Description

Introduction:

This course is designed for data managers, compliance officers, and IT professionals who need to implement and manage this within Databricks environments. As organizations increasingly rely on big data platforms like Databricks, ensuring proper data governance and adherence to regulatory standards becomes crucial. Participants will learn how to establish and enforce data governance frameworks, ensure compliance with data protection regulations, and manage data quality and security within Databricks. The course includes practical examples and hands-on labs to help participants apply governance and compliance principles effectively.

Prerequisites:

• Basic understanding of Databricks and its components.
• Familiarity with data governance concepts and compliance requirements.
• Experience with data management and IT security practices.
• Knowledge of data privacy regulations (e.g., GDPR, CCPA) is beneficial but not required.

Table of Content:

1. Introduction

1.1 Overview of data governance and its importance
1.2 Key principles and objectives of data governance
1.3 Introduction to data compliance and regulatory requirements
1.4 Benefits of implementing governance and compliance in Databricks

2. Data Governance Frameworks in Databricks

2.1 Defining and implementing a data governance strategy
2.2 Establishing data governance roles and responsibilities
2.3 Developing data policies, standards, and procedures
2.4 Integrating governance frameworks with Databricks environments

3. Data Quality Management

3.1 Ensuring data quality and integrity in Databricks(Ref: Databricks and Delta Lake: Building Robust Data Lakes)
3.2 Implementing data quality frameworks and metrics
3.3 Monitoring and improving data quality through data profiling and cleansing
3.4 Handling data anomalies and inconsistencies

4. Data Security and Privacy

4.1 Implementing data security measures in Databricks
4.2 Managing access controls and data encryption
4.3 Ensuring data privacy and protection in compliance with regulations
4.4 Data masking and anonymization techniques

5. Compliance with Data Protection Regulations

5.1 Overview of key data protection regulations (e.g., GDPR, CCPA, HIPAA)
5.2 Implementing compliance measures and practices in Databricks
5.3 Managing data subject rights and consent
5.4 Conducting data protection impact assessments (DPIAs)

6. Data Lineage and Auditing

6.1 Understanding data lineage and its importance
6.2 Implementing data lineage tracking in Databricks
6.3 Setting up auditing and logging for data access and changes
6.4 Using audit logs for compliance and governance purposes

7. Data Classification and Metadata Management

7.1 Implementing data classification policies in Databricks
7.2 Managing metadata and data catalogs
7.3 Using metadata for data governance and compliance
7.4 Integrating data classification with data protection measures

8. Managing Data Retention and Disposal

8.1 Establishing data retention policies and schedules
8.2 Implementing data archiving and disposal practices
8.3 Ensuring compliance with data retention regulations
8.4 Handling data deletion and data lifecycle management

9. Monitoring and Reporting for Governance and Compliance

9.1 Setting up monitoring tools and dashboards for governance and compliance
9.2 Generating compliance reports and documentation
9.3 Conducting regular reviews and audits of governance practices
9.4 Addressing compliance issues and non-conformities

10. Case Studies and Real-World Applications

10.1 Case studies of successful data governance and compliance implementations in Databricks
10.2 Lessons learned and best practices from real-world scenarios
10.3 Innovative approaches to data governance and compliance
10.4 Future trends in data governance and regulatory compliance

11. Final Project: Implementing Governance and Compliance in Databricks

11.1 Designing and implementing a data governance and compliance framework in Databricks
11.2 Applying data quality, security, and privacy measures
11.3 Developing and presenting a compliance report
11.4 Reviewing project outcomes and insights

12. Conclusion and Next Steps

12.1 Recap of key concepts and techniques covered in the course
12.2 Additional resources for further learning and certification
12.3 Career advancement opportunities in data governance and compliance
12.4 Staying updated with Databricks and data governance developments

Reference