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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

Included with

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3 modules

Gain insight into a topic and learn the fundamentals.

Advanced level

Recommended experience

3 hours to complete

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

Advanced level

Recommended experience

3 hours to complete

Flexible schedule

Learn at your own pace

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

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Build your subject-matter expertise

This course is part of the SQL at Scale: Querying, Transforming, and Governing Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

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: • Apply calculations to measure key data quality dimensions • Evaluate quality key performance indicators over time and recommend remediation • 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.

Module details

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 videosTotal 16 minutes

Core Data Quality Dimensions and Mathematical Foundations7 minutes
Advanced Quality Calculation Techniques for Enterprise Systems5 minutes
Building Quality Calculation Dashboards in SQL4 minutes

1 readingTotal 10 minutes

Implementing Quality Calculations in SQL and Python10 minutes

2 assignmentsTotal 20 minutes

Implementing Quality Metrics for E-commerce Product Database15 minutes
Data Quality Dimension Calculations Knowledge Check5 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 videosTotal 14 minutes

How Quality Monitoring Prevents Business Disasters3 minutes
Building Quality KPI Monitoring Systems with Trend Analysis6 minutes
Creating Quality KPI Dashboards with Trend Analysis5 minutes

2 readingsTotal 20 minutes

Quality KPI Frameworks for Enterprise Data Systems10 minutes
Advanced Remediation Strategies for Data Quality Issues10 minutes

2 assignmentsTotal 21 minutes

Quality KPI Analysis and Remediation Planning for Financial Services18 minutes
Remediation Strategy Planning Review3 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.