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
Ce cours fait partie de DeepLearning.AI Data Engineering Certificat Professionnel
Instructeur : Joe Reis
Enseignant de premier plan
17 014 déjà inscrits
(219 avis)
Expérience recommandée
Ce que vous apprendrez
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
Compétences que vous acquerrez
- Catégorie : The principles of good data architecture
- Catégorie : Requirements Gathering
- Catégorie : AWS cloud fundamentals
- Catégorie : Thinking like a data engineer
- Catégorie : Translating requirements into tool and technology choices
Détails à connaître
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septembre 2024
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Il y a 4 modules dans ce cours
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.
Inclus
18 vidéos10 lectures1 devoir1 élément d'application
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.
Inclus
20 vidéos3 lectures1 devoir1 élément d'application
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.
Inclus
22 vidéos2 lectures1 devoir1 élément d'application
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.
Inclus
17 vidéos5 lectures3 devoirs1 élément d'application
Instructeur
Recommandé si vous êtes intéressé(e) par Cloud Computing
Pour quelles raisons les étudiants sur Coursera nous choisissent-ils pour leur carrière ?
Avis des étudiants
219 avis
- 5 stars
85,52 %
- 4 stars
11,31 %
- 3 stars
1,80 %
- 2 stars
0,90 %
- 1 star
0,45 %
Affichage de 3 sur 219
Révisé le 9 oct. 2024
Labs were very much handheld with every step being explained, not much do it yourself
Révisé le 18 oct. 2024
I appreciate the addition of the DE mental framework as it gives a whole lot of context to the more practical knowledge that will be learned in the next courses.
Révisé le 26 oct. 2024
High level overview is great! But I wish the lab should involve hands-on application, and not just following instructions
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