Introduction to DataOps:

• What is DataOps?
• The need for DataOps in modern data-driven organizations.
• Key principles and benefits of DataOps.

Data Management and Governance:

• Data governance best practices.
• Data quality management.
• Data security and compliance considerations.

Data Pipeline Automation:

• Automating data extraction, transformation, and loading (ETL) processes.
• Workflow orchestration and scheduling.
• Tools and platforms for data pipeline automation.

Collaboration and Communication:

• Cross-functional collaboration between data engineers, data scientists, and IT operations.
• Communication tools and practices.
• Agile methodologies and their application in DataOps.

Version Control and Code Management:

• Git and version control for data-related code.
• Managing data pipelines as code.
• Collaborative code development and reviews.