Data Science Pipelines

ETL batch orchestration, Apache Airflow DAGs, feature engineering, Parquet columnar storage, Lakehouse architectures, and MLOps tracking.

1ETL Orchestration & Apache Airflow DAGs

Batch workflow scheduling, DAG dependency resolution, task state machines, retry policies, and atomic partition overwrite idempotency.

ETL Orchestration & Apache Airflow DAGs

Design resilient ETL pipelines using Apache Airflow, resolve task dependency graphs, and guarantee data idempotency.

ETL Orchestration & Apache Airflow DAGs

Explore batch ETL orchestration fundamentals, construct Apache Airflow DAGs, manage task state lifecycles, and enforce pipeline idempotency.

2Feature Engineering & Data Transformation

Data leakage prevention, Fit-Transform hygiene, continuous feature scaling (Z-Score, Min-Max), and high-cardinality categorical target encoding.

Feature Engineering & Data Transformation

Construct leak-free feature preprocessing pipelines, apply numerical scaling, and encode categorical variables.

Feature Engineering & Data Transformation

Master feature engineering pipelines, eliminate data leakage between train and test splits, apply Z-Score standardization, and handle high-cardinality encodings.

3Data Lakehouse & Columnar Storage (Parquet)

Modern Data Lakehouse design, Apache Iceberg ACID metadata snapshots, Parquet row groups, column projection, and predicate pushdown pruning.

Data Lakehouse & Columnar Storage (Parquet)

Architect modern Data Lakehouses, analyze Apache Parquet columnar storage internals, and accelerate queries via predicate pushdown.

Data Lakehouse & Columnar Storage (Parquet)

Explore Data Lakehouse architectures with Apache Iceberg, compare row versus columnar storage paradigms, and examine Parquet predicate pushdown pruning.

4MLOps: Model Tracking & Versioning (MLflow)

MLOps lifecycle management, MLflow Tracking Servers, parameter and metric logging, Model Registry stage transitions, and Population Stability Index (PSI) drift monitoring.

MLOps - Model Tracking & Versioning (MLflow)

Implement end-to-end MLOps pipelines with MLflow, track experiment metrics, govern model registries, and detect production data drift.

MLOps - Model Tracking & Versioning (MLflow)

Deploy enterprise MLOps architectures with MLflow, ensure scientific reproducibility via experiment tracking, govern Model Registry stage transitions, and detect statistical data drift.