Database Modernization

Modernize the Data Layer Holding You Back.

Move from legacy database dependency to a faster, validated, AI-ready data foundation that reduces licensing pressure and supports future growth.

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Your database
should power the business, not trap the budget.

Common executive pain points 

Rising licensing costs

SQL Server and legacy database renewals keep compounding, even when workloads do not grow.

Database lock-in

Critical business logic is often trapped inside schemas, stored procedures, and platform-specific dependencies.

Migration risk

Teams hesitate to move because one missed dependency can break reporting, applications, or core business workflows.

Slow assessment cycles

Manual schema reviews, dependency mapping, and conversion planning can take months before migration even begins.

Performance constraints

Legacy database environments can limit scalability, responsiveness, and the ability to support modern workloads.

AI readiness gaps

AI, analytics, and agentic workflows need clean, governed, cloud-ready data foundations to perform reliably.

SourceFuse modernizes legacy databases into validated, cloud-ready data foundations built for lower cost, stronger control, and future AI use cases.

We deliver 

SQL Server to PostgreSQL / Aurora modernization

Move from legacy SQL Server dependency to open-source, cloud-native database foundations designed for scale and flexibility.

Database assessment

Analyze schemas, stored procedures, queries, dependencies, and business logic to create a clear migration roadmap.

Dependency-aware planning

Identify application, reporting, and workflow dependencies before migration to reduce cutover risk.

Deterministic conversion

Convert database logic with structured guardrails, transparency, and human oversight — not black-box automation.

Automated validation

Use functional equivalence testing, data integrity checks, and unit tests to build confidence before production cutover.

Performance optimization

Tune the modernized database layer for scale, responsiveness, and cloud-native performance.

AI-ready data foundation

Prepare the data layer for analytics, GenAI, agentic workflows, and future intelligence use cases.

Business Impact

Lower database TCO

Reduce licensing pressure by moving from legacy database dependency to cloud-native, open-source alternatives.

Faster migration timelines

Accelerate assessment, conversion, and validation with ArcDBMigrate.

Reduced cutover risk

Validate dependencies, business logic, and data integrity before production migration.

Improved platform flexibility

Move away from platform-specific constraints and build a database foundation that can scale with business needs.

Stronger trust in migration outcomes

Use automated validation and human oversight to improve confidence before go-live.

AI-ready data layer

Create a cleaner, more modern foundation for analytics, GenAI, automation, and intelligent workflows.

customer stories

From fragmented data to measurable outcomes.

Healthcare / Diagnostics

Situation

A diagnostics lab's call center generated 100,000+ calls/month, but recordings sat unstructured in storage with no pipeline to turn them into queryable, analyzable data.

Read Story

Outcome

  • Built an automated data pipeline converting 100% of call recordings into structured, searchable text (AWS Transcribe with speaker diarization)
  • Moved from manual review of 1–2% of calls to automated analysis of every conversation
  • Structured sentiment, category, and keyword datasets surfaced via Amazon QuickSight dashboards
  • Serverless, event-driven processing (AWS Lambda) with pay-per-usage, zero infrastructure to manage
  • Eliminated manual logging errors and reduced call-center operational costs
Healthcare / Diagnostics
Ethics & Legal Compliance Education

Situation

Fragmented data across MongoDB, PostgreSQL, Salesforce, and multiple APIs, with manual JavaScript transformations, blocked reliable multi-region reporting for 30M+ learners.

Read Story

Outcome

  • Migrated disparate sources (MongoDB, PostgreSQL, third-party APIs) into a centralized, encrypted AWS Data Lake
  • Data refresh accelerated from daily to hourly via unified datamarts, enabling real-time visualization
  • Full CDC (change data capture) and incremental loading automate ongoing data flow with exception handling
  • Row-level authentication, PII protection, and VPC/KMS security for controlled cross-region access
  • Reliable CI/CD pipeline plus Amazon QuickSight dashboards delivering ML-driven benchmarking insights
Ethics & Legal Compliance Education