Streamlining Data and Analytics Workflows
Why This Training?
In the rapidly evolving data landscape, traditional data management practices often fall short in meeting the demands of real-time data processing, collaboration, and analytics. DataOps emerges as a game-changing methodology that integrates data engineering, data integration, and data quality with a focus on collaboration and automation. Our training provides a blend of theoretical knowledge and hands-on practices to ensure participants are equipped with actionable skills.
Duration: 9 Hours (online / virtual live session)

Who Should Attend?

 A comprehensive understanding of data's vast landscape.
 IT Managers and Leaders aiming to implement efficient data practices.
 Data Analysts wanting to understand and participate in DataOps processes.
 Developers interested in integrating data pipelines into CI/CD.
 Any professional interested in modern data management practices.

Course Highlights

 Foundations of DataOps: Dive deep into the origins, principles, and significance of DataOps in the modern data landscape.
 Practical DataOps Implementation: From building data pipelines to integrating tools, get hands-on with DataOps practices.
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 EAdvanced DataOps Topics: Explore data security, compliance, and scalability within the DataOps framework.
 Best Practices and Pitfalls: Equip yourself with strategies for successful adoption and understand common mistakes to avoid.
 Cloud and DataOps: Understand how cloud environments augment the DataOps process and the considerations for hybrid DataOps.


 Basic understanding of data management and data pipelines.
 Familiarity with any programming or scripting language (Python is a plus but not mandatory).
 Knowledge of database systems and data storage solutions.

Training Materials Needed by Participants:

Laptop or computer with a stable internet connection.
Access to a cloud platform (like AWS, Azure, or GCP) or local setup for hands-on sessions.
Installation of specific DataOps tools (a list will be provided before the training).
Access to the training platform for resources, slides, and guides.
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Training Content


1. Introduction to DataOps & Setting the Foundations

Objective: Introduce the DataOps methodology, its significance, and its foundational principles.
1.1. Understanding the Data Landscape
  • Data Challenges in Modern Enterprises
  • The Need for Agile Data Processes
1.2. What is DataOps?
  • Definition and Origins
  • DataOps vs. DevOps: Similarities and Differences
1.3. Key Principles of DataOps
  • Collaboration & Communication
  • Automation & Integration
  • Continuous Delivery & Monitoring

1.4. Components of a DataOps Platform
  • Data Version Control
  • Data Testing
  • Data Orchestration & Deployment
1.5. Hands-on Activity:
  • Setting up a basic DataOps environment using open-source tools.

2. Implementing DataOps in Practice

Objective: Delve deeper into the practical aspects of DataOps, focusing on tools, pipelines, and workflow creation.
2.1. Building Data Pipelines in a DataOps Framework
  • Data Ingestion & ETL Processes
  • Data Validation & Testing
2.2. DataOps Toolchain
  • Overview of Popular DataOps Tools (e.g., Airflow, dbt, Data Version Control)
  • Integration with Data Platforms & Lakes
2.3. Collaboration and Monitoring
  • Data Monitoring & Alerting
  • Role of Data Cataloging and Lineage
2.4. Automation and CI/CD in DataOps
  • Building Continuous Integration Pipelines for Data
  • Deploying Data Models & Reports
2.5. Hands-on Activity
  • Building and deploying a sample data pipeline using DataOps principles.

3. Advanced Topics and Best Practices

Objective: Address advanced topics in DataOps and provide guidelines for successful adoption and scaling.
3.1. Data Security & Compliance in DataOps
  • Ensuring Data Privacy
  • Role of Data Masking & Tokenization
3.2. Scalability & Performance Optimization
  • Handling Large Data Volumes
  • Performance Monitoring & Optimization Strategies
3.3. Best Practices & Common Pitfalls
  • Successful DataOps Adoption Strategies
  • Common Mistakes and How to Avoid Them
3.4. DataOps in Cloud Environments
  • Leveraging Cloud Services for DataOps
  • Hybrid DataOps Considerations
3.5. Hands-on Activity:
  • Integrating DataOps processes with cloud storage solutions and exploring scalability.

3.6. Wrap-Up and Future Trends in DataOps
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