Architecture Thinking
System
Design
Click any node in the pipeline diagram to understand the design decisions, trade-offs, and engineering rationale behind each component.
End-to-End Data Platform
Real-Time Lakehouse Architecture
The same pattern I use at large scale — click each node to understand why it was chosen and what trade-offs were made.
Sources
Ingest
Process
Store
Serve
database
PostgreSQL
Transactional DB
api
REST APIs
Market Data
folder
File Drops
CSV / Parquet
stream
Apache Kafka
Event Streaming
bolt
Apache Spark
Batch + Stream
account_tree
Airflow
Orchestration
BRONZE
Raw Ingestion · S3
SILVER
Cleaned + Validated
GOLD
Business Aggregations
cloud_done
Snowflake
Analytics Queries
bar_chart
BI Tools
Tableau / PowerBI
← Click any component to see design decisions →
trending_up
Scalability
Horizontal partitioning by date/entity. Spark's distributed compute over cluster nodes. Snappy compression on Parquet — 4× storage reduction at no cost.
verified
Reliability
Idempotent jobs with checkpoint-based recovery. Dead-letter queues for Kafka consumer failures. Airflow retries with exponential backoff and PagerDuty alerting.
visibility
Observability
Record count assertions at every layer boundary. Schema drift detection alerts. Data freshness SLAs measured and reported in Grafana dashboards.