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Web Applications and Command-Line Tools for Data Engineering

Noah Gift, Kennedy Behrman, and Alfredo Deza

In this fourth course of the Python, Bash and SQL Essentials for Data Engineering Specialization, you will build upon the data engineering concepts introduced in the first three courses to apply Python, Bash and SQL techniques in tackling real-world problems. First, we will dive deeper into leveraging Jupyter notebooks to create and deploy models for machine learning tasks. Then, we will explore how to use Python microservices to break up your data warehouse into small, portable solutions that can scale. Finally, you will build a powerful command-line tool to automate testing and quality control for publishing and sharing your tool with a data registry.

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What's inside

Syllabus

Jupyter Notebooks
This week, you will learn how to install and run Jupyter on your local machine. Additionally, you will explore strategies to use code and text cells in a Jupyter notebook.
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Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Course focuses on skills that are highly relevant in a data engineering setting
Introduces cloud-hosted notebooks, which are commonly used in industry
Provides opportunities for hands-on practice through lab exercises

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Create your own learning path. Save this course to your list so you can find it easily later.
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Reviews summary

Practical data engineering tools & apps

According to learners, this course is a highly practical and hands-on experience, providing valuable skills for data engineering. Students praise its coverage of modern technologies like FastAPI for microservices and Docker for containerization, alongside building powerful command-line tools with Click. While the course delivers highly relevant content, some learners found the pace very fast and noted that it assumes significant prior knowledge in Python, Bash, and cloud services, making environment setup challenging for some. Overall, it's considered excellent for aspiring or current data engineers with a solid foundation.
Unique focus on automating tasks.
"Building a CLI tool with Click was a highlight. ... The emphasis on testing and quality control for publishing tools is unique and very valuable."
"The final project of building a CLI tool from scratch was a fantastic learning experience."
"Valuable content for data engineering, especially the segments on packaging Python projects and automating command-line tools."
Covers modern, industry-standard tools.
"The section on FastAPI microservices and Docker deployment was incredibly useful."
"Excellent course for building practical data engineering skills. The focus on web applications with FastAPI and containerization with Docker is highly relevant..."
"The content on FastAPI and Rust CLI tools (via Click) is current and valuable."
Focuses on real-world data engineering.
"This course was exactly what I needed to bridge the gap between theoretical knowledge and practical data engineering."
"Absolutely loved the practical focus! Building a CLI tool with Click was a highlight. This course isn't just about theory..."
"I particularly appreciated the practical examples and the emphasis on best practices. It's truly for aspiring or current data engineers."
Setup can be frustrating and challenging.
"The initial environment setup was a bit frustrating, requiring a lot of troubleshooting."
"I needed to consult external documentation for certain setup issues, especially with AWS Sagemaker."
"My main critique is that some explanations could have been more detailed, particularly for intermediate learners who might not have deep prior experience."
Assumes significant prior knowledge.
"I felt that a strong background in Python, Bash, and some cloud basics is a must, which wasn't fully emphasized."
"I struggled a lot with this course...the level of assumed knowledge is very high."
"Highly disappointed. The course assumes a strong foundation in Python, Bash, Docker, and cloud services."

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 Web Applications and Command-Line Tools for Data Engineering with these activities:
Read 'Python for Data Analysis'
Expand your knowledge of Python for data analysis in preparation for the course.
View Python for Data Analysis on Amazon
Show steps
  • Read chapters relevant to data engineering concepts
  • Take notes and summarize key concepts
Practice Python basics
Refresh your memory on basic Python skills to prepare for the course.
Browse courses on Python Basics
Show steps
  • Review variables, data types, and operators
  • Practice writing simple functions
  • Solve simple coding problems
Learn about Jupyter Notebooks
Familiarize yourself with Jupyter Notebooks, the primary tool you'll use in the course.
Browse courses on Jupyter Notebooks
Show steps
  • Follow a tutorial on setting up and using Jupyter Notebooks
  • Create a sample Jupyter Notebook and experiment with its features
Five other activities
Expand to see all activities and additional details
Show all eight activities
Attend a Python study group
Connect with fellow learners and discuss course concepts to enhance your understanding.
Browse courses on Python
Show steps
  • Find or create a study group with other learners in the course
  • Meet regularly to discuss course topics, share ideas, and work on projects together
Solve coding challenges
Practice your coding skills by solving challenges related to data engineering.
Browse courses on Python Coding
Show steps
  • Choose a coding challenge platform (e.g., LeetCode, HackerRank)
  • Select challenges related to Python, data analysis, and data engineering
  • Solve the challenges and review your solutions
Create a Python Microservice
Gain practical experience by building a Python Microservice for data engineering.
Show steps
  • Design the microservice's functionality and architecture
  • Implement the microservice using Python and a relevant framework
  • Test and deploy the microservice
Contribute to an open-source data engineering project
Gain real-world experience by contributing to a data engineering project on GitHub.
Browse courses on Open Source
Show steps
  • Find an open-source data engineering project on GitHub
  • Identify a feature or bug to work on
  • Fork the project, make changes, and submit a pull request
Develop a data engineering tool
Demonstrate your skills by creating a useful tool for data engineering tasks.
Browse courses on Python
Show steps
  • Identify a need or problem in data engineering
  • Design and implement a tool using Python
  • Test and document the tool

Career center

Learners who complete Web Applications and Command-Line Tools for Data Engineering will develop knowledge and skills that may be useful to these careers:
Data Engineer
Data Engineers design, build, and maintain data pipelines. Many Data Engineers have a bachelor's degree in computer science or a related field. This course may be useful if you are interested in learning more about Python, Bash and SQL techniques in tackling real-world problems.
See salaries and explore the career path for Data Engineer
Machine Learning Operations Engineer
Machine Learning Operations Engineers deploy, monitor, and maintain machine learning models. Many Machine Learning Operations Engineers have a bachelor's degree in computer science or a related field. This course may be useful if you are interested in learning more about deploying models for machine learning tasks using Python.
See salaries and explore the career path for Machine Learning Operations Engineer
Machine Learning Engineer
Machine Learning Engineers build, test, and deploy machine learning models. A background in computer science is required to become a Machine Learning Engineer, and many have a master's or PhD degree. This course may be useful for you because it will teach you how to apply Python, Bash and SQL techniques in tackling real-world problems, including Jupyter Notebooks and Cloud-Hosted Notebooks. This foundation can help prepare you to build machine learning models.
See salaries and explore the career path for Machine Learning Engineer
Data Scientist
Data Scientists collect, analyze, interpret, and present data. Many Data Scientists have a background in computer science, mathematics, or statistics, with at least a bachelor's degree. This course may be useful for you because it will teach you how to use Python, Bash and SQL techniques in tackling real-world problems. You will also learn how to use Python microservices to break up your data warehouse into small, portable solutions that can scale.
See salaries and explore the career path for Data Scientist
Data Analyst
Data Analysts collect, analyze, interpret, and present data. Many Data Analysts have a bachelor's in a related field, such as computer science, mathematics, or statistics. This course may be useful for you because you will learn more about managing databases with SQL.
See salaries and explore the career path for Data Analyst
Software Engineer
Software Engineers design, develop, and test software. A background in computer science is required. Many have a bachelor's degree in computer science or a related field. This course may be useful for you because you will learn about deploying models for machine learning tasks using Python.
See salaries and explore the career path for Software Engineer
Database Administrator
Database Administrators maintain, configure, and support databases, which are used to store and manage data. A background in computer science or a related field is required for this role and some Database Administrators may also have a master's degree. This course may be useful for you because you will learn more about managing databases with SQL.
See salaries and explore the career path for Database Administrator
Systems Engineer
Systems Engineers design, build, and maintain computer systems. A background in computer science or a related field is required. Many have a bachelor's degree in computer science or a related field. This course may be useful if you are interested in learning more about Python Packaging and Rust Command Line Tools.
See salaries and explore the career path for Systems Engineer
Cloud Engineer
Cloud Engineers design, build, and maintain cloud computing systems. A background in computer science or a related field is required. Many have a bachelor's degree in computer science or a related field. This course may be useful if you are interested in learning more about Cloud-Hosted Notebooks.
See salaries and explore the career path for Cloud Engineer
DevOps Engineer
DevOps Engineers work to bridge the gap between development and operations teams. They ensure that software is built, tested, and deployed efficiently. Many DevOps Engineers have a background in computer science or a related field and may also have a master's degree. This course may be useful for you if you are interested in learning more about Python Microservices and Python Packaging.
See salaries and explore the career path for DevOps Engineer
Business Analyst
Business Analysts work with stakeholders to define and document business requirements. A background in business or a related field is required for this role. This course may be useful if you are interested in learning more about using Python, Bash and SQL techniques in tackling real-world problems.
See salaries and explore the career path for Business Analyst
Product Manager
Product Managers work with stakeholders to define and document product requirements. A background in business or a related field is required for this role. This course may be useful if you are interested in learning more about using Python, Bash and SQL techniques in tackling real-world problems.
See salaries and explore the career path for Product Manager
Project Manager
Project Managers plan and execute projects. A background in business or a related field is required for this role. This course may be useful if you are interested in learning more about using Python, Bash and SQL techniques in tackling real-world problems.
See salaries and explore the career path for Project Manager
Quality Assurance Analyst
Quality Assurance Analysts test software to ensure that it meets requirements and is free of defects. A background in computer science or a related field is required for this role, and many also have a master's degree. This course may be useful if you are interested in learning more about automating testing and quality control.
See salaries and explore the career path for Quality Assurance Analyst
Technical Writer
Technical Writers create documentation for software and other technical products. A background in writing or a related field is required for this role. This course may be useful if you are interested in learning more about creating and deploying models for machine learning tasks using Python.
See salaries and explore the career path for Technical Writer

Reading list

We've selected 11 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Web Applications and Command-Line Tools for Data Engineering.
Cover image
The Elements of Statistical Learning
Save
Covers statistical learning concepts. It classic textbook in the field and is suitable for those who want to learn the fundamentals of statistical learning.
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Kindle Edition
$$$
Cover image
Deep Learning with Python, Second Edition
Save
Covers deep learning concepts using Python. It provides a comprehensive overview of the field and is suitable for those with some prior knowledge of machine learning.
Deep Learning with Python, Second Edition
Paperback
$$
Deep Learning with Python, Second Edition
Kindle Edition
$$
Cover image
Hands-On Machine Learning with Scikit-Learn, Keras,...
Save
Covers machine learning concepts using Python libraries such as Scikit-Learn, Keras, and TensorFlow. It is suitable for those who want to learn how to use these libraries for data analysis and machine learning.
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Kindle Edition
$$
Machine Learning Con Pytorch Y Scikit Lean
Save
Covers machine learning concepts using Python. It comprehensive guide to the field and is suitable for those who want to learn the fundamentals of machine learning.
(Español) Machine Learning con PyTorch y Scikit-Learn:...
Paperback
$$$
Cover image
Deep Learning with R, Second Edition
Save
Covers deep learning concepts using R. It comprehensive guide to the field and is suitable for those who want to learn the fundamentals of deep learning using R.
Deep Learning with R, Second Edition
Paperback
$$$
Deep Learning with R, Second Edition
Kindle Edition
$$
Cover image
Introduction to Machine Learning with Python
Save
Covers Python libraries for machine learning, such as NumPy, Pandas, and Matplotlib. It is useful for those who want to learn how to use Python for data manipulation and analysis.
Introduction to Machine Learning with Python: A...
Kindle Edition
$$
Cover image
Machine Learning with R
Save
Covers machine learning concepts using R. It comprehensive guide to the field and is suitable for those who want to learn the fundamentals of machine learning using R.
Machine Learning with R - Fourth Edition: Learn...
Paperback
$$
Machine Learning with R - Second Edition: Expert...
Kindle Edition
$$
Machine Learning with R - Third Edition: Expert...
Paperback
$$
(Italiano) Machine Learning con R: Conoscere le tecniche per...
Kindle Edition
$$
R: Unleash Machine Learning Techniques
Kindle Edition
$$$
Machine Learning with R: Learn How to Use R to...
Paperback
$$
(中文) Machine learning and R language (3rd edition of the...
Paperback
$$
Cover image
Python Data Science Handbook
Save
Covers Python libraries for data science, such as NumPy, Pandas, and Matplotlib. It is useful for those who want to learn how to use Python for data manipulation and analysis.
Python Data Science Handbook: Essential Tools for...
Paperback
$$$
Cover image
Python for Data Analysis
Save
Covers Python libraries for data analysis, such as NumPy, Pandas, and Matplotlib. It is useful for those who want to learn how to use Python for data manipulation and analysis.
Python for Data Analysis
Paperback
Check
Python for Data Analysis
Kindle Edition
Check
Cover image
Data Science from Scratch
Save
Covers data science concepts from scratch. It is suitable for beginners and provides a solid foundation in data science.
Data Science from Scratch: First Principles with...
Paperback
$$
Data Science from Scratch: First Principles with...
Kindle Edition
$$
Data Science from Scratch: First Principles with...
Paperback
$$
Cover image
Machine Learning for Hackers
Save
Covers machine learning concepts. It is written for non-technical readers and is suitable for those who want to learn the basics of machine learning without getting too technical.
Machine Learning for Hackers: Case Studies and...
Paperback
$$
Machine Learning for Hackers: Case Studies and...
Kindle Edition
$$

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Course image
Effort
4 weeks of study, 3-6 hours / week
Level
Intermediate
Via
Coursera
Institution
Duke University
Instructors
Noah Gift
Kennedy Behrman
Alfredo Deza
Language
English
Transcripts
Español Français Português Deutsch Italiano 中文 العربية 한국어 日本語 हिंदी Nederlands Svenska Pусский Türkçe Polski Ελληνικά українська мова Bahasa Indonesia ไทย қазақша
This course belongs to a collection called Python, Bash and SQL Essentials for Data Engineering. It's comprised of four courses:
  1. Linux and Bash for Data Engineering
  2. Web Applications and Command-Line Tools for Data Engineering (viewing)
  3. Scripting with Python and SQL for Data Engineering
  4. Python and Pandas for Data Engineering
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Course focuses on skills that are highly relevant in a data engineering setting
Introduces cloud-hosted notebooks, which are commonly used in industry
Provides opportunities for hands-on practice through lab exercises
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