Big Data Analysis with MongoDB & PySpark — Tutorial Series

Course Overview

This lecture series bridges relational (SQL) and document (MongoDB/NoSQL) thinking, showing how evolving, nested application data maps from tables and relationships to flexible documents. It covers document models, terminology mapping, and when MongoDB is the right fit for real-world use cases, then scales the same analysis up to distributed computing with MapReduce and PySpark.

Venues

The series was delivered to students of two institutes, University of Dhaka:

• Institute of Information Technology (IIT)
• Institute of Applied Statistics and Data Science (IASDS)

Tutorial Tracks

The hands-on material is published as two tracks:

• Big Data Analysis with MongoDB — Tutorials 1–4: environment setup, MongoDB fundamentals, advanced real-world analysis, and sharding.
• Big Data Analysis with PySpark — Tutorials 5–6: distributed computing concepts and a hands-on PySpark implementation.

Prerequisites

• Basic SQL and data modeling; no prior MongoDB required.
• For the PySpark track: basic Python familiarity (loops, functions) and roughly 1 GB of free disk for Java and PySpark. No MongoDB or Docker needed — the PySpark track is independent of the MongoDB series.

Schedule

Topic	Materials
Lecture 1 — MongoDB Fundamentals & NoSQL Overview SQL foundations, where relational modeling becomes difficult, same data as tables vs documents, what is NoSQL, why MongoDB.	Slides (Google Slides) Hands-on Lab — Tutorial 1 (Environment Setup)
Lecture 2 — Data Modeling & Queries SQL vs MongoDB terminology map, embedding vs referencing, when MongoDB fits (e-commerce, IoT, CMS, user profiles).	Slides (Google Slides) Hands-on Lab — Tutorial 2 (MongoDB Fundamentals)
Lecture 3 — Advanced MongoDB & Big Data Analysis Document model (Database → Collections → Documents), indexing, sharding, horizontal scaling and scalable analytics.	Slides (Google Slides) Hands-on Lab — Tutorial 3 (Advanced MongoDB with Real-World Data)
Lecture 4 — Sharding & Distributed MongoDB Building a real sharded cluster in Docker — config servers, shards and a mongos router, shard key selection and rebalancing on the Olist orders collection.	Slides (Google Slides) Hands-on Lab — Tutorial 4 (Sharding & Distributed MongoDB)
Lecture 5 — MapReduce & Distributed Computing Concepts What MapReduce solves and why, the map/shuffle/reduce model, how Hadoop implements it, why Spark replaced raw Hadoop, and the core PySpark vocabulary.	Slides (Google Slides) Hands-on Lab — Tutorial 5 (MapReduce & Distributed Computing Concepts)
Lecture 6 — PySpark Implementation Installing Java and PySpark, then building four programs on the Olist dataset — Word Count, a key–value RDD job, a DataFrame join and a trained MLlib regression pipeline.	Hands-on Lab — Tutorial 6 (PySpark Implementation: Step by Step)