ODI Modernization Powers 40x Faster Analytics on Azure Databricks

The imperative for modernization to Databricks 

A top-tier industrial manufacturer was unable to keep pace with the rising data volumes and operational demands. Their Oracle Data Integrator (ODI)-based analytics environment became increasingly difficult to scale, maintain, and modernize. Business-critical data processes were complex and difficult to manage, while delays in data availability impacted decision-making across manufacturing and supply chain operations. 

Key Challenges

• High maintenance costs: Managing and monitoring ODI-based pipelines required significant effort and resources. 
• Delayed access to insights: Outdated ETL processes prolonged report generation, impacting business decisions. 
• Limited business agility: Legacy workflows hindered the organization’s ability to respond rapidly to evolving business demands. 
• Scalability constraints: The existing environment could not efficiently support the growing requirements for AI and advanced analytics. 

To accelerate decision-making, improve business outcomes, and fast-track AI adoption, the client chose to modernize its legacy analytics platform to Azure Databricks. 

Powering enterprise-wide intelligence with Azure Databricks 

Data estate modernization empowered the manufacturer with faster, reliable access to trusted data on a scalable, AI-ready Lakehouse foundation with Databricks Unity Catalog for governance.  The transition significantly enhanced data consistency and insight availability, accelerating analytics and AI initiatives across the enterprise. With access to near real-time operational insights, teams could make quicker, more informed decisions across manufacturing, supply chain, and production planning.  

Business Benefits  

• ~40x faster time to insights: Accelerated analytics delivery and enabled faster decision-making. 

• Next-gen industrial data foundation: Organized trusted manufacturing, product, inventory and supply chain data within a governed Lakehouse, enabling faster AI innovation and decision-making. 

• Smarter inventory management: Improved timely access to inventory and operational data, enabling better planning, optimization and supply-demand responsiveness. 

• Deeper product insights: Enabled data-driven quality improvements with granular visibility and advanced analytics.

• Zero business disruption: Ensured uninterrupted business operations across all migration phases. 

• 100% business logic preservation: Went beyond traditional ‘lift and shift’, modernizing not just workloads, but also business logic. 

Accelerating the modernization journey with Impetus and Databricks 

Impetus’ data estate modernization solution, LeapLogic™ Suite, automated and accelerated the migration of ODI to Databricks Delta Live Tables (DLT), transforming legacy ETL workflows into scalable, near real-time Spark-based pipelines. Data was consolidated in Delta Lake with Unity Catalog, enabling centralized governance, streamlined orchestration, and simplified operations. 

The solution combined automated assessment, migration, validation, performance optimization, and custom PySpark frameworks to preserve business logic, meet existing SLAs, and ensure a seamless migration.  

The result: A modern, governed, and AI-ready Lakehouse environment that accelerated analytics and reduced data refresh cycles from overnight to every 15 minutes. 

Key solution highlights

Automated transformation: Converted 50+ ODI packages, along with multiple mappings and load plans into Databricks-native workflows. 

• Logic and context preservation: Recreated ODI-native processing patterns on Databricks and preserved granular product and processing logic, enhancing product insights and quality improvement. 

• Near real-time data ingestion: Used Qlik Replica on Databricks for enabling continuous data ingestion. 

• Domain-ready data products: Consolidated historical and incremental datasets within Delta Lake using a Bronze-Silver-Gold architecture, with Unity Catalog for governance. 

• Semantic foundation: Retained business rules and relationships, enabling consistent interpretation of manufacturing and supply chain data in the new environment. 

• Native orchestration: Implemented scheduling and orchestration across the Azure Databricks environment. 

• Migration validation: Leveraged a custom Spark-based framework for efficient debugging and end-to-end validation of migrated jobs.

• Functionality assurance: Addressed ODI-native functionality gaps to support workload equivalence on Databricks. 

By modernizing its legacy ODI environment on Azure Databricks, the manufacturer established a governed, AI-ready Azure Databricks foundation that supercharged analytics and elevated business outcomes. The transformation simplified operations, while making enterprise data accessible and consumable for AI systems and agents.