Advanced Data Engineering and Scalability
This course is part of multiple programs. Learn more.
This advanced course focuses on building and optimizing scalable data systems for enterprise applications. Students learn to handle massive datasets using cutting-edge technologies like Celery, RabbitMQ, and Apache Airflow. The curriculum covers advanced database implementations including vector and graph databases, along with practical experience in designing and optimizing complex data pipelines. Through hands-on projects, participants develop expertise in creating robust, scalable solutions for real-world data challenges.
Instructors:
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What you'll learn
Create and optimize enterprise-scale data pipelines
Implement message queues for efficient data processing
Master workflow management with Apache Airflow
Utilize vector and graph databases for scalable solutions
Design efficient data processing architectures
Optimize system performance for large datasets
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
Shareable certificate
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Top companies offer this course to their employees
Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.
There are 4 modules in this course
The course provides comprehensive training in advanced data engineering concepts and technologies. Starting with queue management using Celery and RabbitMQ, students progress through workflow optimization with Apache Airflow and advanced database implementations. The curriculum emphasizes scalability and performance optimization, covering vector databases, graph databases, and key-value stores. Practical projects focus on building real-world solutions for complex data challenges, including pipeline design and system optimization.
Queues and Databases-RabbitMQ and MySQL
Module 1
Optimizing Workflow Management at Scale with Apache Airflow
Module 2
Achieving Scalability with Vector, Graph, and Key/Value Databases
Module 3
Real-world Advanced Data Engineering Projects
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: Large Language Model Operations (LLMOps), 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.
20 Courses
A Technology Educator and Former Olympic Athlete Pioneering AI Innovation
Alfredo Deza embodies a unique combination of athletic excellence and technological expertise, transitioning from a distinguished career as Peru's first World Junior Champion in high jump and 2004 Olympian to becoming a leading voice in technology education and development. Currently serving as a Principal Cloud Advocate at Microsoft and Adjunct Assistant Professor at Duke University's Pratt School of Engineering, Deza has built an impressive career spanning nearly two decades in software engineering and education. His academic contributions extend through guest lectures at prestigious institutions including Oxford University, Georgia Tech, and Carnegie Mellon University, where he shares expertise in machine learning, cloud computing, and programming languages. As an accomplished author, he has co-authored several influential books with O'Reilly Media, including "Practical MLOps" and "Python for DevOps," while developing comprehensive courses on Coursera covering topics from large language models to Rust programming. His teaching portfolio at Duke includes graduate-level courses in machine learning operations and Python programming, reflecting his commitment to making complex technical concepts accessible. Deza's expertise encompasses a broad spectrum of technologies, including Azure, MLOps, DevOps, Python, Rust, and Databricks, which he leverages to bridge the gap between academic theory and industry practice. His unique perspective, shaped by his background as an Olympic athlete, influences his approach to teaching and technology, emphasizing the importance of continuous learning and knowledge sharing in the rapidly evolving field of artificial intelligence and cloud computing.
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