Virtualization and Containers for Data Engineering
This course is part of Data Engineering Foundations.
This comprehensive course explores virtualization, containerization, and orchestration technologies essential for modern data engineering. Students will learn fundamental concepts of virtualization and virtual machines, master Docker container deployment, and gain hands-on experience with Kubernetes orchestration. The curriculum covers cloud development environments, container registries, and production best practices including monitoring, testing, and CI/CD pipelines. Through practical exercises and real-world scenarios, participants will develop the skills to build and manage scalable containerized data solutions.
Instructors:
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What you'll learn
Master fundamental concepts of virtualization and virtual machine management
Build and deploy Docker containers for scalable microservices
Orchestrate containers using Kubernetes in cloud environments
Implement cloud development workflows with GitHub Codespaces
Manage container registries and deployments effectively
Develop production-ready monitoring and testing strategies
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 4 modules in this course
This comprehensive course covers essential technologies for modern data engineering infrastructure. Students learn virtualization fundamentals, working with virtual machines and Docker containers for building scalable microservices. The curriculum progresses through Kubernetes orchestration, cloud development environments with GitHub Codespaces, and container registry usage. Advanced topics include production best practices, monitoring systems, testing strategies, and implementing CI/CD pipelines. The course emphasizes hands-on experience with industry-standard tools, preparing students to build and manage containerized data solutions at scale.
Virtualization Theory and Concepts
Module 1
Using Docker
Module 2
Kubernetes: Container Orchestration in Action
Module 3
Building Kubernetes Solutions
Module 4
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Data Engineering Foundations
Instructors
30 Courses
Pioneering Tech Leader & AI Educator Shaping the Future of Machine Learning
Noah Gift is a distinguished technology leader and founder of Pragmatic AI Labs with a remarkable 30-year career spanning film, TV, telecom, social networks, startups, and big data, currently serving as an Executive-in-Residence at Duke University. As an AWS ML Hero and Python Software Foundation Fellow, he has authored best-selling books through O'Reilly and Pearson on DevOps, MLOps, data engineering, and cloud computing that are widely adopted by major universities. His expertise in MLOps, data engineering, cloud architecture, and Rust programming has led him to teach thousands of students across prestigious institutions including Duke, Northwestern, UC Berkeley, University of San Francisco, and UC Davis, while also collaborating with Caltech and JPL on automated IT systems. Gift's impact extends beyond academia through his work as a startup CTO, his role in developing scalable distributed systems, and his contributions as a keynote speaker at conferences focused on cloud development and ethical AI use, holding certifications from AWS, Google, and Microsoft, and having published over 100 technical works while conducting workshops for organizations like NASA, PayPal, and PyCon.
4 Courses
A Pioneering Data Engineer and AI Education Leader
Kennedy Behrman is a distinguished data engineering expert and educator who currently serves as a Senior Data Engineer at Envestnet, Inc. His extensive career spans data engineering, cloud solutions, and machine learning, backed by both undergraduate and graduate degrees from the University of Pennsylvania. As an accomplished author, he has written several influential books including "Foundational Python for Data Science" and co-authored "Python for DevOps" with O'Reilly Media. His educational impact extends through his role as an instructor for multiple prestigious courses, including the "Applied Python Data Engineering Specialization" and "Python, Bash and SQL Essentials for Data Engineering Specialization" which have reached over 21,000 students combined. Previously, he founded Pragmatic AI Labs, providing pragmatic cloud solutions for AI requirements, and held leadership positions including CTO and VP Engineering at Sqor Sports where he led engineering, product, and design teams. His technical expertise encompasses Python, data engineering, AWS solutions, and machine learning, with particular focus on developing scalable data pipelines and implementing cloud-based solutions. Behrman's influence in the field is further demonstrated through his contributions to various platforms, including the development of AWS machine learning certification tests and his role in creating comprehensive educational content for data science and engineering professionals.
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