The document discusses trends in data modeling for analytics. It outlines weaknesses in traditional enterprise data architectures that rely on ETL processes and large centralized data warehouses. A modern approach uses a data lake to store raw data files and enable just-in-time analytics using data virtualization. Key aspects of the data lake include storing data in folders by level of processing (raw, staging, ODS, aggregated), using file formats like Parquet, and creating star schemas and aggregations on top of the stored data.