365onDemand Data Quality for Developers
365onDemand Data Quality for Developers Course Online by Checkmate IT Tech offer a transformative journey, elevating your expertise and mastering essential skills. Join our 365onDemand Data Quality for Developers Training to master the art of building robust, scalable, and data-driven applications.
- 10+ Courses
- 30+ Projects
- 400 Hours
365onDemand Data Quality for Developers Training is suitable for the following target audiences:
Software Developers: Software developers that want to ensure correct and dependable data management by integrating data quality solutions into their applications.
Data Engineers: Data engineers are experts in creating and managing data pipelines that want to improve their ability to apply strong data quality procedures.
Database administrators (DBAs): DBAs work to maintain data integrity and quality while managing and optimizing database performance.
ETL Developers: ETL developers work on Extract, Transform, Load (ETL) procedures and wish to incorporate data quality checks into their workflows.
Quality Assurance Engineers: QA specialists who work to guarantee data consistency and accuracy in software and apps are known as quality assurance engineers.
IT consultants: Consultants assigned to help companies across various sectors adopt data-quality solutions.
Data Quality Engineer: Ensuring data accuracy and dependability through implementing and upkeep of data quality solutions.
ETL Developer: Creating ETL workflows with integrated data quality procedures for efficient data integration and transformation.
Data Analyst: Data analysts help business choices by analyzing and preserving data integrity across platforms.
Software Developer (Data Integration Focus): Creating programs that facilitate precise data processing by integrating data quality functionalities.
Data Engineer: Creating pipelines for data that include data cleansing and validation to guarantee high-quality data flow.
Database Administrator: Database administration is the management of databases with an emphasis on preserving and improving the consistency and quality of the data.
Given the increasing importance of data quality in decision-making, sectors such as technology, healthcare, retail, and finance in the USA and Canada are actively seeking data quality specialists with competitive pay and room for advancement.
- What is data quality?
- Importance of data quality in development and analytics
- Data quality dimensions: accuracy, completeness, consistency, timeliness, uniqueness
- Common causes of poor data quality
- Role of developers in ensuring data quality
- Introduction to data profiling
- Profiling tools and techniques (e.g., SQL profiling, Pandas profiling)
- Identifying anomalies and inconsistencies
- Assessing data quality using statistical summaries
- Hands-on with open-source tools (e.g., OpenRefine, Great Expectations basics)
- Standardization, deduplication, normalization
- Handling missing values and outliers
- Data type corrections and formatting
- Using Python/Pandas or SQL for data cleaning
- Building reusable data cleaning scripts
- Writing automated data validation tests
- Unit tests for data pipelines
- Using Great Expectations for rule-based checks
- Introduction to data validation frameworks
- Integrating checks into ETL/ELT workflows
- Ensuring quality at each stage of data pipelines
- Data lineage and transformation impact
- Using dbt, Apache Airflow, or Azure Data Factory for quality enforcement
- Error handling and alerts in pipelines
- Best practices for scalable data workflows
- Setting up data quality dashboards
- Monitoring tools: Monte Carlo, Soda, or open-source alternatives
- Real-time vs. batch monitoring
- Logging and alerting mechanisms
- KPIs for tracking quality over time
- Role of metadata in data quality
- Data catalogs and documentation (e.g., DataHub, Amundsen)
- Data governance policies for developers
- Standards and frameworks (e.g., ISO 8000, DAMA-DMBOK)
- Collaboration between developers, analysts, and data stewards
- Capstone project: Implement end-to-end data quality on a sample dataset
- Mock assessments and quizzes
- Certification exam preparation
Note: Curriculum can be changed according to the latest industry trends.
365 onDemand Data Quality is a cloud-based solution used to assess, cleanse, validate, and enrich data, mainly for Microsoft Dynamics 365 environments.
This training is ideal for developers, technical consultants, and data engineers working with Dynamics 365 who want to improve data accuracy and consistency.
Basic knowledge of Dynamics 365 and relational databases is recommended, but the training usually covers required concepts from a developer’s perspective.
It helps identify duplicates, missing values, invalid formats, inconsistent records, and data standardization problems.
Developers work with configurations, rules, APIs, and integration points to automate data validation and cleansing processes.
Yes. It supports both real-time validation during data entry and scheduled batch processing for existing records.
Yes. The solution can integrate with external systems and data sources using APIs and connectors.
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
Some scripting or configuration-based development is involved, especially for advanced rules and integrations, but heavy coding is usually not required.
By ensuring clean and consistent data, it reduces errors, improves system reliability, and produces more accurate reports and analytics.
- It strengthens a developer’s profile in Dynamics 365 projects, especially roles focused on data governance, integration, and quality management.
We currently offer online sessions with flexible weekday/weekend batches. All sessions are recorded. You’ll have access to the recordings, along with support from instructors and peers in our learning portal.
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Job opportunities in USA and Canada
Data Quality Engineer: Ensuring data accuracy and dependability through implementing and upkeep of data quality solutions.
ETL Developer: Creating ETL workflows with integrated data quality procedures for efficient data integration and transformation.
Data Analyst: Data analysts help business choices by analyzing and preserving data integrity across platforms.
Software Developer (Data Integration Focus): Creating programs that facilitate precise data processing by integrating data quality functionalities.
Data Engineer: Creating pipelines for data that include data cleansing and validation to guarantee high-quality data flow.
Database Administrator: Database administration is the management of databases with an emphasis on preserving and improving the consistency and quality of the data.
Given the increasing importance of data quality in decision-making, sectors such as technology, healthcare, retail, and finance in the USA and Canada are actively seeking data quality specialists with competitive pay and room for advancement.
