As organizations increasingly migrate from legacy platformsโlike on-prem SQL Server, Oracle Exadata, Teradata, Informatica, Cloudera, or Netezzaโto modern cloud architectures, one critical question often arises:
"Are we just lifting and shifting the same complexity to the cloud?"
Unfortunately, in many cases, the answer is yes.
Despite the promise of lower infrastructure cost and better performance with Lakehouse architectures, enterprises often replicate old inefficiencies, including:
Redundant data models
Siloed and overlapping ETL pipelines
Disorganized, ad-hoc reporting
Minimal data governance or lineage
These shortcomings make every new use caseโwhether AI/ML, GenAI, or predictive analyticsโa manual and expensive endeavor.
Rethinking Modernization: Start with Strategy, Not Code
Weโve learned that how you start a modernization project is just as important as the destination.
โSpend more time sharpening the axe than cutting the tree.โ
The key is restructuring your approach, focusing on reusability, automation, and semantic understanding from Day 1.
Our Modernization Playbook
1. Begin with Data Discovery & Domain Deep Dive
Extract metadata from legacy systems
Conduct POCs with SMEs across departments
Understand data dependencies and logic reuse
2. Adopt a Data Product Mindset
Treat every output (e.g. trial cohort, surgical efficiency report) as a data product
Design for outcomes, not just systems
3. Design for AI, ML & GenAI from the Start
Model clean, curated datasets
Example: An HR GenAI assistant needs unified employee info including payroll, attendance, and attrition
4. Reverse Engineer & Normalize Pipelines
Enter: Semantic Fingerprinting
Semantic Fingerprinting is a powerful way to analyze the meaning and relationships within your dataโnot just schemas or metadata. Think of it as a data DNA match for logic.
It enables you to:
Detect similar logic across disconnected systems
Uncover functionally equivalent pipelines in Informatica, Synapse, SQL, or Python
Cluster and de-duplicate overlapping views and tables
How Semantic Fingerprinting Works
It uses:
NLP on column names, comments, descriptions
Data profiling (value distribution, cardinality)
Query usage behavior (frequency, join paths)
ML-based similarity clustering
How It Modernizes the Lakehouse
1. Redundant Logic Discovery
Cluster similar tables: employee_data, emp_info, hr_employees_2020
Retire stale reports, flag orphaned data
Outcome: Simplifies your Lakehouse model
2. Auto-Term Mapping
Map dob, birth_dt, date_of_birth โ "Date of Birth"
Link synonyms like emp_id, employee_number
Outcome: Easier lineage, glossary creation, and Unity Catalog tagging
3. Accelerated Migration Planning
Prioritize most-used pipelines and strategic models
Create phased plans based on logic clusters
Outcome: Lower risk, faster value realization
4. Semantic Layer Bootstrapping
Auto-suggest metrics, dimensions, hierarchies
Enable tools like Looker, Power BI, or GenAI copilots
Outcome: GenAI-ready analytics from Day 1
5. Improved Data Classification & Security
How AI Agents Help Automate This
Agent Role
| Discovery Agent | Inventory schemas, extract lineage |
| Fingerprint Agent | Detect similar logic, classify fields |
| Model Rationalizer | Propose canonical data models |
| Pipeline Converter | Convert legacy logic into PySpark |
| Governance Agent | Auto-tag Unity Catalog, apply security |
| Copilot Agent | Answer data questions in natural language |
| Orchestration Agent | Coordinate workflows, track decisions |
Case Study: Healthcare Platform Modernization Using Semantic Fingerprinting & AI Agents
Background
A leading healthcare provider had a fragmented data ecosystem:
Oracle DB, Synapse, Informatica, SQL Server
Disconnected reports across Research, Surgical, Finance, and Trials
Each team built pipelines using the same base tablesโbut in different tools, with redundant logic and conflicting metrics.
Our AI-Powered Approach
1. Metadata Extraction from All Systems
Parsed Informatica mappings, SQL views, Synapse pipelines
Included Trials & Surgical Scheduling data
2. Legacy Code Lineage Construction
3. Code Conversion with GenAI
4. Fingerprinting for Logic Similarity
Found overlapping filters/joins (e.g., patient eligibility logic used by both Research and Surgery)
Merged these into reusable building blocks
5. Clustering by Department
Clustered assets like TrialParticipantView, PreOpDashboard, SurgicalUtilization
Mapped BI dashboards to pipeline clusters
6. Refactoring via AI Agents
Unified duplicated views used by Surgical + Research
Created modular views for patient cohorts, procedure mapping, eligibility checks
7. ETL Unification
8. Gold Layer Workflow Redesign
9. GenAI-Ready Data Models
Results
Metric Before After
| Redundant Views | 300+ | < 60 |
| ETL Pipelines | 500+ | ~40 |
| Dashboards per Workflow | 1:1 | 1:20+ |
| AI Readiness | Low | Fully Enabled |
| Data Models | Scattered | Canonical + Clean |
Example Legacy Flow: Anesthesia Department
Previously:
Separate pipelines for documentation, pre-op clearance, and vitals
Same patient info sourced via SQL in one and Informatica in another
No lineage, inconsistent outcomes
After modernization:
Unified pipeline feeds clean data to all views
Semantic fingerprinting aligned logic across teams
One source of truth powers dashboards, risk models, and GenAI copilots
Final Thoughts
Semantic Fingerprinting and AI Agents are not just accelerators โ they are enablers of a fundamentally better way to modernize.
They help organizations:
Migrate with intelligence, not brute force
Design for reuse, automation, and trust
Build Lakehouses that are AI-native, not just cloud-native
If you're planning a legacy migration, start by asking:
โHow semantically ready is our data for GenAI?โ
