Part 2/13:
Most enterprises today are burdened with dispersed data sources—ranging from data lakes, relational databases, NoSQL stores, to streaming platforms—often stored in different formats and environments. Traditional ETL processes aim to consolidate these into a single data warehouse, creating a "single source of truth." However, this approach often results in data that is outdated, fragmented, and difficult to govern. Moreover, such architectures ill-suit the needs of modern AI and data science workflows, which thrive on unstructured data, real-time access, and minimal duplication.