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Introduction to Data Engineering

Joe Reis

In this course, you will be introduced to the data engineering lifecycle, from data generation in source systems, data ingestion, data transformation, storage, all the way to serving data to downstream stakeholders. You’ll take note of the key undercurrents that affect all stages of the lifecycle, and start developing a framework for how to think like a data engineer.

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In this course, you will be introduced to the data engineering lifecycle, from data generation in source systems, data ingestion, data transformation, storage, all the way to serving data to downstream stakeholders. You’ll take note of 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. You’ll also build an end-to-end data system that encompasses all stages of the data engineering lifecycle, dive into some of the details of batch and streaming pipelines to serve data for a product recommendation system, and apply principles of good data architecture to assess the security, performance, reliability, and scalability of a web app hosted on AWS.

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Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Examines the data engineering lifecycle from gathering stakeholder needs to serving data to downstream stakeholders
Provides hands-on practice in building end-to-end data systems that encompass all stages of the data engineering lifecycle
Develops foundational skills for understanding the key undercurrents that affect all stages of the data engineering lifecycle
Teaches principles of good data architecture for assessing the security, performance, reliability, and scalability of data systems
Offers instructors who are experienced professionals in the field of data engineering
Requires students to have a basic understanding of data engineering concepts

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Reviews summary

Practical data engineering fundamentals on aws

Learners say this course provides a strong foundational understanding of the data engineering lifecycle, from data generation to serving. Students particularly value the hands-on approach, praising the opportunity to build an end-to-end data system and apply concepts using AWS services. While it offers a clear introduction to batch and streaming pipelines, some learners found the pace quite challenging for absolute beginners, suggesting prior familiarity with programming or cloud basics would be beneficial. Overall, it's considered a valuable starting point for aspiring data engineers, though a few mentioned a desire for more in-depth coverage on specific advanced topics.
A broad introduction, but some wish for more advanced detail.
"The course covers a lot of ground, giving a wide perspective, which is great for an introduction."
"I would have loved to dive deeper into specific topics like advanced data modeling or more complex pipeline optimization techniques."
"It's a fantastic starting point, but I know I'll need to explore more specialized courses to become truly proficient in certain areas."
Provides a comprehensive yet challenging introduction to data engineering.
"This course gave me a great overview of the entire data engineering lifecycle, truly an 'introduction' in scope."
"As a beginner, I found the pace quite fast at times, requiring me to pause and re-watch lectures frequently."
"While it's an introduction, I felt some prior knowledge in Python or SQL would have made the initial learning curve smoother for me."
Effective use of AWS services for practical data engineering tasks.
"Using AWS throughout the course really helped me connect theoretical concepts to real cloud environments."
"I appreciated the focus on AWS for building scalable and reliable data solutions, very relevant for my career goals."
"The modules on applying data architecture principles to a web app hosted on AWS were particularly insightful and useful."
Excellent opportunities to build real-world data systems.
"The hands-on activities, especially building an end-to-end data system, were incredibly helpful for solidifying my understanding."
"I really appreciated the practical application of concepts using AWS; it made the learning very relevant to industry."
"Building the product recommendation system pipeline was a highlight, giving me tangible experience and boosting my confidence."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Introduction to Data Engineering with these activities:
Compile course materials
Gather and organize all relevant resources to better engage with course materials.
Show steps
  • Gather notes, assignments, and quizzes
  • Organize materials chronologically or by topic
  • Review materials for clarity and understanding
Follow a data ingestion tutorial
Enhance data ingestion understanding and skills by following a guided tutorial.
Show steps
  • Identify a reputable data ingestion tutorial
  • Follow the tutorial step-by-step
  • Apply the concepts to a personal project
Join a data engineering study group
Enhance learning through collaborative problem-solving and knowledge-sharing with peers in a study group.
Show steps
  • Find or create a study group
  • Establish regular meeting times
  • Discuss course materials, share resources
  • Work together on assignments or projects
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice data transformation exercises
Solidify data transformation skills by completing targeted exercises regularly.
Show steps
  • Identify data transformation techniques
  • Find or create practice datasets
  • Apply data transformation techniques
Attend a data engineering workshop
Acquire practical, specialized knowledge by engaging in hands-on, in-person data engineering workshops.
Browse courses on Data Engineering
Show steps
  • Research relevant workshops
  • Register and attend the workshop
  • Actively participate in hands-on activities
  • Network with experts and peers
Build a basic data processing pipeline
Apply data engineering principles and skills by constructing a data processing pipeline from scratch.
Show steps
  • Define the input and output data sources
  • Choose appropriate data transformation techniques
  • Implement the pipeline using a suitable programming language
  • Test and refine the pipeline
Participate in a data hackathon
Accelerate learning and expand practical skills by participating in a data hackathon related to data engineering.
Show steps
  • Identify relevant data hackathons
  • Form or join a team
  • Develop innovative solutions to data-related challenges
  • Present the solution and compete for recognition

Career center

Learners who complete Introduction to Data Engineering will develop knowledge and skills that may be useful to these careers:

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Effort
This is a 4-week course with an estimate of 4-6 hours of learning activities per week
Level
Intermediate
Via
Coursera
Institutions
Amazon Web Services
DeepLearning.AI
Instructor
Joe Reis
Language
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Examines the data engineering lifecycle from gathering stakeholder needs to serving data to downstream stakeholders
Provides hands-on practice in building end-to-end data systems that encompass all stages of the data engineering lifecycle
Develops foundational skills for understanding the key undercurrents that affect all stages of the data engineering lifecycle
Teaches principles of good data architecture for assessing the security, performance, reliability, and scalability of data systems
Offers instructors who are experienced professionals in the field of data engineering
Requires students to have a basic understanding of data engineering concepts
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