Production Systems

Projects Built
for Scale

End-to-end data engineering systems designed for production — not just demos.

Production Systems

Featured Projects

End-to-end data engineering systems built for scale, reliability, and real business impact.

architecture
Project 01 · Flagship End-to-End Data Platform
PRODUCTION

FinStream
Data Platform

Medallion Lakehouse processing 200+ financial datasets per day at Nasdaq. Replaced ad-hoc raw table queries with a governed Bronze→Silver→Gold pipeline — reducing report discrepancies to zero and cutting analyst query time from 45s to 3s.

Bronze Layer Silver Layer Gold Layer Medallion Architecture
200+
Datasets/Day
<1hr
Data SLA
15×
Query Speedup
Databricks Delta Live Tables Auto Loader PySpark AWS S3 Airflow Unity Catalog Terraform
Pipeline Architecture
download_for_offline
Bronze · Raw Ingestion
Auto Loader · S3 · Glue · Schema Evolution
arrow_downward
settings
Silver · Cleansed
DLT Expectations · Dedup · SCD Type 2
arrow_downward
warehouse
Gold · Business-Ready
Aggregations · OPTIMIZE · Databricks SQL
Daily Throughput 300K events/hr
history
Time Travel
Instant rollback
bolt
Photon Engine
4× query speed
stream
Project 02 · Real-Time Kafka + Spark Streaming
● LIVE

MarketPulse
Streaming Pipeline

Real-time intraday anomaly detection on market tick data. Ingests 500K msgs/sec from Kafka via Spark Structured Streaming, applies stateful window aggregations, and fires alerts within 800ms of event occurrence.

speed
End-to-End Latency
< 800ms · event → alert
Kafka · AWS MSK Spark Structured Streaming Databricks Delta Lake Avro + Schema Registry FastAPI Redis
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Project 03 · Framework Data Quality & Observability

DataGuard
Quality Framework

Reusable data quality platform enforcing schema contracts at ingestion. Zero-trust quarantine pattern prevents corrupt data from ever touching production tables.

Pipeline Data Quality Score 99.4%
check_circle YAML schema contracts (GitOps)
check_circle DLT Expectations as quality gates
check_circle Automatic quarantine + alert on breach
Great Expectations DLT Grafana PagerDuty
psychology
Project 04 · ML Engineering Feature Store + Model Serving

ChurnSight
ML Pipeline

End-to-end churn prediction platform: automated PySpark feature engineering → Databricks Feature Store → MLflow registry → FastAPI serving in <50ms.

Features arrow_forward Training arrow_forward Registry arrow_forward API <50ms
Databricks Feature Store MLflow XGBoost + SHAP FastAPI Redis Docker · ECS
table_chart
Project 05 · Analytics Eng Data Warehouse · dbt · Star Schema
6 Source DW

TradeVault
Analytics DWH

Trade lifecycle analytics DWH joining 6 source systems — OMS, LMS, clearing feeds, FX rates — into a unified star schema. Automated reconciliation replaced a 2-day manual process with a zero-touch daily report.

stg_*
Source cleaning
fct/dim_*
Star schema
rpt_*
Reconciliation
trending_down
Reconciliation Process
2-day manual → full automation
dbt Core Databricks SQL Delta Lake AWS Glue Apache Airflow Tableau SCD Type 2