Data Quality: Data Quality Management for the Developer
Data Quality: Data Quality Management for the Developer Online by Checkmate IT Tech will be a transformative journey, elevating your expertise and mastering essential skills. This training is designed for developers who want to master the art of data quality management. Ready to dive in? Get started with Data Quality Management for the Developer today!
- 10+ Courses
- 30+ Projects
- 400 Hours
Data Quality Management for the Developer Training is suitable for the following target audiences:
Software developers: During the coding and development process, developers working on data-centric apps must apply strong data quality measures.
Data Engineers: Data engineers are experts who plan, construct, and manage data pipelines to guarantee data quality across systems.
Database Administrators (DBAs): Database administrators (DBAs) manage large datasets and are responsible for enforcing and validating data quality standards within databases.
ETL Developers: To guarantee data correctness and consistency throughout data migration and integration, developers working on Extract, Transform, and Load (ETL) operations are known as ETL developers.
Quality Assurance Testers: Quality assurance testers are QA specialists who specialize in data testing and validation to find problems impacting system functionality and data quality.
Software developers: During the coding and development process, developers working on data-centric apps must apply strong data quality measures.
Data Engineers: Data engineers are experts who plan, construct, and manage data pipelines to guarantee data quality across systems.
Database Administrators (DBAs): Database administrators (DBAs) manage large datasets and are responsible for enforcing and validating data quality standards within databases.
ETL Developers: To guarantee data correctness and consistency throughout data migration and integration, developers working on Extract, Transform, and Load (ETL) operations are known as ETL developers.
Quality Assurance Testers: Quality assurance testers are QA specialists who specialize in data testing and validation to find problems impacting system functionality and data quality.
- What is Data Quality
- Key dimensions of Data Quality (Accuracy, Completeness, Consistency etc.)
- Overview of data quality lifecycle
- Introduction to Data Quality tools (e.g., Informatica IDQ)
- Exploring data quality challenges in real-world scenarios
- Types of profiling: column, rule, join, and cross-domain profiling
- Profiling data to identify quality issues
- Using profiling results for root cause analysis
- Interpreting profiling statistics and patterns
- Creating and applying business rules
- Reusable rule development
- Data standardization using rules
- Handling conditional logic and exception records
- Address cleansing, formatting, and parsing
- Name and phone number standardization
- Lookup tables and reference data integration
- Locale-specific cleansing practices
- Concepts of fuzzy matching and identity resolution
- Creating match rules and match keys
- Survivorship rules and record consolidation
- Matching using IDQ Match Transformation
- Validating data against rules, lookup tables, and reference data
- Integration with third-party enrichment services (optional)
- Real-time vs batch validation scenarios
- Data Quality Scorecards introduction
- Introduction to Data Quality dashboards
- Building scorecards and setting thresholds
- Trend analysis and continuous improvement
- Reporting data quality metrics to stakeholders
- Capstone project: Build and present a data quality solution
Note: This curriculum will be modified as per latest industry standards.
The course focuses on teaching developers how to implement and manage data quality processes within data pipelines and applications.
It’s ideal for software developers, data engineers and technical professionals involved in handling, transforming, or validating data.
No prior experience is required but familiarity with SQL and basic scripting (like Python) is recommended.
Common tools like SQL, Python (pandas) and frameworks such as Great Expectations or Talend are introduced, depending on the version of the course.
The course includes both theoretical concepts and hands-on exercises with real-world data scenarios.
Topics include data profiling, cleansing, validation rules, monitoring, metadata management, and data quality in pipelines.
Yes, the final week includes a capstone project where you implement a complete data quality solution.
You can enroll via our website or contact our support team directly via email or phone. We’ll guide you through the quick and easy registration process.
https://checkmateittech.com/
Email info@checkmateittech.com OR Call Us +1-347-4082054
It equips developers with in-demand skills to ensure high-quality data, which is crucial in roles like data engineer, ETL developer, and analytics developer.
Yes, a certificate of completion is provided if all assessments and project work are successfully completed.
We currently offer online sessions with flexible weekday/weekend batches for 8 weeks. All sessions are recorded. You’ll have access to the recordings, along with support from instructors and peers in our learning portal.
- Submit Form
Job opportunities in USA and Canada
Software developers: During the coding and development process, developers working on data-centric apps must apply strong data quality measures.
Data Engineers: Data engineers are experts who plan, construct, and manage data pipelines to guarantee data quality across systems.
Database Administrators (DBAs): Database administrators (DBAs) manage large datasets and are responsible for enforcing and validating data quality standards within databases.
ETL Developers: To guarantee data correctness and consistency throughout data migration and integration, developers working on Extract, Transform, and Load (ETL) operations are known as ETL developers.
Quality Assurance Testers: Quality assurance testers are QA specialists who specialize in data testing and validation to find problems impacting system functionality and data quality.
