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  1. Courses
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  3. GenAI for Data Engineers

GenAI for Data Engineers

This course is a primer where learners will discover the key capabilities of GenAI and uncover practical strategies to leverage these powerful tools in their day-to-day data engineering work.

Christopher Klaus
Christopher Klaus
Data Science | core | 1 hour 10 minutes |   Published: Jul 2024
In partnership with:  Coursera

    Discussions

Overview

1KSTUDENTS*
96%RECOMMEND*

This course includes:

  • 1 hour of on-demand video  
  • Certificate of completion  
  • Direct access/chat with the instructor 
  • 100% self-paced online 

As part of the GenAI Academy, "GenAI for Data Engineers" is an exploration of how Generative Artificial Intelligence (GenAI) is transforming the field of data engineering. This course is a primer where learners will discover the key capabilities of GenAI and uncover practical strategies to leverage these powerful tools in their day-to-day data engineering work.  

Through a combination of discussions, video demos, and guided hands-on activities, learners will gain an understanding of how GenAI can enhance productivity for synthetic data generation, data modeling and schema design, data pipeline creation / code generation for data loading and cleansing, and documentation. 

Learners will also consider the ethical concerns surrounding the use of GenAI in data engineering, examining potential risks and challenges, as well as best practices for responsible implementation. By the end of this course, data engineers will be equipped with the knowledge and skills to start scaling their productivity by harnessing the transformative potential of GenAI. 

Whether you're an experienced data engineer looking to stay ahead of the curve or an aspiring professional seeking to future-proof your skillset, this course provides a foundation that will empower you to unlock new levels of efficiency, productivity, and creativity in your work.

Skills You Will Gain

Code Generation
Data cleansing
Data pipeline creation
Ethical Concerns
Productivity enhancement

Learning Outcomes (At the end of this program you will be able to)

  • Identify the capabilities of GenAI for basic role specific, Data Engineer functions.  
  • Evaluate the results of GenAI for a real-world application in Data Engineering functions. 
  • Compare and contrast the risks, challenges, and ethical considerations of GenAI in the workplace for Data Engineer functions in general.  

Prerequisites

Participants should understand data pipelines, ETL/ELT processes, data transformation, and common storage technologies. Experience with Python and SQL is beneficial. Curiosity and openness to new technologies, especially GenAI tools, are essential. Prior data engineering experience helps contextualize the content.

 

 

 

Who Should Attend

This course is tailored to data engineering team leaders and data engineers, including: 

  • Data Engineering Managers and Team Leads responsible for identifying GenAI opportunities and guiding their data engineering teams towards more efficient, innovative, and forward-thinking practices. 
  • Data Engineers, who are looking to enhance their workflows and boost productivity by incorporating GenAI-powered tools and techniques into their daily tasks. 
  • Aspiring Data Engineers, who want to future-proof their skills and gain a competitive edge by mastering the integration of GenAI in data engineering. 

Curriculum

Instructors

Frequently Asked Questions

How much do the courses at Starweaver cost?

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Yes, we do offer a certification upon completion of our course to showcase your newly acquired skills and expertise.

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Are Starweaver's courses designed for beginners or advanced students?

Our course is designed with three levels to cater to your learning needs - Core, Intermediate, and Advanced. You can choose the level that best suits your knowledge and skillset to enhance your learning experience.

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Do you offer refunds?

Yes, we do offer a 100% refund guarantee for our courses within a specified time frame. If you are not satisfied with the course, contact our customer support team to request a refund with your order details. Some restrictions may apply.

*Where courses have been offered multiple times, the “# Students” includes all students who have enrolled. The “%Recommended” shown is also based on this data.
1Module 1: GenAI for Data Engineers
Christopher Klaus

Christopher Klaus

Navigating the intersection of data science, technology innovation, and leadership, Christopher has established a distinguished career spanning diverse sectors, including software, government, healthcare, education, and advertising. As a leader and innovator, Christopher is driven by a passion for solving complex challenges and leveraging technology to drive meaningful advancements. At the core of Christopher’s professional ethos is the commitment to unlocking potential—both in the technologies utilized and the teams led.

With expertise in data analytics, governance, engineering, and cloud technologies, Christopher has been at the forefront of deploying advanced solutions that foster strategic growth and operational excellence. Proficient in AWS, GCP, and Azure, and deeply engaged in AI and machine learning applications, Christopher consistently pioneers transformative projects that push the boundaries of technological innovation. Beyond technical achievements, Christopher cultivates environments where teams can thrive, fostering collaboration and continuous improvement.

Education and mentorship play a fundamental role in Christopher’s leadership philosophy. Through knowledge sharing, the development of educational programs, and contributions to scholarly publications, Christopher actively nurtures a culture of learning and innovation. Recognized through various honors and awards, Christopher’s contributions extend beyond personal milestones, shaping communities and setting new benchmarks in the tech industry. Each day presents an opportunity to inspire change, drive progress, and pave the way for future generations of innovators.

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Segment 01 - Introduction to GenAI for Data Engineers

Segment 02 - GenAI and Data Engineering: Glossary

Segment 03 - History & Background for GenAI and Data Engineering

Segment 04 - Demo: Create Synthetic Data for Testing a Data Pipeline with ChatGPT

Segment 05 - Demo: Create Synthetic Data for Testing a Data Pipeline with Google Gemini

Segment 06 - Demo: Create and Implement an Entity Relationship Diagram with CoPilot

Segment 07 - Remediating Risks and Ethical Concerns

Segment 08 - Demo: Data Pipeline Segment 08 - Development from Low-Code, to SQL, to Python with ChatGPT

Segment 09 - Demo: Data Pipeline Development with Data Loading and Code Documentation using ChatGPT

Segment 10 - Closing Thoughts: What’s Next