Data Engineering

Build the Data
Foundation Your
AI Strategy Needs.

Modernize fragmented data estates into governed, AI-ready Lakehouse platforms that power analytics, GenAI, ML, and agentic workflows.

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CHALLENGES

AI ambition fails
when data foundations are weak.

Common executive pain points 

Data is scattered

Critical data sits across warehouses, lakes, applications, and legacy platforms.

Costs are rising

Multiple platforms create duplicated spend and operational drag.

Teams lack one source of truth

Analytics, AI, and business teams work from inconsistent datasets.

Governance is fragmented

Lineage, access, quality, and compliance are managed system by system.

AI is blocked

Models and agents cannot perform reliably on stale, incomplete, or ungoverned data.

SOLUTION

SourceFuse consolidates and modernizes enterprise data into a governed Lakehouse foundation.

We deliver 

Lakehouse modernization

Migrate from legacy warehouses, data lakes, and fragmented stacks to Databricks Lakehouse.

Modern data pipelines

Batch and real-time pipelines for structured, semi-structured, and unstructured data.

Governed architecture

Unity Catalog for lineage, access control, audit, and compliance.

AI-ready foundation

Data architecture built for analytics, MLflow, Feature Store, Vector Search, RAG, and GenAI.

Accelerated cloud setup

Secure, repeatable infrastructure using SourceFuse ARC IaC patterns.

Business Impact

Lower complexity

Replace disconnected data systems with one governed foundation.

Improved data trust

Create consistent, certified datasets across the enterprise.

Faster AI execution

Give AI teams clean, governed, production-ready data.

Reduced operating cost

Consolidate platforms and reduce duplicated data movement.

Stronger compliance

Centralize lineage, auditability, and access control.

customer stories

From fragmented data to measurable outcomes.

Technology, Information & Media

Situation

A global music streaming platform's SQL Server data layer couldn't scale elastically as API traffic, daily releases, and reporting loads surged.

Read Story

Outcome

  • 10x ingestion scalability, throughput up from 2M to 20M+ tracks per day
  • 33% reduction in annual database operating costs by decoupling compute from storage
  • Re-architected SQL Server to Amazon Aurora PostgreSQL Serverless via AWS SCT and DMS with CDC, zero-disruption cutover, ~2,000 queries rewritten
  • ETL re-engineered from SQL Server Agent jobs to serverless AWS Glue pipelines
  • Independent read/write scaling (10K reads/sec reporting) with 25% less DevOps friction via CloudFormation IaC
Technology, Information & Media