From Legacy Silos to an Industrial Digital Thread with Azure Databricks

6 months
for end-to-end modernization

2x
performance improvement

Powering AI
& predictive analytics with Databricks Lakehouse

Business context
connected across enterprise domains

What Drove the Move to Databricks

A Fortune 200 global power technology leader operates a mission-critical reliability ecosystem that underpins decision-making across supply chain, manufacturing, and service operations. As workloads grew in volume and complexity, the company’s on-premises data estate, built on legacy platforms like Oracle Exadata, Informatica, ODI, and AppWorx, became a massive bottleneck.

Multiple pressures converged, making modernization non-negotiable:

Impending Oracle Exadata license expiry created immediate migration urgency
Escalating data warehouse and ETL overhead costs across eroded IT budgets
On-premises infrastructure limitations restricted scalability, innovation, and responsiveness
Rigid ETL and orchestration frameworks blocked adoption of advanced analytics and AI
Fragmented data systems delayed insights and slowed business responsiveness
Fragmented domain data prevented 360-degree visibility across products, parts, materials, suppliers, plants, assets, and service operations.

The organization needed to quickly move to a future-ready platform that would unlock AI and advanced analytics use cases—without disrupting ongoing operations.

A snapshot of the client’s current and future technology stack

Unified EDW, ETL, and orchestration on Azure Databricks, replacing a fragmented legacy estate with a single, scalable, AI-ready Lakehouse.

Powering a connected, AI-ready data foundation on Azure Databricks

Partnering with Databricks and Impetus enabled the client to transition from a maintenance-heavy, on-premises data estate to a scalable industrial digital thread—delivering measurable performance improvements, reducing costs, and elevating business outcomes.

Business benefits:

Estate consolidation: Replaced multiple legacy and niche tools with a single Azure Databricks platform, simplifying the technology landscape and lowering total cost of ownership.

Massive cost savings: Sunset Oracle Exadata, Informatica, ODI, and AppWorx licenses, decommissioned on-prem infrastructure, and eliminated escalating overhead costs.

2x performance improvement: Delivered a significant performance uplift on Databricks, enabling faster, smarter reliability operations across the enterprise.

45% faster batch processing: Reduced end-to-end execution window from 4.5 hours to 2.5 hours, accelerating data availability for analytics and reporting teams.

Production continuity: Modernized mission-critical workloads without disrupting core service processes, while preserving 100% business logic and meeting operational SLAs.

AI-readiness: Established a future-proof Lakehouse platform powering AI innovation and predictive analytics across supply chain, manufacturing, and service operations.

Governed, context-rich data foundation: Created a lineage-aware foundation for forecasting, quality intelligence, anomaly detection, manufacturing optimization, and agentic AI governance.

Reduced technical debt: Eliminated redundant workflows and orchestration jobs, simplifying the estate and enabling engineers to focus on high-value tasks.

Retired Oracle and Informatica in just 6 months, delivering 2x performance and 45% faster batch processing

Enabling fast, risk-free modernization with Impetus & Databricks

Impetus’ ontology-led modernization solution, LeapLogic™ Suite accelerated the end-to-end transformation journey by automatically converting Oracle Exadata SQL, Informatica mappings, and ODI workflows into Databricks-native equivalents. This dramatically reduced manual effort and compressed the overall migration timeline. Rather than a mechanical lift-and-shift, modernization involved strategic re-platforming, with every workload assessed, optimized, and validated for cloud-native performance and governance.

Key solution highlights:

Intelligent inventory assessment: Identified legacy jobs along with dependencies and provided actionable recommendations to optimize complex ingestion pipelines on Databricks.
Accelerated migration at enterprise-scale: Auto-converted 395+ Oracle procedures, 545+ tables, 170+ Informatica workflows, 90+ AppWorx chains and multiple ODI jobs to Databricks-native equivalents.
Unified Delta Lake foundation: Replaced Oracle Exadata with a scalable, ACID-compliant storage layer, consolidating all migrated EDW objects and materialized views into a single trusted source.
Cloud-native orchestration: Redesigned master orchestration workflows for Databricks, enabling end-to-end execution of all migrated jobs in a single run.
Cost-efficient compute: Leveraged Databricks Cluster Pools to streamline DBU consumption across development, testing, and production, eliminating resource wastage.
Enterprise-grade governance: Established centralized security, access control, and lineage across all migrated workloads on Unity Catalog.
Automated CI/CD pipelines: Integrated Databricks Asset Bundles seamlessly with the existing DevOps framework to enable faster, automated releases.
Industrial digital thread: Established connected business context across products, parts, materials, suppliers, plants, assets, and service operations for holistic intelligence.
Reliability intelligence: Preserved equipment, maintenance, failure and service logic to support predictive maintenance, reliability analysis, and more proactive service operations.
Operational semantics: Harmonized KPIs, calculations and business rules across domains, reducing inconsistent interpretation between enterprise teams.

From a siloed legacy estate to a future-ready architecture on Azure Databricks

Modernization empowered the power solutions leader with a unified platform that was optimized, governed, and production-ready from day one. The transition equipped them with the new-age data foundation needed to accelerate supply chain decisions, improve manufacturing efficiency, drive agentic AI innovation, and scale business growth.