Database Management with AI Integration

Overview

The Database Management with AI Integration course prepares learners to design, automate, secure, and optimise modern cloud-based data systems in AI-driven environments.

Students will explore how relational and non-relational databases power analytics, machine learning, and distributed applications at scale. Through hands-on labs and system design exercises, learners will build real-world infrastructure using tools such as PostgreSQL, MongoDB Atlas, Snowflake, Apache Airflow, Docker, and Apache Spark.

By the end of the course, students will be able to architect scalable database systems, automate data pipelines, integrate AI workflows, and deploy secure, production-ready data environments.

Outcomes

By completing this course, learners will be able to:

Prerequisites

There are no formal prerequisites. However, learners are expected to have:

System Requirements

Supported Operating Systems

Minimum Hardware

Required Software & Accounts

Security Best Practices

Students must:

Module Repos

Each module contains:

Module Associated Lab Tool(s) Used
Module 1: Introduction to Modern Database Systems Exploring Modern Database Architectures PostgreSQL, SQL
Module 2: Database Design and Normalisation Designing and Normalising Relational Databases PostgreSQL, SQL
Module 3: Querying and Managing Relational Data with SQL Querying and Managing Relational Data PostgreSQL
Module 4: Scaling Databases for Performance and Reliability Scaling and Optimising SQL Databases PostgreSQL
Module 5: Introduction to NoSQL and Non-Relational Databases Working with NoSQL in MongoDB Atlas MongoDB Atlas
Module 6: Working with Document and Key-Value Stores Indexing and Querying Document Databases MongoDB Atlas
Module 7: Cloud Databases and Data Warehouses Querying Cloud Data Warehouses BigQuery / Snowflake
Module 8: Implementing Snowflake and BigQuery Solutions Optimising Warehouse Performance BigQuery / Snowflake
Module 9: Data Pipelines and ETL Concepts Building Data Pipelines with Airflow Apache Airflow
Module 10: Automating Data Pipelines with Airflow and Fivetran Automating ETL Processes Apache Airflow
Module 11: Data Storage and Containerization with Docker and Kubernetes Containerising Databases Docker
Module 12: Monitoring, Logging, and Security in Database Systems Deploying Monitored Data Pipelines Docker, Logging Tools
Module 13: Introduction to Distributed Computing and Spark Processing Data with Apache Spark Apache Spark
Module 14: AI and Machine Learning in Data Pipelines Building Scalable Analytics in Spark Spark MLlib
Module 15: Optimizing and Maintaining Modern Data Architectures Integrating Tools for Unified Data Systems Docker, Airflow, Databases
Module 16: Capstone: Building and Deploying an End-to-End Data System Capstone System Design Review All Core Tools
Capstone Project End-to-End Data System Deployment PostgreSQL, MongoDB, Airflow, Docker, Spark

Course Architecture

flowchart LR
    A[Relational Databases] --> B[NoSQL Systems]
    B --> C[Cloud Warehouses]
    C --> D[Automated Pipelines]
    D --> E[AI Integration]
    E --> F[Security & Optimization]
    F --> G[Capstone Deployment]