In this course, you will be introduced to the data engineering lifecycle, from data generation in source systems, to ingestion, transformation, storage, and serving data to downstream stakeholders. You’ll study the key undercurrents that affect all stages of the lifecycle, and start developing a framework for how to think like a data engineer. To gain hands-on practice, you’ll gather stakeholder needs, translate those needs into system requirements, and choose tools and technologies to build systems that provide business value. By the end of this course you’ll be spinning up batch and streaming data pipelines to serve product recommendations on the AWS cloud!
Introduction to Data Engineering
Introduction to Data Engineering
This course is part of DeepLearning.AI Data Engineering Professional Certificate
Instructor: Joe Reis
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Gain insight into a topic and learn the fundamentals.
4.8
(191 reviews)
Intermediate level
Recommended experience
Flexible schedule
Approx. 17 hours
Learn at your own pace
96%
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What you'll learn
Gain a comprehensive understanding of the data engineering lifecycle and its undercurrents
Gather stakeholder needs and translate them into system requirements
Design and implement batch and streaming data pipelines on AWS
Skills you'll gain
Details to know
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Recently updated!
September 2024
Assessments
6 assignments
Taught in English
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This course is part of the DeepLearning.AI Data Engineering Professional Certificate
When you enroll in this course, you'll also be enrolled in this Professional Certificate.
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There are 4 modules in this course
Gain a high-level overview of the data engineering lifecycle and key undercurrents to understand how data engineers add business value to organizations. Start developing a mental framework for thinking like a data engineer, starting with gathering stakeholder needs and translating them into system requirements. Learn the basics of working on the cloud from an AWS expert.
What's included
18 videos10 readings1 assignment1 app item
Dive deeper into the stages of the data engineering lifecycle and its key undercurrents. Build an end-to-end data pipeline on AWS that encompasses all the stages of the data engineering lifecycle.
What's included
20 videos3 readings1 assignment1 app item
Define data architecture and how it fits within the larger enterprise architecture. Examine the principles of good data architecture and how these principles inform tools and technology choices. Evaluate and optimize the security, performance, reliability, cost-efficiency, and scalability of a web application hosted on AWS.
What's included
22 videos2 readings1 assignment1 app item
Practice gathering stakeholder needs and translating them into system requirements. Choose the appropriate tools and technologies based on the system requirements, then build an end-to-end data system that includes a batch and a streaming component to train a product recommendation system and serves product recommendations to a sales platform.
What's included
17 videos5 readings3 assignments1 app item
Instructor
Instructor ratings
4.8 (83 ratings)
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