Live Online Apache Cassandra Course for Data Science

Apache Cassandra for Data Science – Live Course Module

Total Duration: 20 Hours (3 Weeks)

Week 1: Foundations of Cassandra

Goal: Understand Cassandra basics, architecture, and data modeling.

1. Session 1 – Introduction & Use Cases (2 hrs)

   • What is Apache Cassandra & why it matters in Data Science

   • Key features (scalability, fault tolerance, high availability)

   • Real-world case studies (IoT, recommendation systems, financial fraud detection)

   • Hands-on: Setting up Cassandra locally or on the cloud

2. Session 2 – Cassandra Architecture (2 hrs)

   • Peer-to-peer architecture

   • Partitioning, replication, and consistency

   • Write/read path explained

   • Lab: Inspecting system tables & cluster metadata

3. Session 3 – Data Modeling Basics (2 hrs)

   • Query-driven modeling approach

   • Primary keys, clustering columns, partitioning

   • Hands-on: Designing schema for an e-commerce dataset

Week 2: Working with Data

Goal: Learn CQL, CRUD operations, and connecting Cassandra with Python.

4. Session 4 – Cassandra Query Language (CQL) (2 hrs)

   • Basics of CQL syntax

   • CRUD operations (Insert, Select, Update, Delete)

   • Filtering, indexing, and query limitations

   • Lab: Running sample queries on datasets

5. Session 5 – Advanced Data Modeling (2 hrs)

   • Denormalization in Cassandra

   • Time-series data modeling

   • Best practices for analytical workloads

   • Lab: Designing schema for clickstream data

6. Session 6 – Python Integration (2 hrs)

   • Connecting Cassandra with Python (cassandra-driver)

   • Using Cassandra data with Pandas

   • Simple EDA (Exploratory Data Analysis) on Cassandra datasets

   • Hands-on: Analyzing customer transactions

Week 3: Analytics, Scaling & Projects

Goal: Apply Cassandra in real-world data science projects.

7. Session 7 – Cassandra + Spark for Data Science (2 hrs)

   • Introduction to Spark-Cassandra connector

   • Running distributed queries

   • Performing ML tasks using Spark + Cassandra

   • Lab: Simple clustering/classification use case

8. Session 8 – Performance & Optimization (2 hrs)

   • Read/write optimization strategies

   • Compaction & caching

   • Scaling Cassandra clusters

   • Lab: Tuning queries & benchmarking

9. Session 9 – Capstone Project (2 hrs)

   • Use Case: Building a real-time analytics pipeline

   • Store clickstream/user logs in Cassandra

   • Query + analyze with Python/Spark

   • Visualization & insights presentation

10. Session 10 – Wrap-Up & Q&A (1.5 hrs)

    • Recap of all concepts

    • Best practices for Cassandra in data science

    • Next steps & advanced resources