Enterprise data leaders want to make data more accessible, reliable and ready to support analytics and AI. Yet the journey often begins with a less visible challenge: understanding the legacy systems that hold their organisation’s business logic.
Years of transformations may be embedded in ETL mappings, SQL scripts and stored procedures. Dependencies are difficult to trace, documentation is incomplete and engineers spend considerable effort interpreting existing code before they can begin rebuilding it.
A modern target platform is only part of the answer. Enterprises also need a way to understand their legacy estate, preserve business meaning and validate the workloads they move.
This is where Persistent’s iAURA 2.0 migration capabilities support modernization to Snowflake, bringing agentic AI-assisted assessment, conversion and validation into a connected migration lifecycle
Challenge Behind Legacy Data Warehouse Modernization
Moving from legacy data warehouses and ETL platforms to a cloud-based data platform, such as Snowflake, is rarely a straightforward code conversion exercise.
Business rules can be buried within Informatica mappings, Oracle PL/SQL procedures and Teradata BTEQ scripts. Preserving those rules matters because reports, applications and downstream processes depend on them.
Dependencies add another layer of complexity. A reporting pipeline may rely on several upstream transformations, while a shared procedure may support multiple workloads. Without a clear view of these relationships, teams risk discovering missing dependencies late in delivery.
Then comes validation. Converted code must do more than execute successfully: teams need evidence that it produces the expected data and functional results.
These are not separate problems. What teams discover during assessment shapes migration sequencing, conversion, testing and cutover readiness. iAURA 2.0 addresses these stages together rather than treating code conversion as an isolated task.
Agentic Shift: Bringing Intelligence Into Modernization Lifecycle
Traditional migration automation often focuses on predefined conversion rules and repeatable patterns. iAURA 2.0 combines parsing logic, specialized agents and LLM-assisted workflows to analyze legacy code, extract business rules and generate Snowflake-oriented implementations.
Migration capabilities support:
- Analysis of legacy ETL and SQL objects
- Dependency mapping, lineage extraction and complexity assessment
- Generation of Snowflake SQL and Snowpark transformations
- Test generation and source-to-target reconciliation
- Creation of technical documentation and integration artefacts
The objective is not to remove data engineers from the process. It is to reduce repetitive analysis, rewriting and documentation, giving engineers more time to review implementation choices and validate outcomes.
Connecting Legacy Logic to Target Platform
iAURA’s Snowflake migration accelerator generates implementation patterns using Snowflake-native SQL and Snowpark for Python or Java. Depending on the workload, these can incorporate Tasks for execution and orchestration, Streams for change tracking and stored procedures for processing logic. Snowflake Tasks execute SQL statements or stored procedures, while Streams expose change data for incremental processing.
iAURA helps interpret the source environment and translate its logic into target implementations, while validation checks whether those implementations preserve the intended results. That connection runs through the migration lifecycle, from understanding the estate to preparing reviewed and validated assets for deployment.
How iAURA Supports Migration to Snowflake
- Understanding the estate before moving it
One of the most resource-intensive parts of modernization happens before code conversion begins. Workflows, mappings, sessions, DDL statements, DML logic, procedural code and transformation rules may be distributed across several platforms and repositories. Before determining migration waves or timelines, teams need to understand how these elements connect.
iAURA Analyzer Agents parse legacy ETL and SQL code to identify objects, relationships, business logic and cross-system lineage. They analyze dependencies and object complexity and use those insights to generate knowledge graphs, flow diagrams and architecture views. - Moving from code translation to business-logic preservation
Code conversion remains one of the most time-consuming parts of modernization because different SQL dialects, procedural logic, ETL mappings and nested business rules need to be carefully re-engineered.
Using parsing logic, agentic workflows and LLMs, iAURA Code Conversion Agents analyze legacy ETL and SQL code, extract business rules and generate Snowflake-oriented implementation patterns. Depending on the workload, this can include Snowflake SQL ELT, Snowpark transformations using Python or Java and orchestration definitions using Snowflake Tasks, Streams and Stored Procedures. - Building trust into migration outcomes
iAURA supports validation by generating unit tests for SQL and Snowpark logic, checking schemas, expressions and data types and supporting parallel runs against source systems. Reconciliation at row, column and distribution levels, together with data quality and completeness metrics, provides evidence for investigating differences before cutover.
For a finance workload, for instance, even a small difference in rounding, date handling or aggregation rules could affect downstream reporting. Identifying those differences earlier helps engineering teams investigate and correct them before production migration.
The Business Impact: From Manual to Intelligent Modernization
Organizations that integrate iAURA into their Snowflake modernization programs realize these benefits:
| Outcome | Impact |
| Accelerated Time-to-Value | Achieve 50–70% faster modernization through automated assessment, code conversion, validation and agent-driven workflows. |
| Reduced Cost | Reduce modernization costs by 20–35% by limiting manual analysis, rewriting, testing and documentation effort. |
| Improved Data Trust | Automated validation, reconciliation and lineage help improve confidence in migrated data and downstream analytics. |
| Lower Migration Risk | Dependency-aware planning and consistent migration patterns reduce the risk of missed relationships, rework and cutover issues. |
| Higher Engineering Productivity | Engineers spend less time analysing legacy code and performing repetitive validation, allowing greater focus on architecture and optimisation. |
| Governance Built-In | Automatically generated lineage and documentation improve traceability and support enterprise governance requirements. |
| AI-Ready Data Foundation | Snowflake’s elastic and governed architecture provides a scalable foundation for analytics, machine learning and GenAI workloads. |
From Manual Migration to Stronger Data Foundation
The future of data modernization is not defined only by faster code conversion or lower migration costs. It is defined by intelligence embedded across the modernization lifecycle.
With iAURA 2.0 on Snowflake, Persistent is helping enterprises move from manual and uncertain migration programs to intelligent, automated and dependency-aware modernization, where legacy logic is analyzed before it is converted, migrated workloads are validated before cutover and technical knowledge is captured for future use.
With iAURA supporting modernization to Snowflake, organizations can move towards AI-ready data foundations with greater precision, transparency and confidence, setting a new benchmark for how legacy data estates can evolve.
Author’s Profile
Mandar Baxi
Associate Vice President , Technology
