Data Engineering with Databricks
This course is part of Large Language Model Operations (LLMOps).
This comprehensive course teaches data engineering on the Databricks unified lakehouse platform. Students learn to create and manage Databricks clusters, develop ETL pipelines with Delta Lake, and implement advanced data processing techniques using Apache Spark. The curriculum covers essential skills including notebook analysis, data ingestion from diverse sources, complex data transformations, and production pipeline development. Through hands-on labs and real-world projects, participants master data quality management, workload scheduling, and security implementation using Unity Catalog.
Instructors:
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What you'll learn
Create and manage Databricks clusters for efficient workload processing
Build reliable ETL pipelines using Delta Lake and Apache Spark
Implement advanced data transformations with SQL and DataFrame operations
Develop production-ready pipelines with Delta Live Tables
Secure data access and manage permissions with Unity Catalog
Skills you'll gain
This course includes:
PreRecorded video
Labs, Projects, Assignments
Access on Mobile, Tablet, Desktop
Limited Access access
Shareable certificate
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There are 2 modules in this course
This course provides comprehensive training in modern data engineering using the Databricks platform. Students learn cluster management, ETL pipeline development, data transformation techniques, and production deployment strategies. The curriculum emphasizes hands-on practice with real-world scenarios and best practices for data lakehouse architecture.
Databricks Lakehouse Platform Fundamentals
Module 1
Data Transformation and Pipelines
Module 2
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: Large Language Model Operations (LLMOps)
Instructors
Executive in Residence and Founder of Pragmatic AI Labs at Duke University
Noah Gift is the founder of Pragmatic AI Labs and serves as an Executive in Residence at Duke University, where he lectures in the Master of Interdisciplinary Data Science (MIDS) program. He specializes in designing and teaching graduate-level courses on machine learning, MLOps, artificial intelligence, and data science, while also consulting on machine learning and cloud architecture for students and faculty. A recognized expert in the field, Gift is a Python Software Foundation Fellow and an AWS Machine Learning Hero, holding multiple AWS certifications, including AWS Certified Solutions Architect and AWS Certified Machine Learning Specialist. He has authored several influential books, such as Practical MLOps, Python for DevOps, and Pragmatic AI, and has published over 100 technical articles across various platforms, including Forbes and O'Reilly. His extensive industry experience includes roles as CTO and Chief Data Scientist for notable companies like Disney Feature Animation, Sony Imageworks, and AT&T, contributing to major films like Avatar and Spider-Man 3. Gift's work has generated millions in revenue through product development on a global scale. He actively consults startups on machine learning and cloud architecture while leading initiatives to enhance data science education.
Adjunct Assistant Professor at Duke University
Dr. Alfredo Deza is an Adjunct Assistant Professor in the Pratt School of Engineering at Duke University, where he teaches courses on machine learning, programming, and data engineering. He has been involved in academia for several years, focusing on innovative teaching methods and practical applications of technology. Dr. Deza co-authored the book Practical MLOps and has published several other works related to Python and machine learning. His teaching includes courses such as Python Bootcamp and advanced data engineering topics, and he actively develops online courses available on platforms like Coursera. In addition to his academic role, Dr. Deza works in developer relations at Microsoft, leveraging his extensive experience in software engineering and cloud computing to enhance educational content and support for students and faculty. He collaborates with various universities worldwide, including Georgia Tech and Carnegie Mellon University, to promote knowledge sharing in the field of technology and data science.
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