From Legacy to AI-Ready: Aura Minerals’ Data Platform Transformation with Indicium and Databricks
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Aura Minerals, a global mining leader, partnered with Indicium to evolve its analytics capabilities and enable AI at scale. As its data strategy matured, Aura saw the opportunity to modernize its PySpark-based workflows and adopt a scalable framework powered by Databricks and dbt, without delays or tradeoffs.
Databricks unified architecture gave the company the scalability, performance, and governance to move fast and build with confidence. Now, Aura runs a governed, high-performance platform built for AI and real-time analytics.
Challenges: Legacy PySpark, Siloed Workflows, and Limited Visibility
Aura’s PySpark environment evolved but presented constraints in scalability, maintainability, and adaptability, expanding into more than 400 notebooks and 140 tightly coupled workflows. Each pipeline held critical business logic but lacked the modularity, visibility, and governance required to scale and prepare Aura for machine learning, AI, and real-time analytics.
Development velocity slowed, onboarding required specialized skill sets, and business users had limited visibility into data lineage and transformation logic. To eliminate these bottlenecks, Aura partnered with Indicium to rebuild the transformation layer with dbt on Databricks. The new architecture introduced modular SQL models, enforced version control with Git, and embedded tests, CI/CD, and full lineage tracking.
Every pipeline now follows governed, auditable patterns aligned to Databricks best practices. With Databricks’ unified analytics engine in place, Aura turned siloed workflows into a single, scalable platform, engineered for machine learning, real-time analytics, and AI at scale.
The Solution: dbt + Databricks, Delivered by Indicium’s AI Framework
Built on Databricks, the transformation framework delivered automation, governance, and speed at scale. Indicium deployed its proprietary Prompt2Pipeline Agent, fully integrated with Databricks, to accelerate migration, enforce best practices, and enhance all the potential of the Lakehouse platform.
Databricks enabled the end-to-end transformation of Aura’s PySpark estate into a governed, high-performance environment. Indicium deployed a specialized AI Data Squad to accelerate the migration using Databricks’ native capabilities for scalable data engineering, seamless integration, and real-time execution.
Indicium’s AI Migration Agent, purpose-built for the Databricks environment, converted PySpark logic into modular dbt models optimized for performance, reusability, and full alignment with the Lakehouse architecture.
Guided by the Prompt2Pipeline Agent, the transformation included:
- AI-powered discovery of the entire PySpark estate, surfaced and cataloged within Databricks
- Automated code conversion into dbt, with logic modularized and optimized for the Lakehouse architecture
- Seamless integration with dbt’s Metadata and Control Plane (MCP) for version control and governance, running natively on Databricks
- Rigorous validation and AI-augmented tests aligned to dbt and Databricks best practices
- Confident launch with continuous feedback and iteration, fully orchestrated within the Databricks environment
All of this was powered by key Databricks products:
- Lakehouse
- Delta Lake
- Data Governance
- Data Engineering
- Data Warehousing
- Machine Learning
By combining Indicium’s AI-powered framework with Databricks’ unified platform, Aura unlocked a governed, high-performance transformation layer, ready to scale with the business and built for enterprise AI.
87% Faster Pipelines, AI-Ready Architecture, and Full Autonomy
The migration unlocked AI-enabled access across the organization. Indicium converted complex PySpark logic into governed, modular dbt models, built and deployed entirely on Databricks. The platform now runs a self-service analytics layer with version control, full lineage, and built-in quality checks, all natively on the Lakehouse.
Aura’s business teams use AI copilots to access data instantly. The dependency gap has closed. Teams now explore and act on governed data with speed and confidence. We built it AI-ready from day one, and every model follows standardized patterns.
Every transformation includes governance by default. Databricks powers advanced analytics, autonomous agents, and AI copilots, all from a single, scalable platform. Indicium replaced specialized code with standardized templates. Specialized code was replaced with standardized templates. Control was embedded without slowing agility. Aura’s internal teams now own, extend, and operate the entire platform, directly in Databricks, with full autonomy.
Why Indicium + Databricks: Trusted Execution, Built for Scale
Indicium’s ability to combine AI-driven automation, dbt, and Databricks expertise made us the ideal partner for Aura’s platform modernization. Through our IndiMesh Framework, we applied an AI-enabled delivery model that accelerated execution, ensured governance, and maintained code quality throughout the transformation. The result was a scalable, production-grade dbt environment running on the Databricks, built to support operational efficiency and long-term AI readiness.
Aura’s biggest challenge centered on converting a fragmented, high-dependency codebase into a unified, maintainable platform that could scale with evolving data needs. Our integrated approach aligned technical and business teams around a common transformation framework, optimized both delivery and usability, and removed key friction points across the data lifecycle.
By combining automation, domain expertise, and the flexibility of the Databricks platform, we helped Aura establish a self-service, AI-enabled foundation. With governance, observability, and performance built in, Aura now operates a platform that supports continuous innovation, faster decision-making, and long-term data maturity.
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