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Orchestrating a Multi-Engine Big Data Pipeline on AWS: Integrating Kafka, Spark, and Cassandra using Step Functions

Big Data Pipeline

Big Data Pipeline on AWS: Integrating Kafka, Spark, and Cassandra using Step Functions

  • Use Case: Modern enterprises generate real-time, high-velocity streaming data from multiple sources (IoT devices, financial transactions, user activity logs, etc.).The challenge is to ingest, process, and persist this data across heterogeneous engines while maintaining scalability, fault-tolerance, and low latency.This project aims to integrate Kafka (for ingestion), Spark (for processing), and Cassandra (for storage) into a fully automated AWS pipeline.

Objective

  • To design and benchmark a multi-engine big data pipeline orchestrated on AWS.

    To compare performance, fault tolerance, and cost-efficiency of running Kafka → Spark → Cassandra workflow.

    To demonstrate Step Functions orchestration for multi-stage processing pipelines.

    To provide research insights into latency, throughput, and recovery time under workload spikes.

Project Description

  • Data Ingestion Layer (Kafka on AWS MSK):

    High-throughput streaming ingestion using Amazon MSK (Managed Kafka).

    Producers simulate IoT or log data streaming at variable rates.

Data Processing Layer (Apache Spark on Amazon EMR)

  • Real-time stream consumption from Kafka topics.

    Spark Structured Streaming jobs perform ETL, anomaly detection, and aggregations.

    Auto-scaling clusters handle fluctuating workloads.

    Data Storage Layer (Cassandra on Amazon Keyspaces):

    Processed data stored in serverless Cassandra tables.

    Optimized for fast read/write queries and time-series workloads.

Pipeline Orchestration (AWS Step Functions)

  • Step Functions manage Kafka ingestion, Spark jobs, and Cassandra persistence as workflow states.
  • Retry policies handle failures gracefully.

Monitoring & Benchmarking

  • Amazon CloudWatch for pipeline latency and throughput monitoring.
  • Amazon S3 + Athena for querying Spark job logs and Kafka offsets.
  • Amazon QuickSight for visualization of latency/throughput trade-offs.
  • AWS Services & Technologies :
    Service/Technology Role
    Amazon MSK (Managed Kafka) Streaming ingestion of real-time events.
    Amazon EMR (Apache Spark) Stream processing & ETL with Spark Structured Streaming.
    Amazon Keyspaces (Cassandra) Persistent storage for processed data.
    AWS Step Functions Orchestration of Kafka → Spark → Cassandra pipeline.
    Amazon CloudWatch Monitoring throughput, latency, and job health.
    Amazon S3 Storage of Spark job checkpoints & logs.
    Amazon Athena Query benchmarking results and logs in S3.
    Amazon QuickSight Visualization of performance and cost metrics.