Module 1
Foundations
Lectures 1 – 6
LECTURE 01Past
Introduction to Streaming Data
What streaming is, why it matters, batch vs stream
LECTURE 02Past
Data Sources & Personalisation
Identifying sources, real-time personalisation patterns
LECTURE 03Past
Kafka Architecture
Topics, partitions, brokers — how Kafka works
LECTURE 04Past
Setup: Docker & Kafka
Installing Docker, Kafka, Zookeeper environment
LECTURE 05Past
Setup: Docker & Kafka II
Configuration, troubleshooting, verifying the stack
LECTURE 06Available
Your First Producer
Python venv, stock CSV, yFinance, BTC live, multi-producer
Module 2
Making Kafka Work
Lectures 7 – 10
LECTURE 07Available
Reading from Kafka
Console consumer, Python consumer, analytics & decision framework
LECTURE 08Available
Saving to MongoDB
MongoDB Atlas setup, consumer_mongodb.py, persistent storage
LECTURE 09Available
Analytics & Alerts
Decision framework, offset strategy, analytics consumer, alert triggers
LECTURE 10Available
Fraud Detection Capstone
End-to-end: multi-producer → Kafka → MongoDB enrichment → 3 fraud rules
Module 3
Stream Processing
Lectures 11 – 13
Module 4
Grafana Dashboards & Alerts
Lectures 14 – 16
LECTURE 14Notes
Grafana Fundamentals
Install Grafana, connect MongoDB Atlas, build your first live panel
LECTURE 15Notes
Building Live Dashboards
Multi-panel stock monitor, variables, Assignment 3 guidance
LECTURE 16Notes
Alerts in Grafana
Alert rules, contact points, Slack routing & design strategy
Module 5
MongoDB Atlas Charts
Lectures 17 – 18
LECTURE 17Soon
Atlas Charts — First Dashboard
Connect sda_course data, chart types, build an analysis dashboard
LECTURE 18Soon
Atlas Charts — Advanced Analysis
Aggregation pipelines as chart source, scheduled refresh, sharing & embedding
Module 6
Capstone Presentations
Lectures 19 – 20
LECTURE 19Soon
Capstone — Part 1
Teams present streaming pipelines — data sources, architecture, live demo
LECTURE 20Soon
Capstone — Part 2
Remaining team presentations, peer review, course wrap-up