Real-Time Analytics Platform
Processing 500K+ events/sec with sub-second dashboard latency
Overview
This platform was built to replace a legacy batch-processing system that delivered analytics with a 24-hour delay. The business needed real-time visibility into user behavior, transaction patterns, and system health to make time-sensitive decisions. I led the end-to-end design and implementation of a streaming-first architecture that processes over 500,000 events per second and delivers insights to stakeholders through live dashboards with sub-second latency.
The Challenge
The existing batch ETL pipeline ran nightly, meaning business stakeholders were always looking at stale data. During peak traffic events (product launches, flash sales), the team was flying blind — issues went undetected for hours, and opportunities were missed. The legacy system also couldn't scale horizontally, causing frequent job failures as data volume grew 40% year-over-year. We needed a solution that could handle bursty traffic, guarantee exactly-once processing, and integrate seamlessly with existing BI tools.
The Solution
I designed a Lambda architecture that combines a real-time streaming layer for immediate insights with a batch layer for historical accuracy and backfill. The streaming layer uses Apache Kafka as the event backbone, Spark Structured Streaming for transformation and enrichment, and Delta Lake as the unified storage layer supporting both streaming writes and batch reads. A serving layer powered by materialized views in a columnar store feeds the live dashboards.
System Architecture
Ingestion Layer
High-throughput event collection with schema validation and dead-letter routing
Stream Processing
Real-time transformation, enrichment, aggregation, and windowed computations
Storage Layer
Unified lakehouse supporting both streaming upserts and time-travel queries
Serving Layer
Low-latency materialized views optimized for dashboard queries
Orchestration & Monitoring
Pipeline orchestration, data quality checks, and observability
Key Features
Exactly-Once Processing
Implemented idempotent writes with Kafka transactional producers and Spark checkpoint-based recovery to guarantee no duplicates or data loss, even during node failures.
Dynamic Scaling
Auto-scaling Spark executors based on Kafka consumer lag metrics. During peak events, the cluster scales from 20 to 80 executors in under 2 minutes with zero downtime.
Schema Evolution
Full backward/forward compatible schema evolution using Avro and Schema Registry, allowing producers and consumers to deploy independently without breaking changes.
Live Dashboard Engine
Custom WebSocket-based push system that streams pre-aggregated metrics to Grafana and a React dashboard, achieving P99 latency under 800ms from event to pixel.
Data Quality Gates
Automated quality checks at every pipeline stage using Great Expectations. Failed checks route data to quarantine topics and trigger PagerDuty alerts.
Time-Travel & Backfill
Delta Lake time-travel enables instant rollback and historical replay. Backfill jobs can reprocess months of data without affecting the live streaming pipeline.
Results & Impact
Peak sustained throughput across 3 Kafka clusters with 99.99% delivery guarantee
P99 latency from event ingestion to dashboard rendering
Over 12 months of operation with zero data loss incidents
Compared to the legacy system, through better resource utilization and spot instances
Reduced analytics latency from next-day batch to sub-second streaming
Daily volume across all event streams with automatic compaction
Tech Stack Deep Dive
Streaming
Storage
Orchestration
Infrastructure
Monitoring
Lessons Learned
Start with exactly-once semantics from day one — retrofitting it later is exponentially harder and riskier.
Invest heavily in observability before scaling. We built custom Kafka consumer lag dashboards and Spark stage-level metrics that became essential for debugging.
Schema evolution discipline pays dividends. Enforcing backward compatibility from the start prevented multiple potential production incidents.
Delta Lake's time-travel feature was invaluable not just for debugging but for A/B testing pipeline changes against historical data.
Auto-scaling based on consumer lag rather than CPU/memory gave us much better responsiveness to traffic bursts.