Web Applications and Command-Line Tools for Data Engineering
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Web Applications and Command-Line Tools for Data Engineering
This course is part of Python, Bash and SQL Essentials for Data Engineering Specialization
Instructors: Noah Gift
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What you'll learn
Construct Python Microservices with FastAPI
Build a Command-Line Tool in Python using Click
Compare multiple ways to set up and use a Jupyter notebook
Skills you'll gain
Tools you'll learn
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There are 4 modules in this course
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.
In this module, 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.
What's included
8 videos5 readings1 assignment1 discussion prompt3 ungraded labs
8 videos•Total 27 minutes
- Introduction to Web Applications and Command-Line Tools for Data Engineering•0 minutes
- Overview of Key Concepts•6 minutes
- Introduction to Jupyter Notebooks•0 minutes
- Getting Started with Jupyter•3 minutes
- Code Cells in Jupyter•3 minutes
- Text Cells in Jupyter•4 minutes
- Magics in Jupyter•6 minutes
- Overview of Jupyter Lab•5 minutes
5 readings•Total 50 minutes
- Meet your Instructors•10 minutes
- Welcome•10 minutes
- Course Structure and Etiquette•10 minutes
- Report a problem with the course•10 minutes
- Key Terms•10 minutes
1 assignment•Total 30 minutes
- Introduction to Jupyter•30 minutes
1 discussion prompt•Total 10 minutes
- Meet and Greet (optional)•10 minutes
3 ungraded labs•Total 180 minutes
- Code Cells in Jupyter•60 minutes
- Text Cells in Jupyter•60 minutes
- Magics in Jupyter•60 minutes
In this module, you will learn how to create and use a Cloud-based notebook in Google Colab and AWS Sagemaker.
What's included
6 videos4 readings7 assignments1 ungraded lab
6 videos•Total 19 minutes
- Introduction to Colab•2 minutes
- Tour of Colab Features•4 minutes
- Data and Documents in Colab•4 minutes
- Introduction to SageMaker•4 minutes
- Tour of SageMaker Studio•3 minutes
- Overview of SageMaker Pipelines•3 minutes
4 readings•Total 40 minutes
- Key Terms•10 minutes
- Important Notebook Links•10 minutes
- Key Terms•10 minutes
- Get started with Code Editor in Amazon SageMaker Studio•10 minutes
7 assignments•Total 210 minutes
- Introduction to Colab•30 minutes
- Colab Features•30 minutes
- Data and Documents in Colab•30 minutes
- Introduction to SageMaker•30 minutes
- SageMaker Studio•30 minutes
- SageMaker Pipelines•30 minutes
- Jupyter Notebooks•30 minutes
1 ungraded lab•Total 60 minutes
- Notebook Review•60 minutes
In this module, you will learn how to build a Python Microservice with FastAPI and deploy a containerized machine learning Microservice for data engineering.
What's included
11 videos7 readings4 assignments1 ungraded lab
11 videos•Total 78 minutes
- Introduction to Building Python Microservices•0 minutes
- What are the Benefits of Microservices?•4 minutes
- Setting up Python Project Structure for Continuous Integration•7 minutes
- Building a Random Fruit Web App with Python•4 minutes
- Introduction to Python Microservices with FastAPI•1 minute
- Building FastAPI Microservices for Machine Learning Predictions•6 minutes
- Deploying a Python Lambda Microservice•13 minutes
- Introduction to Building Containerized Microservices•1 minute
- Why use Containers for Microservices?•2 minutes
- Deploying a Containerized .NET 6 API•7 minutes
- Deploying a Containerized Machine Learning Microservice•34 minutes
7 readings•Total 70 minutes
- Key Terms•10 minutes
- Lesson Reflection•10 minutes
- Key Terms•10 minutes
- Lesson Reflection•10 minutes
- Key Terms•10 minutes
- Containers and Container Services•10 minutes
- Lesson Reflection•10 minutes
4 assignments•Total 570 minutes
- What are the key components of Python Microservices?•30 minutes
- Quiz-Exploring the Benefits and Characteristics of Microservices•180 minutes
- Quiz-Building Python Microservices•180 minutes
- Quiz-Building Containerized Microservices•180 minutes
1 ungraded lab•Total 60 minutes
- Building a Python Microservice•60 minutes
In this module, you will learn how to organize a Python project so you can build a powerful command-line tool. You will use Click, a useful command-line tool framework to enhance your tool. Finally, you will automate testing and quality control for publishing and sharing your tool with a registry.
What's included
25 videos12 readings5 assignments5 ungraded labs
25 videos•Total 126 minutes
- Introduction to Python Packaging and Command-Line Tools•1 minute
- Introduction to Building Command-Line Tools•0 minutes
- Getting Started with Python Projects•5 minutes
- Overview of Command-Line Tool Frameworks•4 minutes
- Using Click to Build a Command-Line Tool•4 minutes
- Exploring Advanced Command-Line Tool Features•5 minutes
- Recap of Building Command-Line Tools•1 minute
- Introduction to Packaging and Distributing your Python Project•1 minute
- Introduction to Python Packaging•5 minutes
- Working with Python Setup Tools•6 minutes
- Uploading to a Python Registry•5 minutes
- Recap of Packaging and Distributing your Python Project•1 minute
- Introduction to Continuous Integration for Command-Line Tools•1 minute
- Introduction to Linting•5 minutes
- Automating Testing with GitHub Actions•5 minutes
- Automating Publishing of your Python Project•9 minutes
- Recap of Continuous Integration for Command-Line Tools•1 minute
- Introduction•1 minute
- Setting up your development environment for Command-line development•11 minutes
- Your first Command-line tool in Rust•12 minutes
- Working with user input: arguments and options•10 minutes
- Expanding your tool's functionality with modules and libraries•8 minutes
- Managing output: logging, errors, and panics•12 minutes
- Optimizing your Command-line tools: Performance and best practices•9 minutes
- Big O Notation-Final Challenge Walkthrough•5 minutes
12 readings•Total 120 minutes
- Key Terms•10 minutes
- Building Command-Line Tools •10 minutes
- Key Terms•10 minutes
- Key Terms•10 minutes
- Key terms•10 minutes
- External lab: Setup your development environment•10 minutes
- Introduction to Rust command line tools•10 minutes
- External lab: Build your first Rust CLI•10 minutes
- Key Terms•10 minutes
- Lesson Reflection•10 minutes
- Next Steps•10 minutes
- Share your learning experience•10 minutes
5 assignments•Total 750 minutes
- Command-Line Tools and Packaging •30 minutes
- Practice Quiz•180 minutes
- Packaging•180 minutes
- Continuous Integration•180 minutes
- Quiz-Big O Notation•180 minutes
5 ungraded labs•Total 210 minutes
- Install an editable Python CLI tool•30 minutes
- Install a Python CLI tool•30 minutes
- Test and validate a Python CLI tool•30 minutes
- Updating a Command-Line Tool•60 minutes
- Big O Notation Final Challenge•60 minutes
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Reviewed on Feb 14, 2023
covered all the fundamentals can be little slower and detailed
Reviewed on Jan 15, 2025
Fantastic course. Thanks for all teachers involved in writing this.
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