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Modernize Data. Scale AI. Govern Data with Confidence. 

CEI helps enterprises build governed Databricks platforms that deliver production‑ready analytics and AI. 
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The Challenges Holding Back Data and AI at Scale 

Enterprises invest heavily in data platforms, yet many struggle to turn those investments into reliable analytics and production‑ready AI. Business impact is often limited by three persistent challenges: 

Fragmented
architectures that
create silos and limit
enterprise visibility 

Inconsistent
governance that reduces trust,
control, and
compliance readiness 

Fragile pipelines that
slow analytics delivery
and introduce
operational risk 

These issues prevent data and AI initiatives from scaling beyond experimentation. 

Turning Databricks Into a Platform the Business Actually Relies On 

CEI partners with Databricks to help organizations modernize their data foundation and operationalize AI on the Databricks Data Intelligence Platform. The goal is not platform adoption alone—it is building a governed, scalable environment where data can be trusted and used confidently across the business. 

Core Databricks Service Areas 

CEI supports Databricks initiatives across the following focus areas: 

Unity Catalog & Data Governance

Implement centralized governance with secure access, lineage, and control across enterprise data and AI assets.

Lakehouse Modernization & Migration

Modernize legacy data platforms to the Databricks Lakehouse, accelerating migrations and reducing execution risk with CEI’s MigrateIQ™.

AI & Generative AI on Mosaic AI

Design and deploy production‑ready AI and GenAI solutions that integrate cleanly into enterprise systems and workflows.

Data Engineering on Delta Lake

Build reliable, high‑performance data pipelines that support scalable analytics and AI workloads.

Data Governance Advisory

Align governance policies, operating models, and controls to support trusted, enterprise‑scale data.

Why CEI for Databricks Programs 

Enterprises invest heavily in data platforms, yet many struggle to turn those investments into reliable analytics and production‑ready AI. Business impact is often limited by three persistent challenges: 

Deep experience engineering production‑grade data and AI environments 

Strong emphasis on governance, security, and operational readiness 

Proven methods for moving from pilots to enterprise‑scale execution 

Proprietary IP that accelerates modernization without introducing unnecessary risk