2.9M unfilled roles
The talent wall
2.9M unfilled data roles globally. Mid-market teams can't compete for the engineers this work demands.
Auraa
The fastest path to
AI-ready data on Databricks.
Tell Auraa what you need in conversational language. Agents discover your sources, build your pipelines, enforce quality, and register everything in Unity Catalog, autonomously, from day one.
The gap Databricks customers face
Buying Databricks and operationalising it
are completely different problems.
70% maintenance load
The backlog that never clears
Sources the business needs sit in a queue for months. Engineers are consumed by maintenance, not value creation.
12–18 months to value
The activation gap
Databricks customers spending 12–18 months, still not in production. The platform sits idle. The business waits.
Auraa closes that gap. Autonomously. From day one.
The platform
Three things Auraa eliminates permanently.
Autonomous agents that discover, ingest, govern, operate, and build your entire Databricks lakehouse, encoding senior data engineering expertise at unlimited scale.
01
Explode capacity
Reduce engineering requirements by 77%.
Traditional data activation demands an army of engineers. Auraa's agents encode the expertise, your team focuses on the work that actually differentiates your business.
02
Clear the backlog
First source live in under 15 minutes.
Conversational interface. No pipeline code. No configuration. No ticket. The backlog that's been growing for 18 months clears itself.
03
Activate from day one
Deployed in 4 hours. 70% lower cost.
Platform up and running in four hours, not four quarters. 70% lower total cost than a traditional implementation.
Say it. Auraa builds it.
From prompt to Lakehouse.
Autonomously.
No notebooks. No JIRA ticket. No pipeline code. Tell Auraa what you need in conversational language, agents profile your sources, build the pipelines, enforce data quality, and promote tables to Silver in Unity Catalog. Your Databricks lakehouse, built autonomously.
Conversational onboarding Live session
You
Auraa
Profiling source, 47 tables found
Building Bronze pipelines, 3 schedules configured
Applying data quality rules
Promoting to Silver with full lineage
Registered in Unity Catalog
Pipelines live. Silver-ready. Audit trail active.
Governed by Unity Catalog
Silver — production-ready
Bronze — raw, ingested
SQL Server R&D — 47 tables connected
How Auraa works
Autonomous. End to end. On Databricks.
Four steps for fully agent-driven, fully governed, fully on your Databricks workspace.
01
Discover
Identify & profile
Auraa identifies and profiles your data sources. Tell it what you need in plain language. No schema mapping, no manual configuration.
02
Ingest
Build the pipelines
Agents build pipelines automatically. Schema drift is handled without a ticket or a page.
03
Govern
Register & secure
Every pipeline, decision, and data object registered with full lineage and access control, automatic, auditable, queryable.
04
Operate
Monitor & recover
Autonomous monitoring. Agents handle degradation and failure signals. Your team stays focused on what matters.
Coming Full autonomous self-healing — arriving October 2026.
The numbers
Measured outcomes.
%
Lower total cost vs traditional implementation
hrs
From zero to deployed Auraa platform
%
Reduction in engineering team requirement
<min
To onboard your first source, conversationally
Built for
Built for enterprises where AI is the goal, and data is the bottleneck.
Stop throwing an army of engineers at a problem that agents can solve.
Pipeline backlogs, manual onboarding, maintenance that never ends — Auraa automates the 70% of data work that should never have needed a human in the first place.
Your Databricks investment should be working. Not waiting.
Organisations spend many months trying to get from contract to production. Auraa closes that gap — autonomously ingesting sources, enforcing quality, and delivering value from day one.
Resource library.
You Already Have a Message Bus: Why We Stopped Using Kafka
Why Covasant replaced Kafka with DeltaBus, an event bus built entirely on Delta Lake and Change Data Feed: at-least-once delivery, permanent...
Read the blog
The Path Is the Policy: How We Eliminated Row-Level Security Filters
How Auraa enforces multi-tenant isolation by addressing instead of row-level security filters, using tenant-scoped Delta paths that can't be...
Read the blog
The Data Platform Built for Agents: Inside Auraa’s Architecture
Databricks Synced Tables didn't hold up for 35+ governance tables. Here's how a single Governance Writer gave Auraa fast reads without a second...
Read the blog
Genie Code Is the Hands. Auraa Is the Brain and the Rules
Databricks Genie Code writes the code. Auraa adds governance, versioned metadata, and multi-tenant isolation. See how the two work together as one...
Read the blog
Why Auraa Builds on Databricks - Not Around It
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...
Read the blog
Beyond Buttons and Prompts: How We Let AI Agents Build Their Own UI
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...
Read the blog
Reducing AI Agent Documentation Costs by 42x Without a Vector Database
How Covasant Engineering built semantic documentation search for AI coding agents at $2–10/month using only Databricks SQL Warehouse,...
Read the blog
How We Made Databricks Apps APIs Fast Without Breaking Our Single Source of Truth
Agentic AI is in production, but is your governance ready? Explore visibility, control, and accountability gaps, plus six principles every enterprise...
Read the blog
Rethinking Data Engineering: What If Your Pipelines Were Driven by Data, Not Code?
What if data pipelines were driven by data, not code? Learn how Auraa's metadata-driven, AI-first approach replaces 6-month builds with hours...
Read the blog
Configurable Organization Meets the Lakehouse: How Tenant-Scoped Delta Paths Enable Single-Source-of-Truth Retrieval
Most multi-tenant data platforms enforce isolation through row-level filters, query interceptors, and access-control middleware.
Download the white paper
DeltaBus: A Lakehouse-Native Event Bus Pattern for Data Platforms
Most data platforms built on the Lakehouse add a separate message queue. Kafka, SQS, Azure Event Hubs. It’s an another cluster to manage, separate billing line and another governance gap, because events flowing through an external bus exist completely outside your Unity Catalog boundary.
Download the white paper
Semantic Documentation Search for Agentic Platforms
AI coding agents are only as useful as the knowledge they can access. When an agent hits an unfamiliar question how tenant isolation works, where a data contract is defined, what the full authentication flow looks like it falls back to keyword search.
Download the white paper
Auraa Semantic Flow: Transient, Data-Driven Interfaces for Agent-Human Collaboration
Every new agent capability your team ships eventually runs into the same constraint: the frontend. Building the review page, wiring state management, handling confidence scores and approval gates for every agent feature that takes hours on the backend, the interface takes days.
Download the white paper
Auraa, An Agentic Data Activation Platform for Databricks
Most enterprise data platforms today were built for humans, UIs, notebooks, scheduled jobs. Adding an AI copilot on top doesn't change the architecture underneath. The agent suggests.
Download the white paper
Auraa and Genie Code: Better Together - An Agentic Data Platform Meets Databricksʼ AI Coding Agent
YGenie Code writes SQL and Python, builds Lakeflow pipelines, debugs failures, and monitors production workloads.
Download the white paper
Auraa & Databricks: The Agentic Substrate for the Intelligent Lakehouse
Databricks gives you one of the most capable data platforms available. Delta Lake, Unity Catalog, serverless compute, Lakeflow pipelines. The infrastructure is solid.
Download the white paper
Rethinking Data Engineering: An AI-First, Metadata-Driven Platform for Databricks
Auraa is Covasant’s Databricks-native, agent-driven data platform that automates ingestion, data quality, governance, and analytics across the Lakehouse through a modular suite of components orchestrated by AI agents.
Download the white paper
Sub-Second on the Lakehouse: How Auraa Serves APIs from Databricks Without Breaking the Single Source of Truth
Your data platform has one job: be the single source of truth. But as your teams build more on top of it, APIs, AI agents, real-time applications, a quiet tension emerges.
Download the white paper
Auraa: The Fastest Path to AI-Ready Data on Databricks
Auraa delivers governed, AI-ready Databricks lakehouses faster and at significantly lower cost than traditional implementations...
Download the brochureQuestions that enterprise leaders ask us
If your question is not here, our team will answer it directly.
Talk to a Specialist →How does Auraa help organisations close the gap between buying Databricks and getting value from it?
Most Databricks environments take many months to reach production because building a lakehouse is not the same as buying one. Covasant uses Auraa as the delivery platform to build that lakehouse inside your existing workspace, governed and AI-ready, without putting an implementation burden on your team.
How does Auraa automate Databricks lakehouse delivery across the medallion architecture?
Auraa does not just automate pipelines. It builds the lakehouse itself. Schema profiling, pipeline creation, quality enforcement, and governance are all delivered by Covasant using Auraa inside your Databricks workspace, with no manual configuration required from your team. Today that covers the full bronze-to-silver layer. Full Gold layer automation is coming soon, followed by the ability to build governed data products through nothing but a conversational prompt.
What happens to Unity Catalog governance when Auraa is running inside our environment?
It gets stronger from day one. Because Auraa builds the lakehouse rather than individual pipelines, governance is not retrofitted after the fact. Every catalog entry, access policy, lineage record, and audit trail is written to Unity Catalog and Delta Lake as the lakehouse takes shape. Your team inherits a governed foundation from the start.
We have data sitting across many different source systems. Can Auraa unify that into a single lakehouse on Databricks?
Disparate source estates are precisely the environment Auraa is built for. Covasant uses Auraa to onboard those sources into a unified, governed Databricks lakehouse conversationally, without bespoke connector work for supported sources. The fragmented data your AI and analytics workloads depend on becomes part of a single trusted foundation without a year-long unification project.
How does Auraa relate to Lakeflow and other Databricks-native tools?
Auraa sits upstream of Lakeflow, not beside it. Where Lakeflow helps engineers build pipelines, Auraa builds the lakehouse that those pipelines serve, driving Lakeflow using Declarative Pipelines internally as a core execution component. Your existing Databricks tooling stays intact. Covasant uses Auraa to make it deliver the full lakehouse it was always meant to.