AI-Native Lakehouse Architecture

The Challenge

Most enterprise data estates are split across warehouses optimized for BI, lakes optimized for ML, and bespoke vector stores for LLM applications. The result: triplicate storage, drifting governance, and AI agents that cannot trust the data they consume.

How CYBERIA Solves This

Single Open Table Format Surface

We standardize on Apache Iceberg or Delta Lake on cloud object storage, with a unified catalog (Unity, Polaris, or Nessie) that serves Spark, Trino, DuckDB, and vector engines from the same physical bytes.

• ACID transactions across petabytes
• Time travel for model reproducibility
• Branching for safe schema evolution
• Native vector columns for retrieval-augmented AI

Reference Architecture

1. Ingest — Kafka / Kinesis with exactly-once guarantees
2. Land — Iceberg tables on S3 / ADLS / GCS
3. Transform — dbt + Spark with declarative lineage
4. Serve — Trino for analytics, Feature Store for ML, vector index for LLMs
5. Govern — Unified catalog with column-level policies enforced everywhere

Business Impact

• 70% lower storage cost vs warehouse + lake + vector store
• Zero-copy AI training directly from production tables
• Sub-second semantic search alongside SQL workloads

Federated Learning

Cyberia Dataspace is not another FedAvg implementation — the aggregation algorithm is the small, replaceable part. What teams actually lack is the data and control plane federated learning needs to run on: one that knows where records live, what shape they have, who may use them and how every artefact was produced.

Rows stay inside the hospital, the plant, the bank, the country. Only metadata, schema, policy, training instructions, model parameters, gradients and metrics cross a node boundary — every one versioned, inspectable and logged. The result is collaboration across legal entities without moving sensitive data, with audit-ready evidence for GDPR, HIPAA and the EU AI Act built in.

Feature Store & Vector Index

Why It Matters

The hardest part of putting ML and LLMs in production is not the model — it's serving consistent, low-latency features and retrieval at runtime, with the same definitions used in training.

Our Approach

• Online + offline parity — same transformation logic, sub-10ms p99 reads
• Vector-native — embeddings stored alongside structured features
• Lineage to source — every feature traceable to upstream lakehouse tables
• Governed retrieval — RBAC and PII redaction enforced at query time

Stack

Feast, Tecton, LanceDB, Qdrant, Weaviate, with custom Iceberg-native serving layers for clients with extreme scale requirements.