Part 3/10:

He explained the typical data pipeline process—ingestion from sources, storage, warehousing, and visualization via BI tools—highlighting that these processes are traditionally manual, rigid, and susceptible to delays. The critical innovation, according to Mangjunad, is integrating AI-driven intelligence within these pipelines to enable them to adapt swiftly and automatically to changes.

Addressing Common Data Engineering Challenges

Organizations frequently face challenges such as schema changes, data source migrations, and increasing volumes of data. Schema modifications—adding, removing, or modifying fields—are routine but time-consuming, requiring updates across multiple components, from source mappings to destination schemas.