The difference between modeling data as entities and as events, when each approach fits, key design choices, and how to combine both for full analysis.
An introduction to Apache Parquet: how it differs from row formats like CSV and JSON, key features such as compression and schema evolution, and when to use it.
How Parquet's columnar storage model works and why it speeds analytics through faster scans, better compression, and quick aggregation, plus when to avoid it.
Inside a Parquet file: how row groups, column chunks, and pages are laid out, what footer metadata stores, and how their sizes affect query performance.
