Data Engineering
The convergence of modern data platforms and AI infrastructure
Enterprise AI depends on the same data foundations that support analytics, but intelligent applications introduce new requirements around context, freshness, provenance and access.
The boundary between data engineering and AI engineering is becoming increasingly thin. AI applications depend on trustworthy enterprise data, while modern data platforms increasingly expose data products directly to intelligent applications and agents.
AI needs more than connectivity
Connecting a model to a database is relatively straightforward. Building a trustworthy enterprise intelligence layer requires understanding freshness, ownership, lineage, permissions and semantic meaning.
RAG is a data architecture problem
Retrieval quality depends on ingestion, chunking, metadata, indexing, filtering, ranking and source governance.
A poor retrieval architecture can make a capable model appear unreliable even when the model itself is performing correctly.
Data products become agent capabilities
When agents can query enterprise systems or trigger workflows, data and services become capabilities that must be exposed through controlled interfaces.
This creates an opportunity to standardize access through typed tools and governed APIs.
Security follows the data
AI systems should not bypass the access controls already required for enterprise data. Identity, authorization and auditability need to follow information as it moves through retrieval and agent execution.
A unified architecture
The strongest enterprise architectures treat data, intelligence, action and governance as connected layers.
This allows organizations to evolve their data and AI infrastructure without rebuilding every application as models, tools and workflows change.
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