Real-Time Streaming Analytics Platform

A production-ready, end-to-end real-time streaming analytics platform running on Kubernetes with Kafka, Spark, Trino, and Apache Iceberg.

Architecture Overview

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Data Sources  │───▶│   Kafka Cluster │───▶│  Spark Streaming│
│   (Producers)   │    │   (Strimzi)     │    │   (K8s Jobs)    │
└─────────────────┘    └─────────────────┘    └─────────────────┘
                                                               │
┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Trino Query   │◀───│  Apache Iceberg  │◀───│   Spark Output  │
│   Engine        │    │   (S3/MinIO)     │    │   (Spark)       │
└─────────────────┘    └─────────────────┘    └─────────────────┘

Quick Start

Prerequisites

• Google Cloud Platform account
• kubectl configured
• Helm 3.x installed
• Terraform installed

Deployment Steps

1. Deploy Infrastructure:

   cd terraform
   cp terraform.tfvars.example terraform.tfvars
   # Edit terraform.tfvars with your project_id
   terraform init && terraform apply

2. Deploy Kafka:

   cd ../helm
   helm install strimzi strimzi/strimzi-kafka-operator --namespace kafka --create-namespace
   helm install kafka ./kafka --namespace kafka
   kubectl apply -f kafka-topics.yaml

3. Deploy Producer:

   cd ../kafka-producer
   docker build -t streaming-producer .
   kubectl apply -f k8s/

4. Deploy Spark:

   cd ../spark-jobs
   docker build -t streaming-spark .
   kubectl apply -f spark-application.yaml

5. Deploy Trino:

   cd ../trino
   helm install trino ./trino --namespace trino --create-namespace

6. Deploy Monitoring:

   cd ../observability
   kubectl apply -f prometheus/
   kubectl apply -f grafana/
   kubectl apply -f alerts/

Components

• Terraform: Infrastructure as Code for GKE cluster
• Kafka: Distributed streaming platform with Strimzi operator
• Spark: Real-time stream processing with PySpark
• Trino: Interactive SQL query engine
• Iceberg: Table format for data lake
• Prometheus: Metrics collection and monitoring
• Grafana: Visualization and dashboards

Performance

• Throughput: 100k+ events/second
• Latency: < 30 seconds end-to-end
• Scalability: Auto-scaling based on load
• Reliability: Fault-tolerant with checkpointing

License

MIT License