AI Reverse Engineering Unlocks SQL to Redshift Migration

Client's Problem Statement

A system integrator headquartered in Brazil was engaged by a financial services institution to modernize their legacy SQL Server 2015 data warehouse to AWS Redshift as part of a cloud transformation initiative. The project stalled before any migration work could begin. Over years of operation, the data warehouse had accumulated a complex estate of stored procedures, views, ETL scripts, and data models built by engineers who had long since departed. Documentation was either incomplete, outdated, or nonexistent. The SI's modernization team could not determine what data existed, how it was structured, what business logic was embedded in hundreds of SQL scripts, or how objects depended on each other. Without this foundational understanding, they could not define a migration roadmap, produce credible effort estimates, identify required skills, plan migration waves, or anticipate potential bottlenecks such as complex T-SQL constructs without Redshift equivalents or tightly coupled stored procedure chains. Traditionally, this type of reverse engineering and discovery exercise would require 6 to 18 weeks of manual effort involving 5 or more specialized business analysts, data engineers, and architects. At typical blended rates for this level of expertise, that translates to roughly $150K to $300K in discovery phase costs alone before any actual migration work begins. The SI did not have this time or internal expertise, and was at risk of losing the engagement. Every week of delay increased the cost of the eventual migration and pushed the financial client's modernization timeline further behind schedule.

Our Solution Approach

Source-connected discovery establishing secure, read-only access to the SQL Server 2015 environment and performing automated extraction of all database objects including tables, views, stored procedures, functions, indexes, constraints, and triggers with full metadata capture

AI-powered system deconstruction parsing and semantically analyzing every SQL object to extract embedded business logic, transformation rules, data flow patterns, and inter-object dependencies at scale

Automated data model reconstruction generating entity-relationship diagrams, schema documentation, and data dictionaries directly from the live database without dependency on tribal knowledge or departed SMEs

Object-level complexity scoring assessing every stored procedure, view, and script for migration complexity based on T-SQL constructs used, Redshift compatibility, data volumes, and interdependencies with specific rationale for each score

T-SQL to Redshift compatibility analysis performing object-by-object assessment of SQL Server constructs against Redshift SQL capabilities, flagging incompatibilities, and recommending specific workarounds and refactoring strategies

Lineage and dependency mapping producing visual and tabular maps of data flows from source tables through transformation layers to final reporting objects, revealing hidden dependencies critical for migration wave planning

Fact-based migration roadmap and effort estimation generating a phased migration approach organized into waves based on dependency chains, complexity gradients, and business priority with bottom-up effort estimates driven by actual object complexity

Skills and team structure recommendation specifying which roles (Redshift architects, SQL conversion engineers, ETL specialists, QA leads), skill levels, and team composition needed to execute each migration phase

Risk and bottleneck identification surfacing potential migration blockers including unsupported T-SQL features, high-complexity procedure chains, large-volume tables with performance implications, and tightly coupled dependencies with mitigation strategies for each

Refactoring and optimization recommendations identifying opportunities to leverage Redshift-native features including distribution key strategies, sort key optimization, and materialized view opportunities to improve performance beyond a lift-and-shift

How We Implemented

Days 1-2 (Discovery and Connection): Deployed a lean expert team within 48 hours of engagement. Established secure, read-only access to the SQL Server 2015 environment. Automated extraction of all database objects including tables, views, stored procedures, functions, indexes, constraints, and triggers. Catalogued the complete data estate including schema structures, data types, row counts, and storage footprints. Identified all SQL scripts, ETL jobs, and scheduled processes within the environment.

Days 3-6 (Intelligent Analysis and Deconstruction): AI-powered parsing and semantic analysis of every SQL object to extract embedded business logic, transformation rules, and data flow patterns. Automated lineage mapping from source tables through transformation layers to final reporting objects. Complexity scoring of each object based on T-SQL constructs, Redshift compatibility, data volumes, and interdependencies. Identification of refactoring opportunities including deprecated T-SQL patterns, performance anti-patterns, and SQL constructs without direct Redshift equivalents. Automated data model reconstruction generating ERDs and schema documentation from the live database. Gap analysis between SQL Server 2015 capabilities and AWS Redshift feature set with potential migration risks flagged.

Days 7-8 (Migration Canvas Delivery): Delivered comprehensive Migration Canvas covering full source system landscape, object inventory, complexity analysis, and migration strategy. Fact-based effort estimates for each migration wave grounded in real object complexity and dependency analysis. Detailed skills matrix specifying exact roles, skill levels, and duration needed. Phased migration roadmap with recommended wave sequencing based on dependency chains, business criticality, and complexity gradients. Risk register identifying potential migration bottlenecks with mitigation strategies. Refactoring recommendations for Redshift performance optimization. Knowledge transfer session with the SI's modernization team to walk through findings and enable independent execution.

Conclusion

The Brazilian system integrator went from a stalled engagement with no path forward to having a fully documented, fact-based migration plan in 8 business days. The Migration Canvas was built entirely from direct system analysis of the actual SQL Server 2015 environment, giving the SI the concrete foundation they needed to staff the project, plan migration waves, anticipate bottlenecks, and present a credible strategy to their financial services client. Traditional reverse engineering would have required 6 to 18 weeks and 5+ specialized resources at an estimated cost of $150K to $300K. The 3X engagement replaced that timeline with an 8-day sprint that produced more consistent and comprehensive output, with zero dependency on tribal knowledge or legacy SMEs who were no longer available. Beyond the direct cost and time savings on the discovery phase, the accelerated timeline prevented further schedule slippage on the broader Redshift migration program, avoided continued burn of an idle modernization team, and preserved the SI's client relationship. The SI's team received a complete knowledge transfer and continues to reference the Migration Canvas as their primary planning artifact as the migration moves through execution phases.