How Auraa Semantic Flow renders human-in-the-loop AI agent interactions identically in web and chat from one JSON descriptor, no new frontend code per agent capability.
Most data platforms sit on top of Databricks. They wrap it. They abstract it. They build a parallel universe of storage, governance, and compute - then bridge back to the Lakehouse when they need to run something.
Auraa does the opposite.
Auraa is built for Databricks. Not on top of it, not alongside it, not in competition with it. Every design decision starts with the same question: Does Databricks already do this? If yes, we use it. If no, we build the thinnest possible extension.
The result is a platform that amplifies the Lakehouse instead of duplicating it.
What We Use Natively
- Delta Lake is our storage layer: All data - business data, metadata, configuration, and audit logs - lives in Delta tables following the medallion architecture. We don’t maintain a separate database. We don’t sync between systems. When you query Auraa’s metadata, you’re querying Delta.
- Unity Catalog is our security model: We don’t build a parallel RBAC engine. Auraa maps its abstractions - tenants, projects, roles - directly to Unity Catalog’s catalogs, schemas, and grants. One security model. One set of ACLs. One audit surface.
- Databricks Workflows run our heavy workloads: Ingestion, quality analysis, transformations - these execute as Databricks Jobs and Lakeflow Declarative Pipelines. Resilient, distributed, and managed by the platform that does this best.
- Databricks Apps host our control plane: Auraa runs as a Databricks App with serverless compute, OAuth authentication, and native workspace integration. No external VMs. No separate infrastructure to manage.
This isn’t just an architectural preference. It’s a strategic commitment. Every Auraa operation generates Databricks consumption - compute cycles, storage, SQL Warehouse queries. Auraa is a consumption amplifier, not a competing cost center.
What We Add
Databricks provides world-class infrastructure for storing, processing, and governing data. What it doesn’t provide is an opinionated orchestration layer that turns those capabilities into an autonomous, multi-tenant data platform.
That’s what Auraa adds.
- Tool-first design: Every platform capability - registering a data source, running a quality check, and provisioning a tenant - is exposed as a versioned, typed tool. Not a notebook. Not a SQL script. A tool with a contract, discoverable by any agent at runtime. This elevates Databricks from a code-execution environment to a modular, agent-ready API platform.
- Agentic orchestration: Instead of manually configuring Databricks Workflows, Auraa’s orchestrating agent (AIVARA) receives high-level intent, discovers the appropriate tools, builds an execution plan, and delegates the heavy lifting back to Databricks. Humans provide direction. The agent handles the mechanics.
- Active governance: Unity Catalog enforces permissions. Auraa manages them - creating grant policies from templates, applying them across tenant catalogs, detecting drift, and remediating it. Reading ACLs is valuable. Writing and reconciling them at scale is transformative.
- Metadata as data: Auraa treats its own configuration as a first-class data product. Ingestion specs, quality rules, and grant policies - all stored in Delta tables, all flowing through the medallion pipeline, and all governed by Unity Catalog. The platform’s behavior is as queryable and reproducible as the data it produces.
The Synergy
This is not a one-directional relationship. Auraa doesn’t just consume Databricks - it makes Databricks more valuable.
Every tenant Auraa provisions creates new catalogs, schemas, and tables in Unity Catalog. Every ingestion pipeline creates new Lakeflow jobs. Every quality check runs SQL against a Databricks warehouse. Every governance operation exercises Unity Catalog’s grant system.
The more Auraa does, the more the Lakehouse does.
Auraa stores everything in Delta, including its own state, so there’s no data gravity pulling information out of the Lakehouse, no external database to sync, and no middleware state to reconcile. The Lakehouse is the single source of truth for both the data and the platform that manages it.
Why This Matters
If you’re evaluating platforms for your Databricks investment, the architecture question matters more than the feature list.
A platform that builds its own storage, governance, and compute will always be pulling against the Lakehouse - duplicating state, bridging security models, adding latency. A platform that builds on the Lakehouse compounds its value.
Auraa is that platform. Databricks-native. Unity Catalog-governed. Delta Lake-stored. Agent-driven.