Level: Beginner to Intermediate
Partner/Cloud Analytics Practice Executive
Navigating the world of data engineering demands both an understanding of foundational principles and a grasp of evolving paradigms. This half-day course delves into cutting-edge practices to sculpt and refine robust data pipelines. Immerse yourself in the intricacies of data engineering for data meshes, data warehouses, data lakehouses, and data virtualization.
Participants will learn to discern the right design patterns, incorporate rigorous testing, deploy through DataOps methodologies, and evaluate alignment with target architectures. Through a blend of instructive lectures, interactive discussions, and pragmatic exercises, students will gain proficiency in the tools and techniques central to contemporary data engineering.
By the session's culmination, you'll be poised to design, implement, and nurture data pipelines that resonate with your organization's objectives.
Led by a seasoned industry expert, this course offers participants a blend of theoretical insights, practical real-world examples, and the latest best practices. By the end of the course, attendees will be well-prepared to design sophisticated data platforms that align with their organization's requirements.
You Will Learn
- Modern data engineering practices, including the latest trends and best practices in data engineering, giving your team a comprehensive understanding of contemporary practices
- How to select design patterns that fit your organization's needs and data requirements, ensuring your team can design and build effective data platforms
- How to integrate testing into the data engineering process, ensuring your team can develop and deliver high-quality data platforms
- DataOps principles, which focus on automating the deployment and management of data platforms, reducing the time and effort needed for deployment
- How to evaluate progress against the target data architecture, ensuring that your team can measure the success of their data engineering efforts and make informed decisions to improve their data platforms
- Directors of data and analytics
- Data architects
- Analytics architects
- Data engineers
- Analytics engineers
- Data analysts
- Data scientists