15 Topics
10 Days
75 Sessions
Programme Overview
Training Description
Who Should Attend
This course is ideal for;
- Data Engineers
- Scrum Masters
- Product Owners
- Data Architects
- Data Scientists
- Project Managers
- Anyone needing agile data engineering skills
Session Objectives
- Understand the fundamentals of data engineering and agile methodologies.
- Master agile project planning and sprint management for data projects.
- Utilize iterative development and continuous integration in data pipelines.
- Implement collaborative practices and communication strategies for data teams.
- Design and build agile data architectures and infrastructure.
- Optimize data engineering workflows for agile delivery.
- Troubleshoot and address common challenges in agile data implementations.
- Implement data governance and compliance in agile data environments.
- Integrate agile practices with data engineering tools and platforms.
- Understand how to manage large-scale agile data projects.
- Explore advanced agile patterns for data engineering (e.g., DevOps integration, data mesh).
- Apply real world use cases for agile methodologies in data engineering.
- Leverage agile tools and frameworks for efficient data development.
About the Course
Accelerate time-to-value for enterprise analytics with our Data Engineering and Agile Methodologies Training Course. Tailored for data engineers, Scrum Masters, and Data Product Owners, this hands-on program equips technical teams to apply iterative Agile methodologies specifically to complex data platform development. Guided by Agile DataOps coaches, you will master data backlog prioritization, adaptive sprint planning, incremental pipeline deployment, and automated continuous integration—enabling your team to eliminate project bottlenecks, respond dynamically to evolving business needs, and deliver dependable data products on tight delivery cycles.
Curriculum & Topics
15 Topics | 75 Sessions
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Workshop 1.1:
Fundamentals of data engineering and agile methodologies.
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Workshop 1.2:
Overview of agile principles and frameworks for data projects.
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Workshop 1.3:
Setting up an agile data engineering environment.
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Workshop 1.4:
Introduction to agile tools and practices.
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Workshop 1.5:
Best practices for agile data engineering.
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Workshop 2.1:
Mastering agile project planning and sprint management for data projects.
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Workshop 2.2:
Utilizing user stories and backlog management.
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Workshop 2.3:
Implementing sprint planning and review sessions.
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Workshop 2.4:
Designing and building iterative development plans.
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Workshop 2.5:
Best practices for agile planning.
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Workshop 3.1:
Utilizing iterative development and continuous integration in data pipelines.
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Workshop 3.2:
Implementing CI/CD pipelines for data engineering.
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Workshop 3.3:
Designing and building automated testing frameworks.
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Workshop 3.4:
Optimizing development for rapid feedback.
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Workshop 3.5:
Best practices for iterative development.
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Workshop 4.1:
Implementing collaborative practices and communication strategies for data teams.
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Workshop 4.2:
Utilizing daily stand-ups and retrospectives.
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Workshop 4.3:
Designing and building effective communication channels.
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Workshop 4.4:
Optimizing collaboration for team efficiency.
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Workshop 4.5:
Best practices for collaboration.
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Workshop 5.1:
Designing and building agile data architectures and infrastructure.
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Workshop 5.2:
Utilizing modular and scalable data architectures.
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Workshop 5.3:
Implementing infrastructure as code (IaC).
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Workshop 5.4:
Optimizing architectures for agile development.
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Workshop 5.5:
Best practices for agile architecture.
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Workshop 6.1:
Optimizing data engineering workflows for agile delivery.
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Workshop 6.2:
Utilizing automation and workflow orchestration tools.
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Workshop 6.3:
Implementing agile data modeling and design.
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Workshop 6.4:
Designing efficient data processing workflows.
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Workshop 6.5:
Best practices for agile workflows.
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Workshop 7.1:
Troubleshooting and addressing common challenges in agile data implementations.
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Workshop 7.2:
Analyzing sprint backlogs and velocity charts.
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Workshop 7.3:
Utilizing problem-solving techniques for resolution.
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Workshop 7.4:
Resolving common agile data errors.
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Workshop 7.5:
Best practices for troubleshooting.
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Workshop 8.1:
Implementing data governance and compliance in agile data environments.
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Workshop 8.2:
Utilizing data security and access control.
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Workshop 8.3:
Designing and building compliance frameworks.
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Workshop 8.4:
Optimizing governance for agile projects.
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Workshop 8.5:
Best practices for governance.
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Workshop 9.1:
Integrating agile practices with data engineering tools and platforms.
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Workshop 9.2:
Utilizing agile project management tools.
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Workshop 9.3:
Implementing agile testing and deployment tools.
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Workshop 9.4:
Designing efficient tool integrations.
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Workshop 9.5:
Best practices for tool integration.
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Workshop 10.1:
Understanding how to manage large-scale agile data projects.
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Workshop 10.2:
Utilizing scaled agile frameworks (SAFe, LeSS).
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Workshop 10.3:
Implementing program and portfolio management.
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Workshop 10.4:
Designing scalable agile solutions.
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Workshop 10.5:
Best practices for large scale agile.
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Workshop 11.1:
Exploring advanced agile patterns for data engineering (DevOps integration, data mesh).
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Workshop 11.2:
Utilizing DevOps practices for data pipelines.
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Workshop 11.3:
Implementing agile data mesh architectures.
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Workshop 11.4:
Designing and building advanced agile frameworks.
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Workshop 11.5:
Optimizing advanced patterns for specific applications.
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Workshop 11.6:
Best practices for advanced agile.
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Workshop 12.1:
Implementing agile methodologies for data warehouse development.
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Workshop 12.2:
Utilizing agile practices for machine learning deployment.
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Workshop 12.3:
Implementing agile methodologies for data lake implementation.
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Workshop 12.4:
Utilizing agile practices for data governance initiatives.
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Workshop 12.5:
Best practices for real-world applications.
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Workshop 13.1:
Utilizing agile tools and frameworks (Jira, Confluence, GitLab).
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Workshop 13.2:
Implementing agile data projects with specific tools.
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Workshop 13.3:
Designing and building automated agile workflows.
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Workshop 13.4:
Optimizing tool usage for efficient delivery.
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Workshop 13.5:
Best practices for tool implementation.
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Workshop 14.1:
Implementing agile project monitoring and metrics.
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Workshop 14.2:
Utilizing sprint velocity and burndown charts.
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Workshop 14.3:
Designing and building agile dashboards.
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Workshop 14.4:
Optimizing monitoring for real-time insights.
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Workshop 14.5:
Best practices for monitoring.
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Workshop 15.1:
Emerging trends in agile methodologies for data engineering.
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Workshop 15.2:
Utilizing AI for agile project automation.
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Workshop 15.3:
Implementing agile practices in cloud-native environments.
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Workshop 15.4:
Best practices for future applications.
