Enterprise Data Analytics Platform — Modern Data Stack at Scale

We built a modern data stack following the medallion architecture: Bronze layer (raw data ingestion), Silver layer (cleaned and validated data), and Gold layer (business-ready aggregates and features). Data ingestion uses Apache Airflow for orchestration with 200+ DAGs managing extraction from source systems (ERP, CRM, POS, IoT sensors, web analytics, and external APIs). Stream processing uses Apache Kafka with Kafka Connect for CDC from operational databases. The data warehouse is built on ClickHouse for analytical queries processing billions of rows with sub-second response times. Apache Superset provides self-service analytics with 300+ dashboards. A feature store built on Redis serves ML models in production.

### Data Pipeline Architecture

**Ingestion Layer:** Airflow DAGs extract data hourly from operational databases (PostgreSQL, MySQL, Oracle) using CDC with Debezium → Kafka Connect → raw Parquet files in object storage. Batch ingestion from APIs and file uploads follows the same pattern. All raw data is versioned with ingestion timestamp and source system metadata.

**Transformation Layer:** dbt (data build tool) transforms raw data through the medallion layers. Bronze → Silver transformations handle data type casting, null handling, deduplication, and data quality checks using Great Expectations. Silver → Gold transformations build business-specific aggregates — daily sales by product category and region, inventory turnover by warehouse, customer lifetime value cohorts, supply chain efficiency metrics. dbt tests validate referential integrity, uniqueness constraints, and business logic assertions.

**Serving Layer:** ClickHouse serves analytical queries with columnar storage and vectorized execution — a query scanning 500 million rows completes in under 2 seconds. Apache Superset connects to ClickHouse for dashboard visualization. A Python FastAPI service provides ML model inference with features served from the Redis feature store — ensuring training features and serving features are consistent.

## Key Features

Apache Airflow for workflow orchestration — deployed on Kubernetes with the KubernetesExecutor for dynamic pod allocation per task. Apache Spark for large-scale data transformations on Databricks. Apache Kafka with Kafka Connect for real-time CDC from operational databases. ClickHouse for the analytical data warehouse — columnar storage, vectorized query execution, and materialized views. dbt for data transformation with version-controlled SQL models, automated testing, and documentation generation. Apache Superset for visualization and self-service analytics. PostgreSQL for metadata and configuration. Redis for the ML feature store. Great Expectations for data quality validation. Docker and Kubernetes for all services. Terraform for infrastructure provisioning on AWS.

## Results

Data modeling is the foundation — investing in dimensional modeling (star schemas) upfront pays massive dividends in query performance and user comprehension. Schema evolution must be planned — upstream source system changes break downstream pipelines without proper versioning. Data quality is an ongoing investment — initial data validation catches 80% of issues, but the remaining 20% requires continuous monitoring and alerting. Self-service analytics requires governance — without data catalog, lineage tracking, and certification processes, users create conflicting metrics. The feature store was the unsung hero of the ML pipeline — eliminating training-serving skew reduced ML model failures in production by 90%.