In this advanced course, you will gain practical expertise in scaling data engineering systems using cutting-edge tools and techniques. This course is designed for data scientists, data engineers, and anyone with a foundational understanding of data handling who desires to escalate their skills to handle larger, more complex datasets efficiently.
Advanced Data Engineering
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Advanced Data Engineering
This course is part of Large Language Model Operations (LLMOps) Specialization
Instructors: Noah Gift
2,532 already enrolled
Included with 
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
23 hours to complete
3 weeks at 7 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Create and manage data pipelines and their lifecycle
Connect and work with message queues to manage data processing
Use vector, graph, and key/value databases for data storage at scale
Details to know
Shareable certificate
Add to your LinkedIn profile
Assessments
14 assignments
Taught in English
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This course is part of the Large Language Model Operations (LLMOps) Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course
In this module, you will learn about databases and queues. You will find out the purpose and components of RabbitMQ including its use of queues and integration with Celery. Through hands-on exercises, they will gain experience connecting Celery to RabbitMQ within a Flask application and implementing task patterns like fire and forget and result retrieval. The course also covers core MySQL skills like interacting via the command line interface, manipulating databases, and integrating with Python web apps. By the end, students will have a foundational understanding of RabbitMQ, Celery, and MySQL that allows them to start building modern, asynchronous applications backed by a database.
What's included
22 videos14 readings4 assignments1 discussion prompt1 ungraded lab
What's included
17 videos13 readings4 assignments
In this module, we explore vector and graph databases, powerful tools for managing and extracting insights from large, complex datasets. As data volumes continue to grow, scalability is crucial. We'll learn how vector and graph databases can efficiently store data while maintaining relationships, enabling more advanced analytics. Through real-world examples, you'll see how these databases unlock scalability for machine learning, fraud detection, social networks, and more.
What's included
14 videos11 readings3 assignments1 ungraded lab
In this final module, you will work on advanced real-world data engineering projects, applying everything you've learned. You'll encounter complex data challenges and devise solutions using the latest tools and techniques. This is an opportunity to bring together data engineering concepts covered throughout the course and implement them holistically to deliver impactful outcomes.
What's included
13 videos9 readings3 assignments2 ungraded labs
Offered by
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