Real-time Analytics Pipeline
Designed and implemented an ETL pipeline processing 1M+ daily events, enabling real-time business intelligence dashboards for stakeholder decision-making.
Key Metrics & Results
Project Overview
Built a scalable, fault-tolerant data pipeline that ingests, transforms, and serves analytics data in near real-time. The system processes over 1 million events daily from multiple sources, supporting mission-critical business intelligence applications.
The Problem
Legacy batch processing system had 24-hour data latency, preventing timely decision-making. Manual ETL processes were error-prone, couldn't scale with growing data volumes (5x increase expected), and lacked proper monitoring and alerting capabilities.
The Solution
Architected streaming data pipeline using Apache Spark Structured Streaming with Kafka for event ingestion. Implemented incremental processing with checkpointing for fault tolerance. Orchestrated entire workflow with Apache Airflow for monitoring, scheduling, and automated recovery.
📊 Data Visualizations & Insights
Pipeline Throughput Over Time
Chart
Line graph showing consistent processing of 1M+ events/day with ability to handle 5x spike during peak periods without performance degradation
Data Quality Metrics
Chart
Dashboard displaying real-time data quality scores: completeness (99.8%), accuracy (99.5%), timeliness (avg 4.2 min latency)
Business Impact
- ✓Reduced data latency from 24 hours to under 5 minutes
- ✓Achieved 99.9% pipeline uptime with automatic failure recovery
- ✓Scaled to handle 5x data volume without performance degradation
- ✓Enabled real-time anomaly detection saving $300K in fraud prevention
- ✓Reduced infrastructure costs by 40% through resource optimization
- ✓Improved decision-making speed with fresh data availability
Technologies & Tools
Apache Spark
Distributed data processing and transformation at scale
Apache Kafka
Event streaming, message queuing, and data ingestion
PostgreSQL
Analytical data warehouse optimized for OLAP queries
Apache Airflow
Workflow orchestration, scheduling, and monitoring
Python
ETL logic, data quality checks, and automation scripts
Docker & Kubernetes
Containerization and orchestration for scalability
✨ Key Features
- ▸Streaming ingestion from multiple sources: APIs, databases, application logs
- ▸Schema validation and evolution handling with backwards compatibility
- ▸Incremental processing with exactly-once semantics using Kafka transactions
- ▸Comprehensive data quality checks with automated alerting on anomalies
- ▸Intelligent partitioning strategy for optimal query performance
- ▸Real-time monitoring dashboard with SLA tracking and health metrics
- ▸Automated rollback and recovery mechanisms for pipeline failures
⚡ Challenges & Solutions
⚠️Handling Late-Arriving Data
Solution Applied:
Implemented watermarking strategy with configurable grace periods (2 hours) to handle late events. Created separate historical reprocessing pipeline for data arriving beyond grace period to maintain accuracy without blocking real-time flow.
⚠️Ensuring Data Quality at Scale
Solution Applied:
Built automated data quality framework with 50+ validation rules covering completeness, accuracy, consistency, and timeliness. Implemented circuit breaker pattern to halt pipeline on critical data issues while allowing minor issues to be flagged for review.
⚠️Managing Schema Evolution
Solution Applied:
Adopted schema registry with backward compatibility enforcement. Implemented graceful degradation for schema changes and automated migration scripts. Created versioned data models to support multiple schema versions simultaneously during transitions.
🚀 Future Enhancements
- →Implement machine learning feature store for real-time model serving
- →Add multi-region deployment for disaster recovery and compliance
- →Develop self-healing capabilities with automated troubleshooting
- →Create comprehensive data lineage tracking for governance and debugging
- →Implement dynamic resource allocation for cost optimization
Interested in this project?
I'd love to discuss the technical details, methodology, and learnings from this project. Feel free to reach out to learn more!