Part 3/11:

By grasping the subtle details of these workflows, data engineers can design systems that accurately reflect real-world processes, ensuring that the tech supports the domain rather than overshadowing it. This approach reduces rework, prevents issues like data inconsistency, and ultimately results in more resilient and scalable pipelines.

The Core Problems in Data Engineering

The speaker outlined several recurring challenges faced by organizations:

• Data Silos and Fragmentation: Different application systems (CRM, underwriting, collections, etc.) generate data stored across disparate platforms—databases, APIs, Kafka streams, files—leading to isolated data assets.