Unified Data, Smarter Agents—Is Your Architecture Future-Proof?

Why This Chapter Came About

As enterprises scale their AI ambitions, many discover that the real bottleneck isn't the model, its the data. Fragmented, inconsistent and inaccessible data undermines even the most sophisticated AI systems. This title was born from that realization: AI needs a unified, democratized data foundation to thrive.

In the era of agentic systems, where autonomous agents make decisions in real-time, this foundation becomes even more critical. Agents can only act wisely when they have access to accurate, complete and policy-governed context.

The Problem: Fragmented Data, Fragile AI

AI systems require two things:

1. Breadth of signal - to learn from diverse, rich datasets.
2. Stable semantics - to ensure consistent understanding across time and tools.

But most organizations suffer from:

• Data Silos across clouds and departments.
• Inconsistent definitions of key business metrics.
• Shadow AI usage to bypass governance.

The result ? Brittle models, unreliable insights and slow time-to-value.

What We Learned

• Unification is both physical and logical: Its not just about consolidating storage - its about harmonizing meaning.
• Open table formats are foundational: Delta Lake, Apache Iceberg and Hudi bring ACID guarantees schema evolution and time travel to cloud object stores.
• Semantic layers align business logic: Tools like dbt and Looker ensure that "revenue" means the same thing everywhere.
• Governance enables safe self service: Policy tags, masking and lineage make democratization sustainable.
• Multicloud is the new normal: BigLake and Snowflake's Iceberg support unify access without duplicating data by enabling cross cloud joins with security and governance.
• Marketplace turn data into products: Listings and sharing protocols make high-quality datasets discoverable and reusable.

The Solution: Architecting for Unified, Governed and Sharable Data

To build a data foundation that AI can trust and scale with, organizations must do the below:

1. Adopt open table formats (Delta Lake, Iceberg, Hudi) for reliable, scalable storage.
2. Implement semantic layers (dbt Semantic layer, LookML) to align business logic.
3. Enforce policy-based governance (BigQuery, Snowflake, Unity Catalog) to protect sensitive data.
4. Enable multicloud access with consistent security models.
5. Leverage data marketplaces (Snowflake Listings, BigQuery Exchanges, Delta Sharing) to distribute features and datasets.

This architecture doesn't just support AI, it accelerates it.

Use Case: Building a Feature Store for Enterprise-Wide AI

Lets walk through a real-world scenario: building a feature store that serves predictive and generative AI workloads across clouds.

Architecture Overview:

• Ingestion: Change data capture streams updates into bronze, silver and gold tables.
• Storage: Open table formats (eg: Delta Lake) ensure ACID compliance, schema evolution and time travel.
• Semantic Layer: Shared definitions (eg: revenue, churn risk) ensure consistency across models.
• Governance: Policy tags and masking protect sensitive data. Lineage tracks impact.
• Feature Store: Tools like Feast or Vertex AI Feature Store manage definitions, versions and retrieval.
• Distribution: Features are published via marketplaces available in various platforms as listed above in the solution.

Minimal Feature Store Setup with Feast

Below is the simplified Python example using Feast (open source, cloud-agnostic feature store) to define a feature store that integrates with open table formats like Delta Lake or Iceberg (via Parquet):

1. Define entities and feature views using Python APIs
2. Bind features to batch or stream sources.
3. Use feature_store.yaml to configure offline and online stores.
4. Apply the repository to provision storage and update the registry.

Cloud-Native Alternatives

• Vertex AI Feature Store: Autoscaling, retention and cross-cloud support.
• SageMaker Feature Store: Feature groups and cross-account sharing.
• Databricks + Unity Catalog: Governance and discovery for Delta Lake-based features.

Measuring Success: How Do You know its Working?

A unified and democratized data foundation isn't just a technical achievement, its a strategic enabler. But how do you measure its impact?

• Reduced Time to Production

Days to production, thanks to reusable features, governed access and consistent semantics.

• Increased feature reuse across domains

Feature stores act as registries, reducing duplication and promoting standardization.

• SLAs for Freshness and Availability

Offline and online feature stores meet defined service level objectives.

• Fewer Training-Serving Skew incidents

Unified semantics and time-travel enabled storage ensure that training and inference use the same logic and data

• Improved lineage and debugging

When something breaks, lineage tools (Unity Catalog, BigQuery Data Catalog) help trace the issue upstream.

This shortens mean time to resolution (MTTR) and builds trust in the system.

Operating Practices for Day Two: Sustaining the Foundation

Building a democratized data platform is just the beginning. Day two operations determine whether it scales and sustains value.

Time travel enables reproducibility and rollback - This is achieved using Open table formats that support snapshotting and rollback.

For eg: Teams can recreate training datasets exactly as they were at any point in time.

Centralized Semantic Definitions - Semantic layers (dbt, Looker) act as the single source of truth for business metrics.

Metric drift is corrected once and flows downstream automatically.

Policy-based Governance - Tags, masking policies and attribute-based access control (ABAC) ensure safe self-service.

Scalable Distribution - Marketplaces reduce point-to-point integrations and prevents copy sprawl.

Monitoring and Observability - Track lineage, access logs, freshness metrics and usage patterns.

Use this data to optimize pipelines, deprecate unused features and enforce SLAs.

These practices compound value as AI adoption grows.

Putting It All Together: Architecture, Governance and Impact

Unification is the technical and organizational act of giving all AI systems a single, reliable substrate with stable semantics, governed access and cross cloud reach. Democratization is the operational act of making that foundation easy and safe for many systems to use.

Together, they form the backbone of scalable, trustworthy AI.

With open table formats on object storage, semantic layers, policy-based governance, multicloud query planes and data sharing marketplaces, enterprises can build AI-ready foundations that are reproducible, secure and scalable. These capabilities accelerate model delivery, improve trust and enhance performance across predictive and generative workloads.

This article is adapted from our book "Advanced Data Engineering Architectures for Unified Intelligence", which is the culmination of years of hands-on work in building modern data platforms for AI and agentic systems. The book explores how democratized data architectures are not just technical choices, but strategic imperatives for enterprise transformation.