Streaming Systems with Kafka + Spark + Cassandra

Teach students to architect and build low‑latency, end‑to‑end data pipelines by integrating Kafka for ingest, Spark for processing, and Cassandra for scalable storage.

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Audience: Developers, Architects

Duration: 5 days

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

Overview

Build end‑to‑end streaming pipelines using the fast‑data stack: Apache Kafka, Apache Spark, and Apache Cassandra.

Objective

Teach students to architect and build low‑latency, end‑to‑end data pipelines by integrating Kafka for ingest, Spark for processing, and Cassandra for scalable storage.

What You Will Learn

Kafka fundamentals (1 day)
Cassandra for scalable storage (1.5 days)
Spark for analytics & streaming (1.5 days)
Putting it all together (1 day)
Lambda architecture & best practices

Course Details

Audience: Developers, Architects

Duration: 5 days

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

Prerequisites:

Familiarity with Java or Scala • Basic Linux command‑line skills

Setup: Complete cloud lab environment • Zero‑install (no local software required)

Detailed Outline

Kafka design & architecture
Launching a Kafka cluster
Using Kafka utilities
Reading & writing with Kafka Java API
Labs: all Kafka sections

Cassandra design & architecture
CQLSH basics
Read/write path & eventual consistency
Time‑series data modeling
Using Cassandra Java API
Labs: all Cassandra sections

Spark architecture
Spark Shell
RDDs, DataFrames, Datasets
Batch analytics & Spark Streaming
Labs: all Spark sections

Read Kafka streams from Spark
Persist streaming data into Cassandra
End‑to‑end pipeline
Benchmarking, monitoring, tuning
Labs: full pipeline

Discuss student use cases
Design choices & best practices
Group activity