---

name: data-quality description: Comprehensive data quality patterns using Great Expectations, DLT expectations, and custom validators for ensuring data reliability and trust. triggers:

• data quality
• great expectations
• data validation
• quality checks
• data profiling category: quality

---

Data Quality Skill

Overview

Data quality is critical for building trustworthy data products. This skill provides comprehensive patterns for implementing data quality checks using Great Expectations, DLT expectations, and custom validation frameworks.

Key Benefits:

• Automated data quality testing
• Comprehensive profiling and validation
• Integration with DLT pipelines
• Custom business rule validation
• Quality metrics and monitoring
• Data contract enforcement

When to Use This Skill

Use data quality patterns when you need to:

• Validate data against business rules
• Profile datasets for quality metrics
• Implement comprehensive testing frameworks
• Monitor data quality over time
• Enforce data contracts between teams
• Prevent bad data from propagating
• Generate quality reports and alerts

Core Concepts

1. Quality Dimensions

Completeness: Are all required fields present?

# Check for null values
completeness_check = {
    "expectation": "expect_column_values_to_not_be_null",
    "kwargs": {"column": "customer_id"}
}

Accuracy: Does data match expected patterns?

# Validate email format
accuracy_check = {
    "expectation": "expect_column_values_to_match_regex",
    "kwargs": {
        "column": "email",
        "regex": r"^[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}$"
    }
}

Consistency: Are values within expected ranges?

# Check value ranges
consistency_check = {
    "expectation": "expect_column_values_to_be_between",
    "kwargs": {
        "column": "age",
        "min_value": 0,
        "max_value": 120
    }
}

Uniqueness: Are keys unique?

# Check for duplicates
uniqueness_check = {
    "expectation": "expect_column_values_to_be_unique",
    "kwargs": {"column": "order_id"}
}

Timeliness: Is data fresh?

# Check data freshness
timeliness_check = {
    "expectation": "expect_column_max_to_be_between",
    "kwargs": {
        "column": "ingestion_timestamp",
        "min_value": datetime.now() - timedelta(hours=2),
        "max_value": datetime.now()
    }
}

2. Great Expectations Integration

Basic Setup:

import great_expectations as gx
from great_expectations.core.batch import RuntimeBatchRequest

# Initialize data context
context = gx.get_context()

# Create checkpoint
checkpoint_config = {
    "name": "sales_data_quality_check",
    "config_version": 1.0,
    "class_name": "SimpleCheckpoint",
    "validations": [
        {
            "batch_request": {
                "datasource_name": "spark_datasource",
                "data_connector_name": "runtime_connector",
                "data_asset_name": "sales_data"
            },
            "expectation_suite_name": "sales_quality_suite"
        }
    ]
}

Expectation Suite:

# Create expectation suite
suite = context.create_expectation_suite(
    expectation_suite_name="sales_quality_suite",
    overwrite_existing=True
)

# Add expectations
suite.add_expectation(
    gx.core.ExpectationConfiguration(
        expectation_type="expect_table_row_count_to_be_between",
        kwargs={"min_value": 1000, "max_value": 1000000}
    )
)

suite.add_expectation(
    gx.core.ExpectationConfiguration(
        expectation_type="expect_column_values_to_not_be_null",
        kwargs={"column": "order_id"}
    )
)

3. DLT Quality Checks

Multi-Level Expectations:

import dlt
from pyspark.sql.functions import *

@dlt.table(
    name="validated_orders",
    comment="Orders with comprehensive quality validation"
)
# Critical checks (fail pipeline)
@dlt.expect_or_fail("primary_key_not_null", "order_id IS NOT NULL")
@dlt.expect_or_fail("valid_customer_ref",
    "customer_id IN (SELECT customer_id FROM LIVE.customers)")

# Important checks (drop invalid rows)
@dlt.expect_or_drop("positive_amount", "order_amount > 0")
@dlt.expect_or_drop("valid_date_range",
    "order_date >= '2020-01-01' AND order_date <= current_date()")
@dlt.expect_or_drop("valid_status",
    "order_status IN ('pending', 'completed', 'cancelled')")

# Monitoring checks (warn only)
@dlt.expect("reasonable_amount", "order_amount < 100000")
@dlt.expect("same_day_processing",
    "datediff(processing_date, order_date) <= 1")

def validated_orders():
    return dlt.read_stream("raw_orders")

4. Custom Validators

Business Rule Validators:

from typing import Dict, Any, List
from pyspark.sql import DataFrame
from pyspark.sql.functions import *

class BusinessRuleValidator:
    """Custom validator for complex business rules."""

    def __init__(self, spark_session):
        self.spark = spark_session
        self.validation_results = []

    def validate_order_logic(self, df: DataFrame) -> Dict[str, Any]:
        """Validate complex order business rules."""

        # Rule 1: Total matches sum of line items
        total_mismatch = df.filter(
            abs(col("order_total") - col("line_items_sum")) > 0.01
        ).count()

        # Rule 2: Discount doesn't exceed maximum allowed
        invalid_discount = df.filter(
            col("discount_amount") > col("order_subtotal") * 0.5
        ).count()

        # Rule 3: Shipping date after order date
        invalid_dates = df.filter(
            col("shipping_date") < col("order_date")
        ).count()

        return {
            "total_records": df.count(),
            "total_mismatch_count": total_mismatch,
            "invalid_discount_count": invalid_discount,
            "invalid_dates_count": invalid_dates,
            "pass_rate": (
                df.count() - total_mismatch - invalid_discount - invalid_dates
            ) / df.count() * 100
        }

    def validate_referential_integrity(
        self,
        df: DataFrame,
        reference_table: str,
        key_column: str
    ) -> Dict[str, Any]:
        """Check referential integrity constraints."""

        ref_df = self.spark.table(reference_table)

        # Find orphaned records
        orphaned = df.join(
            ref_df,
            df[key_column] == ref_df[key_column],
            "left_anti"
        )

        orphaned_count = orphaned.count()

        return {
            "total_records": df.count(),
            "orphaned_records": orphaned_count,
            "orphaned_percentage": orphaned_count / df.count() * 100,
            "valid": orphaned_count == 0
        }

Implementation Patterns

Pattern 1: Great Expectations Suite

Complete Quality Suite:

"""
Great Expectations suite for customer data validation.
"""
import great_expectations as gx
from great_expectations.core.batch import RuntimeBatchRequest
from pyspark.sql import SparkSession
from typing import Dict, Any


class CustomerDataQualitySuite:
    """Comprehensive quality suite for customer data."""

    def __init__(self, context_root_dir: str = None):
        self.context = gx.get_context(context_root_dir=context_root_dir)
        self.suite_name = "customer_data_quality"

    def create_suite(self) -> gx.core.ExpectationSuite:
        """Create comprehensive expectation suite."""

        suite = self.context.create_expectation_suite(
            expectation_suite_name=self.suite_name,
            overwrite_existing=True
        )

        # Schema validation
        suite.add_expectation(
            gx.core.ExpectationConfiguration(
                expectation_type="expect_table_columns_to_match_ordered_list",
                kwargs={
                    "column_list": [
                        "customer_id",
                        "first_name",
                        "last_name",
                        "email",
                        "phone",
                        "registration_date",
                        "status",
                        "lifetime_value"
                    ]
                }
            )
        )

        # Completeness checks
        for col in ["customer_id", "email", "registration_date"]:
            suite.add_expectation(
                gx.core.ExpectationConfiguration(
                    expectation_type="expect_column_values_to_not_be_null",
                    kwargs={"column": col}
                )
            )

        # Uniqueness
        suite.add_expectation(
            gx.core.ExpectationConfiguration(
                expectation_type="expect_column_values_to_be_unique",
                kwargs={"column": "customer_id"}
            )
        )

        suite.add_expectation(
            gx.core.ExpectationConfiguration(
                expectation_type="expect_column_values_to_be_unique",
                kwargs={"column": "email"}
            )
        )

        # Format validation
        suite.add_expectation(
            gx.core.ExpectationConfiguration(
                expectation_type="expect_column_values_to_match_regex",
                kwargs={
                    "column": "email",
                    "regex": r"^[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}$"
                }
            )
        )

        suite.add_expectation(
            gx.core.ExpectationConfiguration(
                expectation_type="expect_column_values_to_match_regex",
                kwargs={
                    "column": "phone",
                    "regex": r"^\+?[1-9]\d{1,14}$",
                    "mostly": 0.95  # Allow 5% invalid
                }
            )
        )

        # Value constraints
        suite.add_expectation(
            gx.core.ExpectationConfiguration(
                expectation_type="expect_column_values_to_be_in_set",
                kwargs={
                    "column": "status",
                    "value_set": ["active", "inactive", "suspended", "closed"]
                }
            )
        )

        suite.add_expectation(
            gx.core.ExpectationConfiguration(
                expectation_type="expect_column_values_to_be_between",
                kwargs={
                    "column": "lifetime_value",
                    "min_value": 0,
                    "max_value": 1000000,
                    "mostly": 0.99
                }
            )
        )

        # Date validation
        suite.add_expectation(
            gx.core.ExpectationConfiguration(
                expectation_type="expect_column_values_to_be_between",
                kwargs={
                    "column": "registration_date",
                    "min_value": "2020-01-01",
                    "max_value": "2026-12-31",
                    "parse_strings_as_datetimes": True
                }
            )
        )

        # Row count check
        suite.add_expectation(
            gx.core.ExpectationConfiguration(
                expectation_type="expect_table_row_count_to_be_between",
                kwargs={
                    "min_value": 100,
                    "max_value": 10000000
                }
            )
        )

        self.context.save_expectation_suite(suite)
        return suite

    def run_validation(self, df: DataFrame) -> Dict[str, Any]:
        """Run validation against dataframe."""

        # Create runtime batch request
        batch_request = RuntimeBatchRequest(
            datasource_name="spark_datasource",
            data_connector_name="runtime_connector",
            data_asset_name="customer_data",
            runtime_parameters={"batch_data": df},
            batch_identifiers={"default_identifier_name": "default_identifier"}
        )

        # Run checkpoint
        checkpoint_result = self.context.run_checkpoint(
            checkpoint_name="customer_data_checkpoint",
            validations=[
                {
                    "batch_request": batch_request,
                    "expectation_suite_name": self.suite_name
                }
            ]
        )

        return {
            "success": checkpoint_result.success,
            "statistics": checkpoint_result.run_results,
            "validation_results": checkpoint_result.to_json_dict()
        }

Pattern 2: Custom Quality Framework

Comprehensive Quality Framework:

"""
Custom data quality framework for comprehensive validation.
"""
from typing import Dict, List, Any, Callable, Optional
from pyspark.sql import DataFrame, SparkSession
from pyspark.sql.functions import *
from dataclasses import dataclass
from datetime import datetime
import json


@dataclass
class QualityRule:
    """Define a quality rule."""
    name: str
    description: str
    severity: str  # 'critical', 'high', 'medium', 'low'
    validation_func: Callable[[DataFrame], int]
    threshold: float = 1.0  # Pass rate threshold


@dataclass
class QualityResult:
    """Quality validation result."""
    rule_name: str
    passed: bool
    total_records: int
    failed_records: int
    pass_rate: float
    severity: str
    timestamp: datetime
    details: Dict[str, Any]


class DataQualityFramework:
    """Comprehensive data quality validation framework."""

    def __init__(self, spark: SparkSession):
        self.spark = spark
        self.rules: List[QualityRule] = []
        self.results: List[QualityResult] = []

    def add_rule(self, rule: QualityRule) -> None:
        """Add validation rule to framework."""
        self.rules.append(rule)

    def add_completeness_rule(
        self,
        column: str,
        severity: str = "high"
    ) -> None:
        """Add completeness check for a column."""

        def check(df: DataFrame) -> int:
            return df.filter(col(column).isNull()).count()

        self.add_rule(QualityRule(
            name=f"completeness_{column}",
            description=f"Check {column} has no null values",
            severity=severity,
            validation_func=check
        ))

    def add_uniqueness_rule(
        self,
        column: str,
        severity: str = "critical"
    ) -> None:
        """Add uniqueness check for a column."""

        def check(df: DataFrame) -> int:
            total = df.count()
            unique = df.select(column).distinct().count()
            return total - unique

        self.add_rule(QualityRule(
            name=f"uniqueness_{column}",
            description=f"Check {column} values are unique",
            severity=severity,
            validation_func=check
        ))

    def add_format_rule(
        self,
        column: str,
        regex_pattern: str,
        severity: str = "medium"
    ) -> None:
        """Add format validation rule."""

        def check(df: DataFrame) -> int:
            return df.filter(
                ~col(column).rlike(regex_pattern)
            ).count()

        self.add_rule(QualityRule(
            name=f"format_{column}",
            description=f"Check {column} matches pattern {regex_pattern}",
            severity=severity,
            validation_func=check
        ))

    def add_range_rule(
        self,
        column: str,
        min_value: Any,
        max_value: Any,
        severity: str = "medium"
    ) -> None:
        """Add range validation rule."""

        def check(df: DataFrame) -> int:
            return df.filter(
                (col(column) < min_value) | (col(column) > max_value)
            ).count()

        self.add_rule(QualityRule(
            name=f"range_{column}",
            description=f"Check {column} is between {min_value} and {max_value}",
            severity=severity,
            validation_func=check
        ))

    def add_referential_integrity_rule(
        self,
        column: str,
        reference_table: str,
        reference_column: str,
        severity: str = "critical"
    ) -> None:
        """Add referential integrity check."""

        def check(df: DataFrame) -> int:
            ref_df = self.spark.table(reference_table)
            orphaned = df.join(
                ref_df,
                df[column] == ref_df[reference_column],
                "left_anti"
            )
            return orphaned.count()

        self.add_rule(QualityRule(
            name=f"ref_integrity_{column}",
            description=f"Check {column} exists in {reference_table}.{reference_column}",
            severity=severity,
            validation_func=check
        ))

    def add_custom_rule(
        self,
        name: str,
        description: str,
        validation_func: Callable[[DataFrame], int],
        severity: str = "medium",
        threshold: float = 1.0
    ) -> None:
        """Add custom validation rule."""

        self.add_rule(QualityRule(
            name=name,
            description=description,
            severity=severity,
            validation_func=validation_func,
            threshold=threshold
        ))

    def validate(self, df: DataFrame) -> Dict[str, Any]:
        """Run all validation rules."""

        self.results = []
        total_records = df.count()

        for rule in self.rules:
            try:
                failed_count = rule.validation_func(df)
                pass_rate = (total_records - failed_count) / total_records * 100

                result = QualityResult(
                    rule_name=rule.name,
                    passed=pass_rate >= (rule.threshold * 100),
                    total_records=total_records,
                    failed_records=failed_count,
                    pass_rate=pass_rate,
                    severity=rule.severity,
                    timestamp=datetime.now(),
                    details={"description": rule.description}
                )

                self.results.append(result)

            except Exception as e:
                self.results.append(QualityResult(
                    rule_name=rule.name,
                    passed=False,
                    total_records=total_records,
                    failed_records=-1,
                    pass_rate=0.0,
                    severity=rule.severity,
                    timestamp=datetime.now(),
                    details={"error": str(e)}
                ))

        return self.get_summary()

    def get_summary(self) -> Dict[str, Any]:
        """Get validation summary."""

        total_rules = len(self.results)
        passed_rules = sum(1 for r in self.results if r.passed)

        critical_failures = [
            r for r in self.results
            if not r.passed and r.severity == "critical"
        ]

        return {
            "timestamp": datetime.now().isoformat(),
            "total_rules": total_rules,
            "passed_rules": passed_rules,
            "failed_rules": total_rules - passed_rules,
            "pass_rate": passed_rules / total_rules * 100 if total_rules > 0 else 0,
            "critical_failures": len(critical_failures),
            "has_critical_failures": len(critical_failures) > 0,
            "results": [
                {
                    "rule": r.rule_name,
                    "passed": r.passed,
                    "pass_rate": r.pass_rate,
                    "failed_records": r.failed_records,
                    "severity": r.severity
                }
                for r in self.results
            ]
        }

    def save_results(self, output_path: str) -> None:
        """Save validation results to Delta table."""

        results_data = [
            {
                "rule_name": r.rule_name,
                "passed": r.passed,
                "total_records": r.total_records,
                "failed_records": r.failed_records,
                "pass_rate": r.pass_rate,
                "severity": r.severity,
                "timestamp": r.timestamp,
                "details": json.dumps(r.details)
            }
            for r in self.results
        ]

        results_df = self.spark.createDataFrame(results_data)
        results_df.write.format("delta").mode("append").save(output_path)

Pattern 3: DLT Quality Monitoring

Quality Metrics Collection:

"""
DLT pipeline with comprehensive quality monitoring.
"""
import dlt
from pyspark.sql.functions import *
from typing import Dict, Any


class DLTQualityMonitor:
    """Monitor data quality in DLT pipelines."""

    @staticmethod
    def create_quality_tables():
        """Create tables with quality monitoring."""

        @dlt.table(
            name="quality_metrics",
            comment="Quality metrics for all pipeline tables",
            table_properties={
                "quality": "monitoring",
                "delta.enableChangeDataFeed": "true"
            }
        )
        def quality_metrics():
            """Aggregate quality metrics across pipeline."""
            return spark.sql("""
                SELECT
                    current_timestamp() as metric_timestamp,
                    'bronze_orders' as table_name,
                    COUNT(*) as total_records,
                    COUNT_IF(order_id IS NULL) as null_order_ids,
                    COUNT_IF(amount <= 0) as invalid_amounts,
                    COUNT_IF(order_date > current_date()) as future_dates
                FROM LIVE.bronze_orders
            """)

        @dlt.table(
            name="orders_with_quality",
            comment="Orders with quality scores"
        )
        @dlt.expect("valid_order_id", "order_id IS NOT NULL")
        @dlt.expect_or_drop("positive_amount", "amount > 0")
        @dlt.expect("valid_email", "email RLIKE '^[A-Za-z0-9._%+-]+@'")
        def orders_with_quality():
            return (
                dlt.read_stream("bronze_orders")
                .withColumn(
                    "quality_score",
                    when(col("order_id").isNotNull(), 1).otherwise(0) +
                    when(col("amount") > 0, 1).otherwise(0) +
                    when(col("email").rlike("^[A-Za-z0-9._%+-]+@"), 1).otherwise(0)
                )
                .withColumn(
                    "quality_tier",
                    when(col("quality_score") >= 3, "high")
                    .when(col("quality_score") >= 2, "medium")
                    .otherwise("low")
                )
            )

    @staticmethod
    def get_quality_report(event_log_path: str) -> Dict[str, Any]:
        """Generate quality report from DLT event log."""

        events = spark.read.format("delta").load(event_log_path)

        # Extract expectation metrics
        expectations = (
            events
            .filter(col("event_type") == "flow_progress")
            .select(
                col("timestamp"),
                col("origin.flow_name").alias("table_name"),
                col("details.flow_progress.metrics.num_output_rows").alias("output_rows"),
                explode(col("details.flow_progress.data_quality.expectations")).alias("expectation")
            )
            .select(
                "timestamp",
                "table_name",
                "output_rows",
                col("expectation.name").alias("expectation_name"),
                col("expectation.passed_records").alias("passed"),
                col("expectation.failed_records").alias("failed")
            )
        )

        return {
            "total_tables": expectations.select("table_name").distinct().count(),
            "total_expectations": expectations.count(),
            "failed_expectations": expectations.filter(col("failed") > 0).count(),
            "details": expectations.collect()
        }

Pattern 4: Data Profiling

Comprehensive Profiling:

"""
Data profiling utilities for quality assessment.
"""
from pyspark.sql import DataFrame, SparkSession
from pyspark.sql.functions import *
from typing import Dict, Any, List


class DataProfiler:
    """Profile datasets for quality assessment."""

    def __init__(self, spark: SparkSession):
        self.spark = spark

    def profile_dataset(self, df: DataFrame) -> Dict[str, Any]:
        """Generate comprehensive data profile."""

        profile = {
            "record_count": df.count(),
            "column_count": len(df.columns),
            "columns": {}
        }

        for column in df.columns:
            profile["columns"][column] = self.profile_column(df, column)

        return profile

    def profile_column(self, df: DataFrame, column: str) -> Dict[str, Any]:
        """Profile individual column."""

        col_type = df.schema[column].dataType.simpleString()

        base_stats = df.select(
            count(col(column)).alias("count"),
            count(when(col(column).isNull(), 1)).alias("null_count"),
            countDistinct(col(column)).alias("distinct_count")
        ).first()

        total_count = df.count()

        profile = {
            "type": col_type,
            "count": base_stats["count"],
            "null_count": base_stats["null_count"],
            "null_percentage": base_stats["null_count"] / total_count * 100,
            "distinct_count": base_stats["distinct_count"],
            "cardinality": base_stats["distinct_count"] / total_count
        }

        # Numeric profiling
        if col_type in ["int", "bigint", "double", "float", "decimal"]:
            numeric_stats = df.select(
                min(col(column)).alias("min"),
                max(col(column)).alias("max"),
                avg(col(column)).alias("mean"),
                stddev(col(column)).alias("stddev")
            ).first()

            profile.update({
                "min": numeric_stats["min"],
                "max": numeric_stats["max"],
                "mean": numeric_stats["mean"],
                "stddev": numeric_stats["stddev"]
            })

        # String profiling
        if col_type == "string":
            string_stats = df.select(
                min(length(col(column))).alias("min_length"),
                max(length(col(column))).alias("max_length"),
                avg(length(col(column))).alias("avg_length")
            ).first()

            profile.update({
                "min_length": string_stats["min_length"],
                "max_length": string_stats["max_length"],
                "avg_length": string_stats["avg_length"]
            })

            # Top values
            top_values = (
                df.groupBy(col(column))
                .count()
                .orderBy(desc("count"))
                .limit(10)
                .collect()
            )

            profile["top_values"] = [
                {"value": row[column], "count": row["count"]}
                for row in top_values
            ]

        return profile

    def detect_anomalies(self, df: DataFrame, column: str) -> DataFrame:
        """Detect anomalies using IQR method."""

        # Calculate quartiles
        quantiles = df.stat.approxQuantile(column, [0.25, 0.75], 0.05)
        q1, q3 = quantiles[0], quantiles[1]
        iqr = q3 - q1

        lower_bound = q1 - 1.5 * iqr
        upper_bound = q3 + 1.5 * iqr

        # Flag anomalies
        return df.withColumn(
            f"{column}_anomaly",
            when(
                (col(column) < lower_bound) | (col(column) > upper_bound),
                "anomaly"
            ).otherwise("normal")
        )

    def compare_distributions(
        self,
        df1: DataFrame,
        df2: DataFrame,
        column: str
    ) -> Dict[str, Any]:
        """Compare distributions between two datasets."""

        stats1 = df1.select(
            count(col(column)).alias("count"),
            avg(col(column)).alias("mean"),
            stddev(col(column)).alias("stddev")
        ).first()

        stats2 = df2.select(
            count(col(column)).alias("count"),
            avg(col(column)).alias("mean"),
            stddev(col(column)).alias("stddev")
        ).first()

        return {
            "dataset1": {
                "count": stats1["count"],
                "mean": stats1["mean"],
                "stddev": stats1["stddev"]
            },
            "dataset2": {
                "count": stats2["count"],
                "mean": stats2["mean"],
                "stddev": stats2["stddev"]
            },
            "mean_difference": abs(stats1["mean"] - stats2["mean"]),
            "stddev_difference": abs(stats1["stddev"] - stats2["stddev"])
        }

Best Practices

1. Quality Rule Organization

/quality/
  ├── rules/
  │   ├── completeness_rules.py
  │   ├── accuracy_rules.py
  │   └── business_rules.py
  ├── suites/
  │   ├── customer_suite.py
  │   └── order_suite.py
  └── monitors/
      └── pipeline_monitor.py

2. Layered Quality Approach

Bronze: Schema and format validation

@dlt.expect("valid_schema", "all required columns present")

Silver: Business rule validation

@dlt.expect_or_drop("valid_business_key", "customer_id IS NOT NULL")

Gold: Aggregate and metric validation

@dlt.expect("reasonable_totals", "daily_revenue < 10000000")

3. Quality Metrics

Track these key metrics:

• Completeness rate (% non-null)
• Accuracy rate (% matching patterns)
• Consistency rate (% within ranges)
• Uniqueness rate (% unique keys)
• Timeliness (data freshness)
• Validity (% passing business rules)

4. Alerting Strategy

def check_quality_thresholds(quality_results: Dict[str, Any]) -> None:
    """Alert on quality threshold violations."""

    if quality_results["pass_rate"] < 95:
        send_alert(
            severity="high",
            message=f"Quality pass rate {quality_results['pass_rate']}% below threshold"
        )

    if quality_results["has_critical_failures"]:
        send_alert(
            severity="critical",
            message="Critical quality rules failed"
        )

Complete Examples

See /examples/ directory for:

• customer_data_validation.py: Complete customer data quality suite
• pipeline_quality_monitoring.py: DLT pipeline with quality monitoring

Common Pitfalls to Avoid

Don't:

• Validate everything (focus on critical fields)
• Use only warning-level checks
• Ignore quality metrics over time
• Hard-code validation thresholds
• Skip profiling new data sources

Do:

• Prioritize rules by business impact
• Mix enforcement levels appropriately
• Track quality trends
• Make thresholds configurable
• Profile before implementing rules

Related Skills

• delta-live-tables: DLT expectations
• testing-patterns: Quality testing
• data-products: Data contracts
• medallion-architecture: Layer-specific quality

References

• Great Expectations Docs
• DLT Expectations
• Data Quality Patterns