15 Topics
10 Days
Programme Overview
Training Description
Who Should Attend
This course is ideal for;
- Data Engineers
- Compliance Officers
- Data Architects
- Data Scientists
- Security Analysts
- Legal Professionals
- Anyone needing data compliance skills
Session Objectives
- Understand the fundamentals of data engineering and data compliance.
- Master data privacy regulations (GDPR, CCPA, HIPAA) and their impact on data systems.
- Utilize data governance frameworks for compliance management.
- Implement data audit logging and monitoring for regulatory requirements.
- Design and build compliant data pipelines and storage solutions.
- Optimize data systems for data security and privacy.
- Troubleshoot and address common compliance challenges in data engineering.
- Implement data retention and deletion policies for compliance.
- Integrate compliance tools and practices with data engineering platforms.
- Understand how to manage large-scale data compliance implementations.
- Explore advanced compliance patterns for data engineering (e.g., data anonymization, pseudonymization).
- Apply real world use cases for data compliance in data engineering.
- Leverage compliance tools and frameworks for efficient implementation.
About the Course
Architect privacy-first, legally sound data platform infrastructure with our Data Engineering and Data Compliance Training Course. Tailored for data engineers, compliance officers, and platform architects, this practical program equips technical teams to integrate regulatory controls directly into data ingestion and processing pipelines. Guided by data protection and platform engineering experts, you will master privacy-by-design principles, automated audit logging, data anonymization/pseudonymization techniques, and compliance framework integration (such as GDPR, CCPA, and HIPAA)—enabling your enterprise to eliminate regulatory risk, avoid severe penalties, and maintain audit-ready data ecosystems at scale.
Curriculum & Topics
15 Topics | 10 Days
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Subtopic 1.1:
Fundamentals of data engineering and data compliance.
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Subtopic 1.2:
Overview of key data privacy regulations and compliance requirements.
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Subtopic 1.3:
Setting up a compliance-focused data engineering environment.
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Subtopic 1.4:
Introduction to compliance tools and frameworks.
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Subtopic 1.5:
Best practices for data compliance.
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Subtopic 2.1:
Mastering data privacy regulations (GDPR, CCPA, HIPAA) and their impact on data systems.
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Subtopic 2.2:
Utilizing data protection principles and requirements.
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Subtopic 2.3:
Implementing data subject rights and access controls.
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Subtopic 2.4:
Designing and building privacy-preserving data systems.
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Subtopic 2.5:
Best practices for data privacy.
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Subtopic 3.1:
Utilizing data governance frameworks for compliance management.
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Subtopic 3.2:
Implementing data governance policies and procedures.
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Subtopic 3.3:
Designing and building data catalogs and metadata management systems.
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Subtopic 3.4:
Optimizing data governance for compliance.
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Subtopic 3.5:
Best practices for governance.
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Subtopic 4.1:
Implementing data audit logging and monitoring for regulatory requirements.
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Subtopic 4.2:
Utilizing audit logging and data lineage tracking.
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Subtopic 4.3:
Designing and building security information and event management (SIEM) systems.
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Subtopic 4.4:
Optimizing monitoring for compliance.
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Subtopic 4.5:
Best practices for auditing.
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Subtopic 5.1:
Designing and building compliant data pipelines and storage solutions.
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Subtopic 5.2:
Utilizing data encryption and masking techniques.
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Subtopic 5.3:
Implementing secure data transfer protocols.
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Subtopic 5.4:
Designing compliant data storage architectures.
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Subtopic 5.5:
Best practices for compliant systems.
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Subtopic 6.1:
Optimizing data systems for data security and privacy.
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Subtopic 6.2:
Utilizing data access control and authentication mechanisms.
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Subtopic 6.3:
Implementing data loss prevention (DLP) strategies.
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Subtopic 6.4:
Designing secure and private data environments.
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Subtopic 6.5:
Best practices for security.
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Subtopic 7.1:
Troubleshooting and addressing common compliance challenges in data engineering.
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Subtopic 7.2:
Analyzing compliance logs and audit reports.
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Subtopic 7.3:
Utilizing problem-solving techniques for resolution.
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Subtopic 7.4:
Resolving common compliance errors.
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Subtopic 7.5:
Best practices for troubleshooting.
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Subtopic 8.1:
Implementing data retention and deletion policies for compliance.
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Subtopic 8.2:
Utilizing data lifecycle management tools.
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Subtopic 8.3:
Designing and building data deletion workflows.
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Subtopic 8.4:
Optimizing retention for regulatory requirements.
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Subtopic 8.5:
Best practices for data retention.
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Subtopic 9.1:
Integrating compliance tools and practices with data engineering platforms.
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Subtopic 9.2:
Utilizing compliance management systems.
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Subtopic 9.3:
Implementing data discovery and classification tools.
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Subtopic 9.4:
Designing efficient tool integrations.
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Subtopic 9.5:
Best practices for tool integration.
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Subtopic 10.1:
Understanding how to manage large-scale data compliance implementations.
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Subtopic 10.2:
Utilizing compliance automation and orchestration.
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Subtopic 10.3:
Implementing compliance monitoring and reporting.
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Subtopic 10.4:
Designing scalable compliance solutions.
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Subtopic 10.5:
Best practices for large scale compliance.
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Subtopic 11.1:
Exploring advanced compliance patterns for data engineering (data anonymization, pseudonymization).
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Subtopic 11.2:
Utilizing data anonymization techniques.
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Subtopic 11.3:
Implementing data pseudonymization for privacy preservation.
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Subtopic 11.4:
Designing and building advanced compliance frameworks.
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Subtopic 11.5:
Optimizing advanced patterns for specific applications.
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Subtopic 11.6:
Best practices for advanced compliance.
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Subtopic 12.1:
Implementing data compliance for financial data systems.
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Subtopic 12.2:
Utilizing data compliance for healthcare data platforms.
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Subtopic 12.3:
Implementing data compliance for e-commerce data pipelines.
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Subtopic 12.4:
Utilizing data compliance for government data management.
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Subtopic 12.5:
Best practices for real-world applications.
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Subtopic 13.1:
Utilizing compliance tools and frameworks (OneTrust, Collibra).
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Subtopic 13.2:
Implementing compliance policies with specific tools.
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Subtopic 13.3:
Designing and building automated compliance workflows.
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Subtopic 13.4:
Optimizing tool usage for efficient compliance.
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Subtopic 13.5:
Best practices for tool implementation.
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Subtopic 14.1:
Implementing compliance monitoring and metrics.
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Subtopic 14.2:
Utilizing data compliance dashboards and reports.
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Subtopic 14.3:
Designing and building compliance monitoring systems.
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Subtopic 14.4:
Optimizing monitoring for real-time insights.
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Subtopic 14.5:
Best practices for monitoring.
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Subtopic 15.1:
Emerging trends in data engineering compliance.
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Subtopic 15.2:
Utilizing AI for compliance automation.
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Subtopic 15.3:
Implementing compliance in cloud-native data environments.
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Subtopic 15.4:
Best practices for future applications.
