Data Engineering

he cross-cutting and heterogeneous nature of this discipline implies criticality and complexity for organizations. It requires a wide variety of knowledge of very different tools, typically rapidly and continuously evolving.

Among the aspects to be covered are:

• Data movement from source to destination
• Access control and segregation of information
• Data processing, transformation, and enrichment
• Orchestration and automation of data flows
• Governance, from the inventory of the meaning of the data to their physical location, from mapping between physical and semantic form to lineage for tracing their diffusion, from data quality to consent management

For the success of the data strategy, it is also crucial to be able to design scalable and reliable data architectures, prioritize polyglotism, and reduce lock-in.