Back-end Application Development Capstone Project
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Back-end Application Development Capstone Project
This course is part of IBM Back-End Development Professional Certificate
Instructor: IBM Skills Network Team
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What you'll learn
Demonstrate proficiency to develop and deploy backend services and applications
Apply your back-end development skills to author microservices and REST APIs in Flask
Create and query databases like MongoDB and integrate with Django apps
Containerize microservices using Docker and deploy using Kubernetes, OpenShift and serverless
Skills you'll gain
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There are 5 modules in this course
This is the final course in the IBM Backend Development Professional Certificate. This capstone project course will give you the chance to practice the work that back-end developers do in real life when working with applications.
In this course, you will assume the role of a real-life developer working to develop and deploy back-end microservices and applications. You will be tasked to develop, deploy, and integrate an application composed of several microservices, and integrating them seamlessly. You will use various back-end tools and technologies, including Flask, Django, NoSQL, and Mongo DB, to accomplish this project while applying coding best practices. You will also deploy and scale and update each of the application’s microservices independently using Cloud Native technologies: Kubernetes, OpenShift and serverless (Code Engine). In this course, you’ll mostly focus on hands-on work to demonstrate and apply what you have learnt in previous courses. By successfully completing this Capstone you will have added a project to your programming portfolio to showcase to employers.
In the first module, you will learn about Flask, a lightweight python-based web application framework. The module will teach you how to create a Flask application that responds to user requests at specific URLs. You will also learn about handling both server and user errors. You will learn how to make external API calls from Flask. You will apply these lessons to create a service that produces pictures from past events.
What's included
6 videos3 readings3 assignments3 app items1 plugin
6 videos•Total 37 minutes
- Course Introduction•3 minutes
- Introduction to Flask•7 minutes
- Basic Application and Routes•8 minutes
- Request and Response Objects•8 minutes
- Dynamic Routes•4 minutes
- Error Handing •6 minutes
3 readings•Total 6 minutes
- Lesson Overview•2 minutes
- Lesson Summary•2 minutes
- Lesson Overview•2 minutes
3 assignments•Total 60 minutes
- Graded Quiz•30 minutes
- Practice Quiz•12 minutes
- Checklist•18 minutes
3 app items•Total 155 minutes
- Hands-on Lab: Practice with Flask Part 1•20 minutes
- Hands-on Lab: Practice with Flask Part 2•45 minutes
- Hands-on Lab - Create Get Pictures Service with Flask•90 minutes
1 plugin•Total 10 minutes
- Project Tasks Overview•10 minutes
With the advent of Big Data and agile development methodologies, the database landscape has evolved with NoSQL databases gaining more importance and relevance. The main benefit of using NoSQL databases is the ability to effectively handle scalability and flexibility issues presented by the latest complex applications. In this second module, you will learn various flavors of NoSQL database products and practice using them in multiple hand-on labs throughout the course. You will start by learning the history and the basics of NoSQL databases and discover their key characteristics and benefits. Then you will learn about the four NoSQL databases and how they differ from each other. You will further learn how to work with MongoDB shell. You will then get hands-on experience using MongoDB basic commands, querying, and connecting from Flask. You will create the songs microservice using MongoDB and Flask.
What's included
6 videos3 readings3 assignments4 app items1 plugin
6 videos•Total 31 minutes
- Overview of NoSQL•5 minutes
- Characteristics of NoSQL Databases•7 minutes
- Document-Based NoSQL Databases•4 minutes
- Overview of MongoDB•5 minutes
- CRUD Operations•6 minutes
- Accessing MongoDB from Python•5 minutes
3 readings•Total 6 minutes
- Lesson Overview•2 minutes
- Lesson Overview•2 minutes
- Module 2 Summary & Highlights•2 minutes
3 assignments•Total 55 minutes
- Graded Quiz•30 minutes
- Practice Quiz•15 minutes
- Checklist•10 minutes
4 app items•Total 180 minutes
- Hands-On Lab: Getting Started with MongoDB•30 minutes
- Hands-On Lab: MongoDB CRUD•30 minutes
- Hands-On Lab: Accessing MongoDB from Python•30 minutes
- Hands-on Lab: Creating Get Songs Service with Flask•90 minutes
1 plugin•Total 30 minutes
- Connecting to MongoDB from Flask•30 minutes
In the third module, you will get hands-on practice creating a Django application and connecting it to services. You will be asked to create a data model and use the Django migration tool to create the tables and relationships. You will then create controllers to implement business logic to send the appropriate data to the provided templates.
What's included
1 reading2 assignments1 app item
1 reading•Total 2 minutes
- Lesson Overview•2 minutes
2 assignments•Total 40 minutes
- Graded Quiz•30 minutes
- Checklist•10 minutes
1 app item•Total 90 minutes
- Hands-on Lab: Create Band Website with Django•90 minutes
In Module 4, you will deploy your application and services using Code Engine, OpenShift, and Kubernetes. You will first learn about IBM Cloud code engine and how it helps in deployment of your applications. You will create a DockerFile for the Pictures and deploy it to IBM code engine. You will create a second DockerFile for the Songs microservice and deploy it to an OpenShift cluster. You will learn how to push the docker images for the microservices to IBM Container Registry. Finally, you will deploy the main application to IBM Kubernetes Service using YAML deployment files.
What's included
1 video2 readings3 assignments4 app items1 plugin
1 video•Total 7 minutes
- IBM Cloud Code Engine•7 minutes
2 readings•Total 4 minutes
- Lesson Overview•2 minutes
- Lesson Overview•2 minutes
3 assignments•Total 72 minutes
- Graded Quiz•30 minutes
- Practice Quiz•12 minutes
- Checklist•30 minutes
4 app items•Total 215 minutes
- Hands-on Lab: Deploying your first application on Code Engine•15 minutes
- Hands-on Lab: Deploying your first docker image on Code Engine•20 minutes
- Hands-on Lab: Deploying Microservices•90 minutes
- Hands-on Lab: Deploying Main Application•90 minutes
1 plugin•Total 30 minutes
- Using Code Engine•30 minutes
In the final module, you will submit your capstone project using one of two options: AI-Graded Submission or Peer-Graded Submission. Depending on your selected option, you may need to submit public GitHub repository URLs, terminal outputs saved in text files, and/or screenshots with exact file names. For the peer-graded option, you will also provide URLs for the deployed microservices and the Django application. After submitting your project, you will review and evaluate one of your peer’s submissions using the provided grading rubric.
What's included
2 readings1 peer review1 app item1 plugin
2 readings•Total 3 minutes
- Thanks from the Course Team•1 minute
- Congrats & Next Steps•2 minutes
1 peer review•Total 60 minutes
- Option 2: Peer-Graded Submission and Evaluation•60 minutes
1 app item•Total 30 minutes
- Option 1: AI-Graded Submission and Evaluation•30 minutes
1 plugin•Total 10 minutes
- Complete Documentation and Prepare Your Submission•10 minutes
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Reviewed on Mar 13, 2024
I directly applied the concepts and skills I learned from my courses to an exciting new project at work!
Frequently asked questions
This project requires you to engineer a decoupled, production-grade back-end system by integrating multiple independent services. You will build an agile Songs microservice utilizing Flask and a MongoDB NoSQL database to manage unstructured data catalogues with maximum flexibility. Simultaneously, you will build a separate service to process media from external API calls. These specialized microservices are then wired directly into a centralized Django application, where you will use Django migration tools to manage relational data models and orchestrate core business logic.
You will graduate from local runtime environments to production-ready cloud deployment. Throughout the course, you will write custom Dockerfiles to package your independent Flask microservices. You will then push these container images to the IBM Container Registry and deploy them using diverse cloud native strategies: launching serverless workloads on IBM Code Engine, orchestrating clusters via Red Hat OpenShift, and deploying the primary Django application framework to a live Kubernetes Service using structured YAML deployment files.
A strong portfolio piece requires verifiable deployment and clean code architecture. For your final submission—which can be evaluated via AI grading—you will provide your public GitHub repository URLs, terminal command outputs, and active microservice endpoints. Because you are tasked with configuring real routing, custom error handling, and cross-service communication, this capstone delivers a functional, multi-container portfolio showpiece that proves you can build and scale modern cloud-native backend applications.
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