original project: https://github.com/scardoso-lu/fabric-skills-settings/blob/33534eb185fda573125308a0129457bd65419d13/server/skills/fabric-validate/SKILL.md
Attribution basis: Source repository record

Fields of work: Data & Analytics, Development & Engineering
Roles: Data Analyst, Data Scientist, ML / AI Engineer
Specialties: Database, Testing
Tools: Great_Expectations
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 7527d74a0ade3ab959cbcae1fc3e1efefcfab966b3463fd406f73bb617c897ff