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Data Quality

Learn to build data systems that deliver trustworthy, accurate data. Because garbage in, garbage out is still true.

Why Data Quality Matters

Bad data leads to: - Wrong business decisions - Failed ML models - Angry stakeholders - Wasted engineering time - Lost revenue

Good data quality practices save time, money, and sanity.

The Data Quality Framework

1. Accuracy

Is the data correct and error-free?

Common Issues: - Typos and data entry errors - Outdated information - Wrong data types - Invalid values

Solutions: - Input validation - Regular data audits - Source system improvements - Business rule validation

2. Completeness

Is all expected data present?

Common Issues: - Missing records - NULL values where they shouldn't be - Incomplete data loads - Partial system failures

Solutions: - Completeness checks - Required field validation - Data load monitoring - Source system SLAs

3. Consistency

Is data uniform across systems and time?

Common Issues: - Different formats across systems - Conflicting business rules - Time zone inconsistencies - Naming convention variations

Solutions: - Data standardization - Master data management - Cross-system validation - Canonical data models

4. Timeliness

Is data available when needed?

Common Issues: - Delayed data loads - Stale cache data - Batch processing delays - Network issues

Solutions: - Real-time monitoring - SLA definitions - Alerting systems - Data freshness checks

5. Validity

Does data conform to defined rules?

Common Issues: - Invalid email formats - Out-of-range values - Foreign key violations - Schema mismatches

Solutions: - Schema validation - Business rule engines - Data type enforcement - Constraint checking

Implementing Data Quality

Data Quality Checks

# Example: Email validation
def validate_email(email):
    pattern = r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$'
    return re.match(pattern, email) is not None

# Example: Completeness check
def check_completeness(df, required_columns):
    missing_data = {}
    for col in required_columns:
        missing_count = df[col].isnull().sum()
        missing_data[col] = {
            'missing_count': missing_count,
            'missing_percentage': (missing_count / len(df)) * 100
        }
    return missing_data

Data Profiling

Understand your data before you work with it:

  • Statistical analysis: Min, max, mean, median, standard deviation
  • Data distribution: Histograms, frequency counts
  • Pattern analysis: Common formats, outliers
  • Relationship analysis: Correlations, dependencies

Data Quality Metrics

Track these KPIs: - Error rate: Percentage of records with errors - Completeness score: Percentage of complete records - Freshness: Time since last update - Consistency score: Cross-system data agreement - Business rule compliance: Percentage passing validation

Tools and Frameworks

Open Source

  • Great Expectations: Data validation framework
  • Apache Griffin: Data quality service
  • Deequ: Data quality library for Apache Spark
  • Pandas Profiling: Automated data profiling

Commercial

  • Talend Data Quality: Enterprise data quality platform
  • Informatica Data Quality: Comprehensive DQ suite
  • AWS Glue DataBrew: Visual data preparation with quality checks

Practical Implementations

Project 1: Building a Data Quality Dashboard

Create a real-time dashboard showing: - Data quality scores by dataset - Trend analysis over time - Alert notifications - Root cause analysis

Project 2: Automated Data Quality Pipeline

Build a pipeline that: - Validates incoming data - Quarantines bad records - Alerts on quality issues - Reports quality metrics

Project 3: Cross-System Data Validation

Implement checks that: - Compare data across systems - Detect data drift - Validate business rules - Monitor data lineage

Best Practices

1. Fail Fast

Catch data quality issues as early as possible in the pipeline.

2. Document Everything

Keep clear documentation of: - Data quality rules - Expected data formats - Business logic - Known issues and workarounds

3. Automate Quality Checks

Manual checks don't scale. Automate everything you can.

4. Make Quality Visible

Data quality should be visible to everyone who uses the data.

5. Continuous Improvement

Data quality is not a one-time effort - it's an ongoing process.

Data quality isn't glamorous, but it's the foundation of everything else you'll build. Get this right and everything else becomes easier.