Real-Time E-commerce Event Streaming & Analytics Platform

Build a production-grade real-time data engineering platform that processes millions of e-commerce events per second, implements advanced stream processing, and provides real-time business intelligence. This enterprise-level project demonstrates expertise in modern data engineering practices used by companies like Amazon, Uber, and Netflix.

Business Context

E-commerce platforms generate massive amounts of real-time data: user clicks, purchases, inventory changes, fraud signals, and personalization events. This project builds the infrastructure that powers real-time recommendations, fraud detection, and business analytics.

Real-World Impact:

• Powers real-time product recommendations (like Amazon's "People who bought this also bought")
• Enables instant fraud detection and prevention
• Provides real-time business metrics for decision-making
• Supports dynamic pricing and inventory management

Technology Stack

Core Technologies

• Apache Kafka (3.6+) - Event streaming platform
• Apache Flink (1.18+) - Stream processing engine
• Apache Airflow (2.7+) - Workflow orchestration
• ClickHouse (23.8+) - Real-time analytics database
• Redis Cluster (7.2+) - Real-time caching and session store
• Kubernetes (1.28+) - Container orchestration
• Python (3.11+) - Primary development language
• Apache Iceberg (1.4+) - Data lakehouse table format

Cloud Infrastructure

• Google Cloud Platform or AWS
• Google Cloud Storage / S3 - Data lake storage
• BigQuery / Redshift - Data warehouse
• Cloud Monitoring - Observability
• Terraform - Infrastructure as Code

Monitoring & DevOps

• Prometheus + Grafana - Metrics and monitoring
• Jaeger - Distributed tracing
• ELK Stack - Logging and search
• ArgoCD - GitOps deployment

Architecture Design

High-Level Architecture

```

User Events → API Gateway → Kafka → Flink → ClickHouse → Business Intelligence

↓

Data Lake (Iceberg) → BigQuery → ML Models

```

Microservices Components

1. Event Ingestion Service - High-throughput event collection

2. Stream Processing Engine - Real-time data transformation

3. Real-time Analytics API - Low-latency query service

4. Data Quality Monitor - Automated data validation

5. ML Feature Store - Real-time feature serving

6. Business Intelligence Dashboard - Executive reporting

Key Features

1. High-Throughput Event Ingestion

Technical Implementation:

• Multi-tenant Kafka cluster with 50+ partitions per topic
• Schema registry with Avro serialization
• Exactly-once delivery semantics
• Auto-scaling based on throughput metrics

Business Value:

• Handles 10M+ events per second during peak traffic
• Zero data loss guarantee for critical business events
• Supports multiple data formats and sources

2. Advanced Stream Processing with Flink

Technical Implementation:

• Stateful stream processing with checkpointing
• Windowed aggregations for real-time metrics
• CEP (Complex Event Processing) for fraud detection
• Watermarks for handling late-arriving data

Business Value:

• Real-time fraud detection with <100ms latency
• Live business KPIs updated every second
• Personalized recommendations based on current session

3. Real-Time Analytics with ClickHouse

Technical Implementation:

• Columnar storage optimized for analytical queries
• Materialized views for pre-computed aggregations
• Distributed cluster setup with replication
• Real-time data ingestion from Kafka

Business Value:

• Sub-second query response times on billions of records
• Real-time dashboards for business stakeholders
• Ad-hoc analytics capabilities for data scientists

4. Data Lakehouse with Apache Iceberg

Technical Implementation:

• ACID transactions on data lake storage
• Time travel and schema evolution capabilities
• Partition pruning and Z-ordering for performance
• Integration with Spark and Flink for processing

Business Value:

• Single source of truth for all historical data
• Support for both batch and streaming analytics
• Cost-effective storage with high performance

5. Real-Time ML Feature Store

Technical Implementation:

• Low-latency feature serving with Redis
• Feature pipeline orchestration with Airflow
• Feature versioning and lineage tracking
• A/B testing framework for feature experiments

Business Value:

• Enables real-time ML model predictions
• Consistent feature definitions across teams
• Reduced time-to-market for ML features

Development Roadmap

Phase 1: Foundation (Weeks 1-2)

Infrastructure Setup:

• Set up Kubernetes cluster with Helm charts
• Deploy Kafka cluster with monitoring
• Configure schema registry and basic topics
• Set up development and staging environments

Deliverables:

• Working Kafka cluster processing sample events
• Basic monitoring and alerting setup
• CI/CD pipeline with automated testing

Phase 2: Stream Processing (Weeks 3-4)

Stream Processing Implementation:

• Develop Flink jobs for data transformation
• Implement real-time aggregations and windowing
• Build fraud detection and anomaly detection
• Set up checkpointing and state management

Deliverables:

• Real-time metrics dashboard showing key KPIs
• Fraud detection system with alerting
• Data quality monitoring and validation

Phase 3: Analytics and Storage (Weeks 5-6)

Analytics Platform:

• Deploy ClickHouse cluster with replication
• Implement data lakehouse with Apache Iceberg
• Build real-time analytics API
• Create business intelligence dashboards

Deliverables:

• Sub-second analytics queries on billions of records
• Historical data analysis capabilities
• Executive dashboards with real-time metrics

Phase 4: ML Integration (Weeks 7-8)

Machine Learning Pipeline:

• Build feature store with real-time serving
• Implement recommendation engine
• Deploy ML models for real-time scoring
• Set up A/B testing framework

Deliverables:

• Real-time product recommendations
• ML-powered business insights
• A/B testing results and optimization

Technical Challenges & Solutions

Challenge 1: Handling Peak Traffic Loads

Problem: E-commerce traffic can spike 10x during sales events

Solution:

• Auto-scaling Kafka partitions and Flink task slots
• Circuit breakers and backpressure handling
• Tiered storage with hot/warm/cold data classification

Challenge 2: Ensuring Data Quality at Scale

Problem: Bad data can corrupt analytics and ML models

Solution:

• Real-time schema validation with Great Expectations
• Automated data quality monitoring with alerting
• Data lineage tracking and impact analysis

Challenge 3: Low-Latency Analytics

Problem: Business users need sub-second query responses

Solution:

• Pre-computed materialized views in ClickHouse
• Intelligent caching strategies with Redis
• Query optimization and indexing strategies

Production Considerations

Scalability

• Horizontal scaling: All components designed for horizontal scaling
• Load testing: Regular load testing with realistic traffic patterns
• Capacity planning: Automated resource allocation based on traffic

Reliability

• Fault tolerance: Multi-region deployment with automatic failover
• Data replication: 3x replication for critical data
• Disaster recovery: Automated backup and recovery procedures

Security

• Encryption: End-to-end encryption for data in transit and at rest
• Access control: RBAC with fine-grained permissions
• Compliance: GDPR and PCI DSS compliance implementation

Cost Optimization

• Resource efficiency: Right-sizing based on actual usage patterns
• Data lifecycle: Automated archiving of historical data
• Cloud optimization: Spot instances and reserved capacity

Performance Metrics

System Performance

• Throughput: 10M+ events/second during peak
• Latency: <100ms end-to-end processing time
• Availability: 99.99% uptime SLA
• Recovery time: <5 minutes for service restoration

Business Metrics

• Data freshness: Real-time metrics updated every second
• Query performance: 95th percentile <200ms for analytics
• Cost efficiency: 40% cost reduction vs. traditional solutions

Career Impact

Skills Demonstrated

1. Advanced Stream Processing: Flink, Kafka, real-time systems

2. Cloud-Native Architecture: Kubernetes, microservices, auto-scaling

3. Big Data Technologies: Data lakes, columnar databases, distributed systems

4. DevOps Excellence: Infrastructure as Code, monitoring, CI/CD

5. Business Acumen: Understanding of e-commerce metrics and KPIs

Resume Value

• Enterprise-scale experience with billions of events processed
• Production deployment experience with monitoring and alerting
• Cost optimization skills with measurable business impact
• Cross-functional collaboration with ML, product, and business teams

Interview Talking Points

1. System Design: How you designed for 10M+ events/second

2. Problem Solving: Challenges with data quality and how you solved them

3. Business Impact: How real-time insights improved business metrics

4. Technical Depth: Deep dive into Flink state management and Kafka optimization

Salary Impact

Market Data (Updated for 2024):

• India Market: ₹15-25 LPA for mid-level, ₹25-45 LPA for senior
• US Market: $120K-160K for mid-level, $160K-220K for senior
• Premium for real-time skills: Additional 30-50% over batch processing roles

Companies Using Similar Technology

• Amazon: Product recommendations and fraud detection
• Uber: Real-time pricing and demand forecasting
• Netflix: Content recommendation and streaming analytics
• Spotify: Music recommendation and user behavior analysis
• Airbnb: Dynamic pricing and demand prediction

Getting Started

Prerequisites

• Strong Python programming skills
• Basic understanding of distributed systems
• Familiarity with SQL and database concepts
• Docker and Kubernetes fundamentals

Next Steps

1. Start with data generation: Create realistic e-commerce event data

2. Set up Kafka cluster: Configure topics and partitions

3. Implement basic stream processing: Simple transformations and filtering

4. Add analytics layer: ClickHouse setup and basic queries

5. Build monitoring: Prometheus and Grafana dashboards

This project represents the cutting edge of data engineering and will position you as a senior data engineer capable of building production-scale real-time systems used by top technology companies.