Improve Data Quality and Automate Errors
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Improve Data Quality and Automate Errors
This course is part of SQL at Scale: Querying, Transforming, and Governing Specialization
Instructor: Hurix Digital
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What you'll learn
Data quality measurement uses standardized quantitative methods to objectively assess reliability across all critical data dimensions.
Proactive monitoring of quality trends enables early intervention and systematic remediation before issues cascade to downstream systems.
Self-healing data systems with automated error recovery reduce operational overhead while maintaining data integrity at scale.
Quality assurance is most effective when built into the data pipeline architecture rather than applied as an afterthought.
Skills you'll gain
- Automation
- Key Performance Indicators (KPIs)
- Data Cleansing
- Extract, Transform, Load
- Data Validation
- Data Management
- Data Pipelines
- Continuous Monitoring
- Data Quality
- Performance Analysis
- Quality Assurance
- Quantitative Research
- SQL
- Data Processing
- Trend Analysis
- Quality Improvement
- Continuous Quality Improvement (CQI)
- Quality Assessment
Details to know
February 2026
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There are 3 modules in this course
Master the critical skills for ensuring data reliability and building self-healing data systems. This course transforms your approach to data quality from reactive firefighting to proactive engineering driven reliability.
This Short Course was created to help data management and engineering professionals accomplish systematic data quality assurance and error automation at enterprise scale. By completing this course, you'll be able to implement quantitative data quality measurements, establish monitoring systems that catch degradation trends before they impact business operations, and build intelligent SQL routines that automatically recover from data pipeline failures. By the end of this course, you will be able to: β’ Apply calculations to measure key data quality dimensions β’ Evaluate quality key performance indicators over time and recommend remediation β’ Create an automated SQL routine to handle and reprocess data errors. This course is unique because it blends quantitative data quality methods with practical automation engineering, enabling you to build self-healing data systems that maintain measurable quality standards at scale. To be successful in this course, you should have a background in SQL, data pipeline concepts, and basic data engineering principles.
Learners will master the quantitative measurement of critical data quality dimensions through systematic calculation methods that provide objective assessment of data reliability.
What's included
3 videos1 reading2 assignments
3 videosβ’Total 16 minutes
- Core Data Quality Dimensions and Mathematical Foundationsβ’7 minutes
- Advanced Quality Calculation Techniques for Enterprise Systemsβ’5 minutes
- Building Quality Calculation Dashboards in SQLβ’4 minutes
1 readingβ’Total 10 minutes
- Implementing Quality Calculations in SQL and Pythonβ’10 minutes
2 assignmentsβ’Total 20 minutes
- Implementing Quality Metrics for E-commerce Product Databaseβ’15 minutes
- Data Quality Dimension Calculations Knowledge Checkβ’5 minutes
Learners will master the evaluation of quality key performance indicators over time and develop actionable remediation strategies that prevent quality degradation before it impacts business operations.
What's included
3 videos2 readings2 assignments
3 videosβ’Total 14 minutes
- How Quality Monitoring Prevents Business Disastersβ’3 minutes
- Building Quality KPI Monitoring Systems with Trend Analysisβ’6 minutes
- Creating Quality KPI Dashboards with Trend Analysisβ’5 minutes
2 readingsβ’Total 20 minutes
- Quality KPI Frameworks for Enterprise Data Systemsβ’10 minutes
- Advanced Remediation Strategies for Data Quality Issuesβ’10 minutes
2 assignmentsβ’Total 21 minutes
- Quality KPI Analysis and Remediation Planning for Financial Servicesβ’18 minutes
- Remediation Strategy Planning Reviewβ’3 minutes
Learners will create resilient automated SQL routines that detect, quarantine, and reprocess data errors without manual intervention, building self-healing data systems at enterprise scale.
What's included
3 videos1 reading2 assignments1 ungraded lab
3 videosβ’Total 21 minutes
- How Automated Error Handling Transforms Data Engineering Excellenceβ’4 minutes
- Automated Error Handling Architecture and SQL Implementation Patternsβ’11 minutes
- Building Complete Automated Error Handling Systems in SQLβ’6 minutes
1 readingβ’Total 10 minutes
- Advanced SQL Patterns for Self-Healing Data Systemsβ’10 minutes
2 assignmentsβ’Total 15 minutes
- Data Quality and Automated Error Handling Mastery Assessmentβ’10 minutes
- Automated Error Handling Implementation Knowledge Checkβ’5 minutes
1 ungraded labβ’Total 18 minutes
- Automated SQL Error Handling and Recovery Systemβ’18 minutes
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