Cassandra for Developers

Equip developers to design schemas, write CQL, and build high‑performance applications on Apache Cassandra clusters.

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Audience: Developers / Architects / Database admins

Duration: 3 days

Format: Lectures and hands‑on labs (50% lecture, 50% lab)

Overview

Modern, large-scale applications involve dealing with Big Data, which is often larger than what traditional databases (RDBMS) can handle. The Cassandra (C*) is a massively scalable NoSQL database that provides high availability and fault tolerance. This hands-on course will introduce Cassandra, concepts, data modeling, and CQL (Cassandra Query Language). The focus is practical aspects of working with C* effectively. We will also cover “anti-patterns” and best practices that will lead to optimal C* implementations in high-performance production systems.

Objective

Equip developers to design schemas, write CQL, and build high‑performance applications on Apache Cassandra clusters.

What You Will Learn

NoSQL concepts
Cassandra’s concepts and architecture
Setting up and running C*
Setting up C* and your IDE
CQL (Cassandra Query Language)
Data modeling in CQL
Using APIs to interact with Cassandra
Understand C* internals (read/write path)
Deletion and compaction
C* administration
C* case studies
C* data modeling
C* workshop (time permitting)

Course Details

Audience: Developers / Architects / Database admins

Duration: 3 days

Format: Lectures and hands‑on labs (50% lecture, 50% lab)

Prerequisites:

Comfortable with Java programming language • Comfortable in Linux environment (navigating command line, running commands)

Setup: Cloud-based lab environment • Modern laptop with unrestricted Internet • Chrome browser • SSH client

Detailed Outline

Big Data challenges vs RDBMS
NoSQL overview
CAP theorem
When is NoSQL appropriate
Columnar storage
NoSQL ecosystem

C* architecture overview
C* clusters, rings, nodes
Keyspaces, tables, rows and columns
Partitioning, replication, tokens
Quorum and consistency levels
Labs: installing Cassandra, interacting with Cassandra using CQLSH

Introduction to CQL
CQL Datatypes
Creating keyspaces and tables
Choosing columns and types
Choosing primary keys
Data layout for rows and columns
Time to live (TTL)
Querying with CQL
CQL updates
Collections (list, map, and set)
Labs: various data-modeling exercises using CQL; experimenting with queries and supported data types

Creating and using secondary indexes
Composite keys (partition keys and clustering keys)
Time series data
Best practices for time-series data
Counters
Lightweight transactions (LWT)
Labs: creating and using indexes; modeling time-series data

Introduction to Java driver
CRUD operations using Java client
Asynchronous queries
Labs: using Java API for Cassandra

Partitioners, gossip protocols, snitches
sstables, memtables, commit log
Read path, write path
Deletions, compactions, tombstones
Failure handling
Caching

Hardware selection
Software dependencies
Cassandra distributions
Lab: students install Cassandra, run benchmarks

Performance tuning
Troubleshooting tools and tips
“Anti-patterns” – how NOT to use C*

Real-world C* use cases, system architecture, best practices, and recommendations

Attendees work in teams on multiple domain use cases
Design and model discussion, decision analysis

Implement a real-world use case using C*
Team projects, testing, presentations
Sample ideas: Slack-like messaging, music service, stock-quotes tracker