Every data leader today is chasing the same promise—to make data faster, cleaner and more reliable for business use. Yet, behind every modern dashboard lies a familiar struggle: pipelines fail silently, schemas change without warning, migrations drag on for months and despite all their automation tools, data engineers spend most of their time firefighting rather than innovating.
The reality is that data ecosystems have become too large, too fragmented and too dependent on manual intervention. The more we scale, the harder it becomes to keep things consistent.
That’s where Generative and Agentic AI are beginning to change the narrative—and where Persistent Systems, together with Databricks, is taking a bold step forward through a suite of GenAI-powered, agent-driven accelerators under the iAURA umbrella.
The Pain Behind Modern Data Pipelines
Even with the best platforms and teams, enterprises still wrestle with fundamental challenges that slow down their data modernization journeys, which include:
- Slow and Risky Migrations: Moving from legacy data warehouses to modern platforms like Databricks can be tedious, especially due to rewriting, testing and validation.
- Data Quality and Trust: When hundreds of pipelines feed thousands of tables, tracking drift or anomalies manually becomes nearly impossible.
- Complex Mapping and Modelling: As new systems are onboarded, teams must reconcile diverse structures and build unified data models that preserve business meaning and consistency across domains.
- Limited Visibility and Governance: Without clear lineage, it’s hard to understand where data came from, how it changed and why a certain number looks wrong.
These issues aren’t just technical inconveniences; they erode confidence in enterprise data and delay critical business decisions.
The Agentic Shift: Making Data Engineering Autonomous
Traditional automation in data engineering focuses on scripts and rules. Agentic AI, built on the foundation of Generative AI, goes a step further, it understands context. It can interpret schemas, infer relationships, detect anomalies and even explain why something looks wrong.
Imagine a system that can:
- Suggest mappings between source and target schemas in seconds
- Detect unusual trends in data and explain possible root causes
- Convert legacy ETL logic into Databricks-native code
- Learn continuously from user corrections
That’s what GenAI brings to the data engineering lifecycle – intelligence, adaptability and context awareness. It doesn’t replace data engineers; it makes them exponentially more productive.
Persistent + Databricks: Turning the Vision into Reality
Persistent Systems has partnered with Databricks to bring GenAI-powered automation directly into the Lakehouse ecosystem. The result is iAURA – a suite of GenAI accelerators purpose-built to simplify data engineering, observability and migration on the Databricks Data Intelligence Platform.
Each accelerator leverages core Databricks components – Delta Lake for storage, Unity Catalog for governance, Mosaic AI and Agent Bricks for intelligence and automation and Databricks Workflows for orchestration. These components deliver intelligence as part of the data platform itself, not as an external bolt-on.
The three core modules in focus are:
- iAURA Migrate Accelerator: Accelerates and validates legacy-to-Databricks migrations
- iAURA Data Observability: Continuously monitors data quality, reconciliation and freshness.
- iAURA Data Modeler & Mapper: Automates schema mapping and transformation logic.
Together, they make the entire lifecycle – from source to insight – faster, safer, and smarter.
iAURA Migrate Accelerator: Making Migration Seamless
Data migration is one of the most resource-intensive phases of modernization. Different SQL dialects, procedural ETL code, nested business rules – all must be re-engineered carefully. The iAURA Migrate Accelerator changes that game. Using a combination of parsing logic, agentic workflows and LLMs, it reads legacy scripts (like PL/SQL or Informatica mappings), extracts business logic, and generates Databricks-native code. It also automates data reconciliation, generating validation checks and row-level comparisons between source and target.
Under the hood, iAURA Migrate is powered by the Databricks Data Intelligence Platform – combining the governance strength of Unity Catalog, the intelligence of Mosaic AI, and the reliability of Delta Lake and Workflows for scalable execution and validation.
Enterprises adopting this accelerator have reported significant reductions in migration timelines – often 30–50% faster than traditional manual rewrites.
iAURA Data Observability: Making Data Trustworthy
Data quality issues, inconsistent reconciliations, and stale datasets continue to undermine confidence in enterprise analytics. As data volumes grow and pipelines multiply, manually tracking accuracy, completeness, or freshness becomes unmanageable.
The iAURA Data Observability Accelerator changes that. Powered by intelligent monitoring and GenAI-driven DQ rule discovery, it continuously evaluates data flowing through the platform – detecting drift, anomalies, and reconciliation gaps before they impact business outcomes. It automates data qualitychecks across domains, performs data reconciliation between pipelines and systems, and monitors data freshness.
Under the hood, iAURA Data Observability runs through a series of Databricks-native scripts and notebooks, orchestrated via Workflows to continuously track data quality, reconciliation, and freshness. These scripts leverage the Databricks Data Intelligence Platform – using Unity Catalog for governance and lineage, Mosaic AIfor anomaly detection and intelligent rule generation, and Delta Lake for versioned validation and historical traceability.
Enterprises leveraging this accelerator have seen measurable gains in data trust and operational efficiency – often reducing quality issue resolution time by 30–40%.
iAURA Data Modeler & Mapper: The Brain Behind the Models
Schema mapping has long been the hidden tax in data engineering. Manually reconciling hundreds of tables across diverse systems is tedious, repetitive, and error-prone – often delaying downstream analytics and model deployment.
The iAURA Data Modeler & Mapper Accelerator brings intelligence and automation to this process. Using GenAI models, it interprets source and target schemas, understands semantic intent, and proposes mappings automatically. It can also generate transformation logic ready for engineer validation and deployment within the Databricks environment.
The result is a guided, explainable, and continuous-improving data modelling process – transforming schema mapping from a manual bottleneck into an intelligent, continuously learning capability.
The Business Impact: From Reactive to Autonomous
Organizations that integrate iAURA accelerators into their Databricks environment see tangible benefits:
| Outcome | Impact |
| Accelerated Time-to-Value | Achieve up to 30–50% faster migration and pipeline delivery through automated code conversion, mapping, and validation. |
| Improved Data Trust | Automated drift detection, reconciliation, and freshness monitoring reduce data quality incidents by 30–40%, improving reliability of downstream analytics. |
| Reduced Cost | Automation cuts manual rework and testing cycles by 25–40%, lowering overall migration and maintenance effort. |
| Higher Productivity | Engineers spend 50-60% less time on repetitive mapping and validation, focusing instead on optimization and innovation. |
| Governance Built-In | Unity Catalog–driven lineage and audit tracking ensure full traceability and compliance with enterprise data governance frameworks. |
| Continuous Learning | Embedded GenAI models continuously fine-tune with feedback, improving mapping accuracy and rule precision over time. |
Conclusion: A Smarter Future for Data Engineering
The future of data engineering isn’t defined by faster pipelines or lower storage costs, it’s defined by intelligence embedded at every step of the data journey.
With iAURA accelerators on Databricks, Persistent is empowering enterprises to evolve from reactive data operations to intelligent, self-optimizing ecosystems, where data quality issues are proactively identified, migrations execute with built-in validation and each iteration enhances the platform’s resilience and performance.
With iAURA running on Databricks, organizations are achieving modernization with precision, transparency and continuous learning – setting a new benchmark for how data ecosystems should evolve.
The journey from chaos to clarity is no longer aspirational; it’s already redefining the way modern data platforms operate.
Author’s Profile
Mandar Baxi
Associate Vice President , Technology
