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Apache Cassandra Mastery

Course Level: Intermediate to Advanced Prerequisites: Basic understanding of databases, distributed systems helpful but not required Time Commitment: 30-40 hours for complete mastery What You’ll Build: Production-grade knowledge to design, operate, and optimize Cassandra clusters

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What is Apache Cassandra?

Apache Cassandra is a highly scalable, distributed NoSQL database designed to handle massive amounts of data across commodity servers while providing high availability with no single point of failure. Originally created at Facebook in 2008 to power their Inbox Search feature, Cassandra combines the best of two legendary distributed systems:
  • Amazon Dynamo’s distribution design and replication model
  • Google Bigtable’s data model (column-family storage)
Unlike many NoSQL databases that sacrifice consistency for availability, Cassandra gives you tunable consistency - you choose the right balance for each query.

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Why Learn Cassandra?

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Real-World Impact

Cassandra powers some of the world’s most demanding applications:

Netflix

Serves 100+ million subscribers globally. Uses Cassandra for user viewing history, recommendations, and personalization at massive scale.

Apple

Runs one of the largest Cassandra deployments (75,000+ nodes) for iCloud and other services.

Discord

Stores trillions of messages. Handles 5+ billion message reads daily with Cassandra.

Uber

Uses Cassandra for real-time location data, trip data, and analytics across their global platform.

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When to Choose Cassandra

Cassandra excels when you need: ✅ Linear scalability - Add nodes to increase throughput proportionally ✅ High availability - No single point of failure, multi-datacenter replication ✅ Write-heavy workloads - Optimized write path handles millions of writes/sec ✅ Time-series data - Perfect for IoT, logs, events, metrics ✅ Geographical distribution - Built-in multi-datacenter awareness ✅ Predictable performance - Consistent low-latency reads and writes at scale ❌ Avoid Cassandra when you need:
  • Complex JOINs and relational queries
  • Strong ACID transactions across multiple rows
  • Ad-hoc queries (Cassandra requires query-driven data modeling)
  • Small datasets that fit on a single machine

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What Makes This Course Different?

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1. Paper-First Approach

We start with the seminal Cassandra paper written by its creators at Facebook. Understanding the theoretical foundations gives you:
  • Deep intuition for why design decisions were made
  • Ability to predict behavior in production scenarios
  • Interview advantage - explain trade-offs, not just features
  • Architectural thinking to apply to other distributed systems

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2. Practical, Production-Focused

Every concept is tied to real-world scenarios:
  • Design data models for actual use cases (messaging, IoT, recommendations)
  • Tune Cassandra for production workloads
  • Debug common issues (compaction storms, hot partitions, repair problems)
  • Operate multi-datacenter clusters

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3. Hands-On Labs

You’ll build real systems:
  • Set up local and cloud Cassandra clusters
  • Implement time-series, event-sourcing, and messaging systems
  • Perform rolling upgrades and disaster recovery
  • Optimize queries using tracing and metrics

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Course Structure

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Foundation Track

  • The Cassandra paper (Facebook, 2009) explained in plain language
  • Dynamo and Bigtable influence
  • Core architecture: ring topology, consistent hashing, gossip protocol
  • Replication strategies and tunable consistency
  • Lab: Understand trade-offs through thought experiments
  • Query-driven data modeling philosophy
  • Primary keys, partition keys, clustering columns
  • Denormalization patterns
  • CQL (Cassandra Query Language) mastery
  • Lab: Model a Twitter-like messaging system
  • Write path: CommitLog, MemTable, SSTables
  • Read path: Bloom filters, partition index, data files
  • Compaction strategies (STCS, LCS, TWCS)
  • Lab: Trace queries and optimize performance

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Intermediate Track

  • Gossip protocol for failure detection
  • Hinted handoff and read repair
  • Anti-entropy repair operations
  • Monitoring with nodetool and metrics
  • Lab: Set up a 3-node cluster and simulate failures
  • Tunable consistency levels (ONE, QUORUM, ALL)
  • Replication factors and strategies
  • Multi-datacenter replication
  • Lightweight transactions (Paxos-based)
  • Lab: Configure multi-DC replication and test failover
  • JVM tuning for Cassandra
  • Choosing the right compaction strategy
  • Partition sizing and tombstones
  • Read/write optimization techniques
  • Lab: Optimize a slow production-like workload

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Advanced Track

  • Time-series data patterns
  • Event sourcing and CQRS
  • Materialized views and secondary indexes
  • Counters and collections
  • Lab: Build an IoT time-series system
  • Backup and restore strategies
  • Rolling upgrades and cluster maintenance
  • Capacity planning and scaling
  • Security: authentication, authorization, encryption
  • Lab: Perform zero-downtime cluster upgrade
  • Common production issues (hot partitions, repair storms)
  • Using tracing, logs, and metrics
  • Resolving data inconsistencies
  • Recovery from disasters
  • Lab: Debug and fix realistic production scenarios
  • Design and implement a complete system
  • Multi-datacenter deployment
  • Performance testing and optimization
  • Disaster recovery simulation

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Learning Path

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Beginner Track (20-25 hours)

Modules 1-3 + Selected labs Outcome: Understand Cassandra fundamentals, model basic schemas, run simple clusters

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Intermediate Track (30-35 hours)

Modules 1-6 + All labs Outcome: Design production schemas, operate clusters, tune performance

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Advanced Track (40-50 hours)

Complete course + Capstone Outcome: Architect and operate large-scale, multi-datacenter Cassandra deployments

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Prerequisites

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Required

  • Basic SQL knowledge (helpful for CQL comparison)
  • Understanding of basic data structures (hash tables, trees)
  • Comfort with command-line tools

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Helpful (But We’ll Teach You)

  • Distributed systems concepts
  • NoSQL database experience
  • Java/JVM basics
  • Linux system administration

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Tools & Setup

You’ll work with:
  • Apache Cassandra (latest stable version)
  • Docker for local clusters
  • cqlsh (Cassandra Query Language Shell)
  • nodetool for cluster management
  • Monitoring tools: Prometheus, Grafana
  • Optional: CCM (Cassandra Cluster Manager) for multi-node local testing
All tools are open source and free. Setup instructions provided in Module 1.

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Interview Preparation

This course prepares you for:
  • Database Engineer roles requiring Cassandra expertise
  • Distributed Systems design interviews
  • Site Reliability Engineer positions managing Cassandra
  • Data Architect roles designing scalable systems
Common interview topics covered:
  • Cassandra vs other NoSQL databases (MongoDB, DynamoDB)
  • Data modeling trade-offs
  • Consistency models and CAP theorem
  • Scaling strategies
  • Production incident resolution

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What You’ll Build

By the end of this course, you’ll have implemented:
  1. Messaging System (like Discord/WhatsApp)
    • User timelines, chat history
    • Multi-datacenter replication
    • Billions of messages at scale
  2. IoT Time-Series Platform
    • Sensor data ingestion
    • Time-window aggregations
    • Efficient compaction for time-series
  3. User Activity Tracking (like Netflix)
    • View history
    • Recommendations data
    • High-throughput writes
  4. Distributed Counter System
    • Real-time analytics
    • Handling counter conflicts
    • Materialized views

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Who Created Cassandra?

Understanding the creators gives context to design decisions: Original Authors (Facebook, 2008):
  • Avinash Lakshman - Previously worked on Amazon Dynamo
  • Prashant Malik - Facebook engineer
Why They Built It: Facebook needed to power Inbox Search - searching across billions of messages for hundreds of millions of users. Existing solutions couldn’t:
  • Handle write-heavy workloads at Facebook’s scale
  • Provide predictable performance during peak traffic
  • Replicate data across multiple datacenters
  • Scale linearly by adding commodity hardware
Cassandra was their answer - combining Dynamo’s availability and Bigtable’s data model.
The Cassandra paper was published in 2009 at Facebook but was open-sourced and became an Apache incubator project in 2009, graduating to top-level project in 2010. It’s now maintained by a vibrant open-source community.

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Course Philosophy

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Learn by Understanding “Why”

We don’t just teach commands and syntax. Every concept is explained from first principles:
  • Why does Cassandra use a ring topology instead of master-slave?
  • Why are writes faster than reads in Cassandra?
  • Why can’t you do JOINs efficiently?

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Production-First Mindset

Concepts are immediately connected to real-world scenarios:
  • How Netflix uses Cassandra for 200M+ users
  • Why Discord chose Cassandra over MongoDB
  • How Uber handles billions of location updates

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Hands-On Learning

Theory is useless without practice. Every module includes:
  • Practical labs with real Cassandra clusters
  • Production scenario simulations
  • Performance tuning exercises
  • Troubleshooting challenges

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Getting Started

Ready to master Cassandra? Let’s begin with the foundational paper that started it all.

Module 1: The Cassandra Paper & Core Architecture

Understand the theoretical foundations through the seminal Facebook paper, explained in an accessible way
Time Estimate: Module 1 takes 3-4 hours. Take your time - this foundation is crucial for everything that follows.

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Community & Resources

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Official Resources

  • Cassandra: The Definitive Guide by Jeff Carpenter & Eben Hewitt
  • Mastering Apache Cassandra by Nishant Neeraj

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Community

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Let’s Build Something Amazing

Cassandra powers some of the most impactful systems in the world. By mastering it, you’ll gain skills that are:
  • In-demand: Companies desperately need Cassandra experts
  • Future-proof: Distributed systems thinking applies everywhere
  • Impactful: Build systems that serve millions of users
Let’s get started.

Start Learning: Module 1

Begin with the Cassandra paper and core architecture