Data Engineering with Spark, Hadoop, and Snowflake
Master essential data engineering platforms and learn to build efficient, scalable data pipelines using Python and MLflow.
Master essential data engineering platforms and learn to build efficient, scalable data pipelines using Python and MLflow.
This comprehensive course covers the fundamentals of modern data engineering platforms including Hadoop, Spark, and Snowflake. Students learn to optimize and manage data pipelines, execute analytics using Databricks, and implement machine learning workflows with MLflow. The curriculum includes hands-on experience with PySpark for data science, along with best practices in DataOps and DevOps methodologies. Through practical exercises and real-world scenarios, participants develop skills in building scalable, efficient data solutions.
Instructors:
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What you'll learn
Master essential data engineering platforms like Hadoop, Spark, and Snowflake
Execute advanced data analytics using Databricks and PySpark
Implement end-to-end machine learning workflows with MLflow
Optimize and manage scalable data pipelines
Apply DataOps and DevOps methodologies to data engineering projects
Build efficient ETL processes using modern tools
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 data engineering course focuses on mastering essential platforms and tools for modern data processing. Beginning with foundational concepts in Hadoop and Spark, students progress through advanced topics including Snowflake data warehousing, Databricks analytics, and MLflow implementation. The curriculum emphasizes practical skills in building data pipelines, executing analytics, and managing machine learning workflows, while incorporating industry best practices in DataOps and project management.
Overview and Introduction to PySpark
Module 1
Snowflake
Module 2
Azure Databricks and MLFlow
Module 3
DataOps and Operations Methodologies
Module 4
Fee Structure
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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