Mastering Generative AI for Data Engineering from edX

Generative AI skills are in demand, with Ascend.io reporting that 89% of respondents reporting using generative AI in some capacity. ZipRecruiter reports that data engineers with generative AI skills earn an average of USD 115,000 annually, with top-end salaries of USD 179,000 annually.

Data engineering is responsible for building strong data pipelines, managing data infrastructure, and ensuring high-quality data evaluation.

This course is suitable for existing and aspiring data engineers, data warehousing specialists, and other data professionals such as data analysts, data scientists, and BI analysts.

First, learn about the current impact of generative AI on data engineering.

Throughout the course, you will assume the role of a data engineer and gain experience using generative AI to enhance productivity by introducing innovative ways to deliver projects.

You will learn how to use and apply generative models for tasks such as architecture design, database querying, data warehouse schema design, data augmentation, data pipelines, ETL workflows, data analysis and mining, data lake house, and data repositories. You will also explore challenges and ethical considerations associated with using Generative AI.

To complete this course, you'll demonstrate your new generative AI skills in a real-world, shareable, hands-on data engineering project.

After you successfully complete your final quiz, you will receive your certificate, and you can request your Badge. You can share both your project and certificate with your current or prospective employers.

What's inside

Learning objectives

Use generative ai tools and techniques in data engineering processes across industries
Apply generative ai solutions for data generation, augmentation, and anonymization

Evaluate real-world case studies that feature successful application of generative ai for etl and data repositories
Build generative ai skills in hands-on labs and projects for data warehouse schema design and infrastructure setup

Use generative ai tools and techniques in data engineering processes across industries
Apply generative ai solutions for data generation, augmentation, and anonymization
Evaluate real-world case studies that feature successful application of generative ai for etl and data repositories
Build generative ai skills in hands-on labs and projects for data warehouse schema design and infrastructure setup

Syllabus

Module 1: Data Engineering and Generative AI

Welcome

Data Engineering and Generative AI

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Explores the impact of generative AI on data engineering, which is a rapidly evolving field with increasing demand for professionals with these skills

Develops skills in using generative models for tasks such as architecture design, database querying, and data warehouse schema design, which are highly relevant to data engineering

Includes hands-on labs and a guided project, allowing learners to apply generative AI skills in a real-world data engineering scenario, which is crucial for practical experience

Examines ethical considerations associated with using generative AI, which is an important aspect of responsible data engineering practices and helps learners develop a comprehensive understanding

Presented by IBM, a company recognized for its contributions to data management, data warehousing, and artificial intelligence, which lends credibility to the course content

Focuses on applying generative AI solutions for data generation, augmentation, and anonymization, which are specialized skills that can enhance data engineering workflows and improve data quality

Reviews summary

Generative ai for data engineering overview

According to learners, this course provides a solid introduction to applying Generative AI concepts within the data engineering domain. It covers how AI can enhance tasks like ETL workflows, database querying, and schema design. Many appreciate the focus on practical applications and the inclusion of a hands-on project and labs to reinforce learning. While students found the course valuable for understanding the immediate impact of AI on their field and highlighting ethical considerations, some more experienced data engineers noted that the depth might be more suitable for those newer to the intersection of AI and DE, wishing for more advanced coverage on underlying models or complex optimization techniques. Overall, it's seen as a useful starting point.

Addresses important ethical challenges.

"The section on ethical considerations in using Generative AI for data engineering was very relevant and well-covered."

"It's important to think about the ethical implications, and I appreciate the course dedicating time to this."

"Discussing ethical challenges adds a crucial layer of responsibility to using these powerful tools."

Provides a strong foundational overview.

"This was a great introduction to the topic of Generative AI specifically for data engineers. It clearly laid out the landscape."

"As someone new to Generative AI but familiar with data engineering, the course did a good job of bridging the gap."

"It sets the stage well for understanding the potential and current impact of AI in our field."

Includes helpful labs and a culminating project.

"The labs were useful for getting hands-on experience with the concepts taught."

"The guided project was a highlight; applying everything in a realistic scenario solidified my understanding."

"I learned best by doing, and the hands-on components of this course were very effective for me."

Applies AI to core DE tasks like ETL and querying.

"I really appreciated how the course showed concrete examples of using Gen AI for everyday data engineering tasks, like generating SQL queries or helping with ETL scripts."

"The module on using AI for data warehouse schema design was particularly helpful; it felt very relevant to my job."

"This course focuses on practical use cases, not just theory, which is exactly what I needed to see how to apply AI in my work."

May be too basic for experienced learners.

"While the overview is good, I was hoping for more in-depth coverage of the actual AI models or more complex integration patterns."

"As an experienced data engineer, I found some sections covered material I was already familiar with; it felt more like a high-level survey."

"Could use more advanced topics or optimization techniques for enterprise-level applications."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Mastering Generative AI for Data Engineering with these activities:

Review Data Engineering Fundamentals

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Reinforce your understanding of core data engineering concepts to better grasp how generative AI can augment these processes.

Browse courses on Data Engineering

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Review key concepts in data modeling and database design.
Familiarize yourself with common ETL tools and workflows.
Understand the principles of data warehousing and data lake architectures.

Read 'Designing Data-Intensive Applications'

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Gain a deeper understanding of data system design principles to better evaluate the potential and limitations of generative AI in data engineering.

View Designing Data-Intensive Applications: The Big... on Amazon

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Read the chapters on data models and storage engines.
Study the sections on distributed systems and fault tolerance.
Consider how generative AI could impact the design choices discussed in the book.

Explore Generative AI Tutorials for SQL Generation

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Practice using generative AI tools to generate SQL queries from natural language descriptions, a key skill for data engineers.

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Find online tutorials demonstrating SQL generation using tools like OpenAI Codex or similar models.
Experiment with different prompts and database schemas to test the accuracy and efficiency of the generated SQL.
Compare the generated SQL with your own hand-written queries to identify areas for improvement.

Four other activities

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Project: Automate Data Pipeline Documentation with Generative AI

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Develop a project that uses generative AI to automatically generate documentation for data pipelines, improving maintainability and collaboration.

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Choose a data pipeline project with existing code and configuration files.
Use a generative AI model to analyze the code and configuration and generate documentation in a suitable format (e.g., Markdown, HTML).
Evaluate the quality of the generated documentation and refine the prompts or model parameters to improve accuracy and completeness.
Integrate the documentation generation process into the data pipeline's CI/CD workflow.

Create a Blog Post on Ethical Considerations of Generative AI in Data Engineering

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Research and write a blog post discussing the ethical implications of using generative AI in data engineering, such as data privacy, bias, and transparency.

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Research the ethical challenges associated with generative AI, focusing on data privacy, bias, and transparency.
Outline the key points and arguments for your blog post.
Write the blog post, providing examples and potential solutions to the ethical challenges.
Publish the blog post on a platform like Medium or your personal website.

Read 'Generative Deep Learning'

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Deepen your understanding of the generative AI models used in data engineering tasks.

View Generative Deep Learning: Teaching Machines To... on Amazon

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Read the chapters on GANs, VAEs, and transformers.
Experiment with the code examples provided in the book.
Consider how these models can be applied to data generation, augmentation, and anonymization.

Contribute to an Open-Source Generative AI Project for Data

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Contribute to an open-source project that uses generative AI for data-related tasks, gaining practical experience and contributing to the community.

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Find an open-source project that aligns with your interests and skills.
Review the project's documentation and contribution guidelines.
Identify a bug or feature that you can work on.
Submit a pull request with your changes.

Career center

Learners who complete Mastering Generative AI for Data Engineering will develop knowledge and skills that may be useful to these careers:

Data Engineer

As a data engineer, you are responsible for designing, building, and maintaining the infrastructure that enables data-driven decision-making within an organization. This course directly aligns with the responsibilities of a data engineer, as it explores how generative AI can be leveraged to enhance productivity and innovation in data engineering projects. By learning how to use generative models for tasks such as data pipeline creation, database querying, and data warehouse schema design, you can become a more efficient and effective data engineer. The hands-on labs and projects included in the course will give you practical experience applying generative AI to real-world data engineering challenges, such as ETL and data repository design.

See salaries and explore the career path for Data Engineer

Data Warehousing Specialist

Data warehousing specialists focus on designing, building, and maintaining data warehouses. The skills taught in this course apply directly to the data warehousing specialist career path. Throughout the course, you'll dive deep into how generative AI tools and techniques can enhance data engineering processes across different industries. You will learn how to apply generative AI to data generation, augmentation, and anonymization, as well as evaluate case studies of successful generative AI applications for ETL and data repositories. By completing the hands-on labs and projects, you'll gain practical experience in data warehouse schema design and infrastructure setup.

See salaries and explore the career path for Data Warehousing Specialist

ETL Developer

An ETL developer builds data pipelines that extract, transform, and load data from various sources into a data warehouse or data lake. This course is directly relevant to your work as an ETL developer, particularly the module on generative AI for ETL and data repositories. The course teaches you how to use generative AI to automate the design and development of ETL workflows, which can significantly improve your productivity. You'll also learn how to address the considerations and use cases of generative AI. The project-based approach allows you to gain practical experience with real-world ETL challenges.

See salaries and explore the career path for ETL Developer

AI Data Specialist

An AI data specialist concentrates on curating and managing data to enhance AI models. This course is highly relevant, as it emphasizes generative AI's role in data augmentation, anonymization, and generation. This course can significantly improve your proficiency in preparing data for AI applications. The hands-on component of this course is valuable, as it allows you to apply generative AI techniques to real-world data engineering challenges, making you an adept AI data specialist.

See salaries and explore the career path for AI Data Specialist

AI Integration Engineer

AI integration engineers specialize in incorporating AI tools and models into existing systems and workflows. This course directly addresses the integration of generative AI into data engineering processes, making it a valuable resource for AI integration engineers. The course teaches how generative AI can enhance productivity and innovation in data engineering projects. Hands-on experience is emphasized, where AI becomes an integral part of architecture design, pipeline creation, and data analysis.

See salaries and explore the career path for AI Integration Engineer

Data Architect

A data architect is responsible for designing and implementing the overall structure of an organization's data systems. You need extensive knowledge of data modeling, database technologies, and data warehousing concepts. This course would be useful because it covers how generative AI can be applied to tasks such as data warehouse schema design, data lakehouse implementation, and data repository management. You can learn how to leverage generative AI to optimize data infrastructure and improve data accessibility. The course's emphasis on real-world case studies and hands-on projects can provide practical insights into how generative AI can be integrated into data architecture strategies.

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Cloud Data Engineer

Cloud data engineers specialize in building and maintaining data infrastructure on cloud platforms such as AWS, Azure, or Google Cloud. This course can be helpful for cloud data engineers, as it covers how to use generative AI to automate infrastructure setup and configuration. By learning how to leverage generative AI for tasks such as data pipeline creation and data warehouse schema design, you can potentially streamline cloud data engineering workflows and improve the efficiency of cloud-based data systems. The real-world data engineering project in this course gives you hands on practice.

See salaries and explore the career path for Cloud Data Engineer

Business Intelligence Analyst

A business intelligence analyst analyzes data to identify trends and insights that can inform business decisions. This course is relevant because it explores how generative AI can be used for data analysis and data mining. By learning how to leverage generative AI to automate data exploration and pattern discovery, you can potentially identify more valuable insights and communicate them more effectively to stakeholders. The course's coverage of data repositories and data visualization techniques may also be helpful for business intelligence analysts who need to present data in a clear and concise manner.

See salaries and explore the career path for Business Intelligence Analyst

Data Mining Specialist

A data mining specialist uncovers patterns and insights from large datasets, often using advanced statistical techniques. This course can be fairly relevant, particularly the section on leveraging generative AI for data analysis and mining. By knowing how to use generative AI to automate data exploration, pattern discovery, and anomaly detection, you can identify new insights. Also, the course's knowledge of data repositories and data visualization techniques may serve to present data in a clear and concise way.

See salaries and explore the career path for Data Mining Specialist

Data Scientist

The role of a data scientist involves extracting insights and knowledge from data using statistical modeling, machine learning, and data visualization techniques. This course may be useful for data scientists who want to enhance their data engineering skills. The course covers how to use generative AI for data augmentation and data anonymization, which can be valuable for improving the quality and quantity of data available for analysis. Furthermore, the course explores how generative AI can be applied to data analysis and mining, potentially leading to new and innovative approaches to data science problems. The knowledge gained in this course can help you collaborate more effectively with data engineers and build end-to-end data science solutions.

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Analytics Engineer

Analytics engineers focus on transforming raw data into usable datasets for analysis and reporting. As an Analytics Engineer, this course may be useful because it delves into generative AI's capabilities in data transformation and pipeline automation. You can explore the latest AI-driven techniques for refining data, building robust data warehouses, and ensuring data quality. The course's hands-on labs are invaluable, as they provide practical insights into how generative AI tools can be integrated into your daily workflows.

See salaries and explore the career path for Analytics Engineer

Data Governance Manager

The role of a data governance manager is to establish and enforce policies and procedures for managing data quality, security, and compliance. This course can be useful for data governance managers, as it explores the ethical considerations associated with using generative AI in data engineering. Understanding these considerations is crucial for ensuring that data is used responsibly and ethically. The course's content on data anonymization and data security may also be valuable for data governance managers who are responsible for protecting sensitive data. Furthermore, data governance managers may be required to have advanced degrees.

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Machine Learning Engineer

A machine learning engineer focuses on deploying and scaling machine learning models into production systems. As a machine learning engineer, you may find this course valuable as it examines how generative AI can improve data engineering processes, which indirectly supports the machine learning pipeline. Knowing how to use generative AI for data generation, data augmentation, and data anonymization, you can contribute to building more robust and reliable datasets for training machine learning models. The course's coverage of ETL workflows and data pipelines is also relevant, as these are critical components of the infrastructure that supports machine learning applications.

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Information Architect

An information architect focuses on organizing and structuring information to make it easy to find and use. This course can be valuable for information architects, as it delves into how generative AI can improve data engineering processes, indirectly improving information retrieval. Gaining insights into how to use generative AI for data generation, augmentation, and anonymization, you can help organizations build more comprehensive and accessible datasets. The course's exploration of ETL workflows and data pipelines is also relevant, as these are critical components of the infrastructure that supports information architecture.

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Database Administrator

The database administrator role handles the performance, integrity, and security of databases. This course may be useful for a database administrator, especially the section on using generative AI for database querying. By understanding how generative AI can automate and improve query generation, you can potentially streamline database management tasks and enhance data access for end-users. Furthermore, the course’s exploration of data warehouse schema design could be helpful for database administrators involved in data warehousing projects. Keep in mind that you'll also learn about challenges, as well as ethical considerations.

See salaries and explore the career path for Database Administrator