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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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Good to know

Know what's good
, what to watch for
, 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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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
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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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