Introduction of Advanced DataOps

Advanced DataOps extends the foundational principles of DataOps by incorporating critical elements of data governance and compliance into the data pipeline. As organizations deal with increasing amounts of sensitive data and face stricter regulatory requirements, ensuring the integrity, privacy, and security of data becomes a paramount concern. This course focuses on advanced practices for embedding governance and compliance measures directly into DataOps workflows. By combining automation, version control, and monitoring with comprehensive data governance protocols, DataOps can ensure that data processes remain efficient, secure, and compliant.

The course provides an in-depth exploration of how organizations can achieve data governance at scale while maintaining agility, ensuring that data meets regulatory standards, and building trust with stakeholders.

Prerequisites

Participants should have:

• A foundational understanding of DataOps principles and practices.
• Familiarity with data engineering and data science workflows.
• Basic knowledge of data governance concepts, including data privacy, security, and compliance frameworks.
• Experience with tools like Apache Airflow, Jenkins, or Git for automation and CI/CD.
• Familiarity with regulatory frameworks such as GDPR, HIPAA, and CCPA.
• Experience working with cloud-based data platforms and databases.

Table of Contents

1. Introduction to Advanced DataOps
   1.1 Overview of DataOps and Its Role in Data Governance
   1.2 The Need for Governance in Modern Data Pipelines
   1.3 Key Regulatory Challenges in Data Management
2. Data Governance in DataOps
   2.1 Integrating Data Governance with DataOps
   2.2 Ensuring Data Privacy and Security in Pipelines
   2.3 Best Practices for Metadata Management(Ref: DataOps for Data Science Teams: Bridging the Gap Between Development and Operations)
   2.4 Data Lineage: Tracking the Journey of Data
3. Automating Data Compliance
   3.1 Tools and Techniques for Automating Compliance Checks
   3.2 Continuous Compliance Monitoring in Data Pipelines
   3.3 Embedding Compliance into CI/CD for Data Operations
4. Data Quality and Auditing in DataOps
   4.1 Ensuring High-Quality Data Across the Pipeline
   4.2 Automated Data Validation and Cleansing Tools
   4.3 Data Auditing Practices and Tools for Compliance
   4.4 Auditable Logs for Data Operations and Model Changes
5. Version Control and Data Governance
   5.1 Managing Version Control for Data and Models
   5.2 Ensuring Data Integrity and Provenance with Git and DVC
   5.3 Implementing Data Provenance for Regulatory Requirements
6. Securing Data in DataOps Pipelines
   6.1 Best Practices for Data Encryption and Access Control
   6.2 Role-Based Access and Secure Authentication in Data Pipelines
   6.3 Protecting Sensitive Data: Techniques for Anonymization and Masking
7. Compliance Frameworks and DataOps
   7.1 Navigating Major Regulatory Standards (GDPR, HIPAA, CCPA)
   7.2 Applying DataOps to Achieve Compliance with Legal Requirements
   7.3 Auditing for Compliance in DataOps Pipelines
8. Continuous Monitoring and Reporting
   8.1 Real-Time Monitoring for Data Quality and Security
   8.2 Automated Reporting and Alerts for Compliance Violations
   8.3 Integrating Monitoring with DataOps Tools for Governance
9. Advanced DataOps Tools for Governance and Compliance
   9.1 Overview of Tools for Data Governance (Apache Atlas, Collibra, etc.)
   9.2 Integrating Compliance Tools into DataOps Pipelines
   9.3 Exploring Automation Platforms for Data Governance
10. Future Trends in DataOps and Governance
    10.1 Emerging Technologies in Data Governance Automation
    10.2 The Impact of AI and ML on Data Governance and Compliance
    10.3 Evolving Regulations and the Future of DataOps

Conclusion

Advanced DataOps practices are essential for ensuring that data governance and compliance are seamlessly integrated into modern data operations. By embedding governance processes like data quality checks, compliance automation, and audit trails into DataOps workflows, organizations can achieve better control over their data while adhering to regulatory requirements. This course has provided the foundational principles and tools for building a governance-centric DataOps pipeline, emphasizing automation and monitoring as key enablers for maintaining compliance. As data regulations become increasingly complex, embracing Advanced DataOps practices will help organizations scale their data operations while minimizing risk and ensuring the integrity of their data assets.

Reference