The Big Data Ecosystem
Data ETL & Pipelines
Orchestrating complex workflows using Apache Airflow, Dagster, or Prefect. Moving data from source to sink with high reliability.
- • Change Data Capture (CDC)
- • Schema Evolution Management
- • Backfill & Idempotency
Real-time Computing
Stream processing with Apache Flink and Spark Streaming. Sub-second latency for fraud detection and live monitoring.
- • Event-driven Architecture
- • Windowing & State Management
- • Kafka / Pulsar Integration
OLAP & Data Lake
High-performance analytics with ClickHouse, StarRocks, and Doris. Modern Data Lakes using Iceberg, Hudi, or Delta Lake.
- • Columnar Storage Formats
- • Vectorized Execution
- • Data Cube Materialization
The Technology Stack
Languages
- Python (PySpark, Pandas)
- Scala (Spark Core)
- Java (Hadoop Ecosystem)
- Rust (Data Fusion, Polars)
- SQL (The Universal Language)
Machine Learning
- PyTorch & TensorFlow
- Scikit-learn
- XGBoost / LightGBM
- MLflow (Lifecycle)
- Feature Stores (Feast)
Storage & Compute
- HDFS / Amazon S3
- Apache Spark / Trino
- Elasticsearch / Solr
- Redis (In-memory)
- PostgreSQL (Metadata)
Ops & Cloud
- Kubernetes (K8s)
- Docker / Containerization
- Terraform (IaC)
- Prometheus & Grafana
- CI/CD (GitHub Actions)