Retire legacy Oracle EDW, Exadata and ADW — with their Informatica and PL/SQL estates — and re-platform onto a governed Databricks Lakehouse. Accelerator-led, GenAI-powered, validation-first.
Legacy Oracle warehouses were built for a different era. Cost, scalability and AI-readiness are now the constraints — not the capabilities.
High Oracle / OCI licensing, compute and Informatica maintenance — costly to run and hard to justify as data volumes grow.
Non-scalable, tightly-coupled architectures throttle enterprise reporting and can't absorb new data or workloads.
Business logic buried in PL/SQL, views and thousands of ETL mappings — brittle, SME-dependent and slow to change.
Siloed, ungoverned data can't feed modern BI, GenAI or ML — the platform blocks the AI roadmap instead of enabling it.
Outcomes from real Oracle → Databricks programs — automation that shows up on the balance sheet and the timeline.
A proven, accelerator-led path from legacy Oracle to a governed Lakehouse — de-risked at every stage.
Profile the Oracle source & dependencies with the KPI Assessment Module.
Scope what to migrate and how it fits the Databricks platform.
Objects, data & security — historical + incremental sync.
ETL/ELT → notebooks; jobs → Databricks Workflows.
Source-to-target reconciliation via the KPI Data Validator.
Repoint BI; expose Gold to MLflow & AI/ML workloads.
Our standard migration pattern — KPI accelerators mapped to each stage. Source-agnostic; shown for Oracle.
Oracle EDW / ADW / CDW · Exadata · Informatica / PL-SQL · OBIEE / OAC
Data Platform Migration Accelerator (CodeGPT AI Toolkit) · KDIF · Autoloader · CDC
Medallion — Bronze / Silver / Gold on Delta, governed by Unity Catalog & orchestrated by Workflows.
Power BI / AI-BI · MLflow · Mosaic AI · Databricks Genie
A stack of proprietary, GenAI-powered, Databricks-native accelerators — the engine behind every migration.
GenAI code & data migration (CodeGPT AI Toolkit) — Oracle, Exadata, ADW & Informatica → Databricks SQL.
Metadata-driven ingestion & PySpark pipeline generation for repeatable, governed data loads.
Source-to-target reconciliation, data quality & observability — continuous QA across the pipeline.
Cluster & cost optimization — FinOps for the Lakehouse, right-sizing compute and spend.
Prebuilt HR / ERP data products & common models — accelerating downstream analytics.
Governance, lineage & security across every domain — trusted, compliant data access.
Three production migrations — quantified results and execution excellence. Anonymized by industry.
High OCI cost, hard-to-maintain pipelines and limited scalability on Oracle Autonomous Data Warehouse.
Phased, accelerator-led migration with GenAI & metadata-driven frameworks; Oracle Sales Cloud & Eloqua ingested into a governed Bronze/Silver/Gold Lakehouse.
Census & DEI logic embedded in Oracle views and stored procedures; siloed HR data; a slow, manual month-end.
Re-engineered Oracle logic as PySpark; a common HR model via ERP Analytics for Workday + KDIF; automated month-end and retired Oracle EDW, Informatica & AAS.
A non-scalable Oracle warehouse and 8,000 Informatica mappings under aggressive timelines and a governance mandate.
Phased "Quick Win" migration with the Data Platform Migration Accelerator + Data Validator; 8,000 mappings → Databricks SQL on a Unity Catalog-governed AWS Lakehouse.
Let's scope a Quick-Win pilot on your estate — and prove the accelerated path in weeks, not years.