Data Engineering
The infrastructure layer of all AI and analytics work. Without reliable pipelines moving clean data from sources to models, no machine learning system can function at scale. This category covers the tools and patterns that keep data flowing.
Apache Airflow
Workflow orchestration for data pipelines. Schedule, monitor, and manage complex ETL processes at scale.
Apache Spark
Distributed data processing. Transform and analyse datasets too large to fit in memory on a single machine.
Data Warehouses
BigQuery, Snowflake, and Redshift — columnar storage and SQL analytics on billions of rows.
ETL Pipelines
Extract, Transform, Load — designing reliable pipelines that move data from sources to analytical systems.
dbt (Data Build Tool)
Transform raw data in your warehouse using SQL. Version-controlled, testable, and production-ready transformations.