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Curriculum Designed by Experts
Multisoft Virtual Academy’s Databricks Data Quality & QA Engineering Corporate Training is designed to help organizations build robust, reliable, and scalable data pipelines. This comprehensive program covers core concepts such as data quality frameworks, automated testing strategies, data validation techniques, and QA best practices within the Databricks ecosystem. Participants gain hands-on experience with real-world use cases, enabling them to detect data issues early and ensure consistent data accuracy. Led by certified experts, the training is ideal for data engineers, QA professionals, and analytics teams aiming to strengthen data governance, improve pipeline reliability, and deliver high-confidence insights that support smarter business outcomes.
Databricks Data Quality & QA Engineering training is a specialized program that focuses on ensuring accuracy, consistency, and reliability of data within Databricks Lakehouse environments. It teaches professionals how to design data validation rules, test data pipelines, monitor data quality, and automate QA processes across batch and streaming workloads. The training helps organizations reduce data errors, improve analytics trust, and support reliable decision-making.
- Overview of data quality challenges in modern data platforms
- Role of QA engineering in data pipelines
- Key data quality dimensions: accuracy, completeness, consistency, timeliness
- Introduction to Databricks Lakehouse architecture
- Databricks workspace overview
- Lakehouse architecture concepts
- Understanding Delta Lake fundamentals
- Data ingestion patterns in Databricks
- Data profiling techniques
- Identifying anomalies, nulls, and outliers
- Data completeness and uniqueness checks
- Establishing baseline data quality metrics
- Designing data validation rules
- Column-level and row-level quality checks
- Handling invalid and bad records
- Implementing reusable validation frameworks
- Delta Lake constraints and expectations
- Schema enforcement and evolution
- Handling late-arriving and corrupted data
- Auditability and version control using Delta Lake
- QA strategies for batch pipelines
- Testing streaming pipelines
- Validating transformations and aggregations
- Regression testing for data pipelines
- Monitoring pipeline health and data freshness
- Detecting data drift and schema changes
- Setting up alerts and notifications
- Logging and error tracking best practices
- Automating data quality checks
- Integrating QA into CI/CD pipelines
- Version control and deployment strategies
- Environment management (Dev, Test, Prod)
- Optimizing quality checks for large datasets
- Managing performance overhead
- Scaling QA frameworks in enterprise environments
- Cost considerations and best practices
- End-to-end data quality implementation scenarios
- Common production issues and resolutions
- Industry best practices for data QA
- Designing a robust data quality framework
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Databricks Data Quality & QA Engineering Corporate Training Certification
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