Data Engineering System
Real-Time Crypto Streaming on Azure
Produced analytics-ready Gold tables on a 5-minute cadence with checkpointing, quality validation, and layered Delta Lake storage for simulated real-time market analytics.
- Binance WS
- Event Hub
- Databricks
- Delta Lake
Problem
Crypto market data changes continuously, but raw WebSocket feeds are not usable for trusted analytics without durable ingestion, transformation, and quality checks.
Solution
Built a streaming pipeline that ingests Binance WebSocket events into Azure Event Hub, processes them with Databricks, stores Delta Lake tables, and models Bronze, Silver, and Gold layers.
Results
Produced analytics-ready Gold tables on a 5-minute cadence with checkpointing, quality validation, and layered Delta Lake storage for simulated real-time market analytics.
Solution
The pipeline separates ingestion, processing, and analytics. A Python producer publishes Binance trade events into Azure Event Hub, Databricks Structured Streaming processes the feed, and Delta Lake stores progressively cleaned Bronze, Silver, and Gold datasets.
Architecture
Binance WebSocket producer, Azure Event Hub ingestion, Databricks Structured Streaming, Delta Lake Bronze/Silver/Gold tables, checkpoints, and Databricks SQL dashboards.
Scalability Considerations
Scale by partitioning streams by symbol or market segment, monitoring consumer lag, and increasing Databricks cluster capacity as event volume grows.
Engineering Decisions
- Used Event Hub to decouple WebSocket ingestion from downstream processing.
- Modeled the lakehouse in Bronze, Silver, and Gold layers to separate raw retention, cleansing, and business-ready aggregates.
- Used checkpointing so streaming jobs can recover without replaying or duplicating processed events.
- Applied quality validation before promoting records into analytics-ready tables.
Tradeoffs Considered
- Streaming improves freshness, but it adds operational overhead around checkpoints, replay, cluster cost, and monitoring.
- A lakehouse layout keeps raw events available for backfills, but it requires clear promotion rules between Bronze, Silver, and Gold.
- Five-minute Gold updates balance near real-time analytics with compute cost and downstream query stability.
Results
- Created a repeatable pattern for real-time financial ingestion on Azure.
- Delivered Gold-layer metrics for price movement, volume, spread, and volatility analysis.
- Designed the workload for restartability with checkpoints and layered Delta tables.
What I Would Do In Production
Add schema registry, dead-letter queues, alerting on stream lag and failed batches, CI/CD for Databricks jobs, replay procedures, access controls, and cloud cost monitoring.