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To establish a robust and impactful delivery plan for my first year as a certified DLI Instructor under the DLI Ambassador Program, particularly focusing on Accelerating Data Engineering Pipelines, I propose a structured and comprehensive approach that ensures consistent engagement, effective knowledge transfer, and measurable outcomes. Below is the detailed delivery plan:

1. Workshop Frequency and Scheduling
    - Quarterly Workshops: Conduct one major workshop per quarter, resulting in four main workshops over the year. This frequency balances consistency with quality, allowing ample time for preparation, customization, and follow-up.
    - Monthly Mini-Sessions: In addition to the quarterly workshops, I will host monthly mini-sessions or webinars. These will be shorter, more focused sessions (1-2 hours) aimed at reinforcing concepts, addressing common challenges, and providing a platform for continuous learning.

2. Target Audience and Group Composition
Primary Audience:
    - Data Engineers: Professionals seeking to enhance their skills in building and optimizing data pipelines.
    - Data Scientists and Analysts: Individuals looking to improve their understanding of data engineering to better manage and process large datasets.
    - Software Developers: Developers transitioning into data engineering roles or integrating data pipelines into their applications.

Secondary Audience:
    - Students: Graduate students and final-year undergraduates in computer science, data science, and related fields who are preparing to enter the workforce.
    - IT Professionals: Existing IT staff aiming to pivot their careers towards data engineering.

Group Size:
    - Quarterly Workshops: Each major workshop will be designed to accommodate 30-50 participants. This size ensures personalized attention and effective hands-on learning while maintaining an interactive environment.
    - Monthly Mini-Sessions: These sessions will be open to a larger audience (up to 100 participants) to foster a broader community engagement and allow for scalable knowledge sharing.