New engineers join mid-modernisation and slow delivery velocity
Without structured onboarding, new team members spend weeks getting productive on Delta Lake and Unity Catalog conventions, directly blocking sprint delivery.
Data Platform Enablement
Databricks Modernization
Accelerate Databricks adoption with MCT-led enablement covering Delta Lake production patterns, Unity Catalog governance, DP-750 certification, and Databricks ML and GenAI engineering programs.
DP-750 Certification ReadinessMCT-Led DeliveryDelta Lake & Unity CatalogDatabricks ML & MLflowGenAI on Databricks
Why This Matters Now
The challenges that bring enterprise teams to this conversation
Teams know Spark conceptually but struggle with Delta Lake production patterns
Conceptual Spark knowledge does not translate to Delta Lake SCD handling, change data capture, MLflow model management, or Unity Catalog governance in production.
GenAI roadmap exists but no team can connect it to Databricks ML
Data teams with a Databricks platform cannot execute GenAI use cases without specific RAG, LangChain, and Databricks Model Serving enablement beyond core data engineering.
Strategic context: Databricks is how modern data teams build AI-ready data products — the enablement gap is the only thing that slows platform adoption after the infrastructure is in place.
Capability Coverage
What the program covers
DP-750 Certification Readiness
Structured preparation for the Azure Databricks Data Engineer Associate (DP-750) exam with hands-on lab coverage of Delta Lake patterns and Unity Catalog governance.
Delta Lake, Medallion Architecture & Unity Catalog
Implement production Delta Lake patterns including SCD handling, CDF, and Unity Catalog governance for enterprise data mesh architectures and cross-workspace data sharing.
DLT Pipelines & Databricks Workflows
Design declarative Delta Live Tables pipelines, Databricks Workflows orchestration, and Autoloader patterns for reliable, observable data ingestion at enterprise scale.
Databricks ML & MLflow Production
Build end-to-end ML pipelines with MLflow experiment tracking, model registry, Feature Store, and Databricks Model Serving for reliable production ML deployment.
Generative AI on Databricks
Implement RAG pipelines, LangChain orchestration, LLM fine-tuning, and AI Playground workflows on Databricks with Vector Search and Model Serving integration.
Databricks SQL & Analytics Engineering
Configure Databricks SQL warehouses, dashboard development, query performance tuning, and semantic layer integration for BI teams and analytics engineering workflows.
Delivery Approach
How we deliver this
01
Assess
Platform Adoption & Skills Audit
Evaluate current Databricks adoption maturity, identify Delta Lake and Unity Catalog gaps, and assess team skill levels across data engineering, ML, and analytics roles.
02
Design
Concurrent Onboarding & Specialisation Tracks
Design role-split tracks: data engineering onboarding (Delta Lake, DLT, DP-750), ML engineering (MLflow, Model Serving), and GenAI (RAG, LangChain, Databricks AI).
03
Enable
DP-750 + Custom Program Delivery
Deliver DP-750 certification preparation, custom data engineering programs, and role-specific Databricks labs with pipeline-specific hands-on exercises and production data.
04
Adopt
Platform Conventions & Unity Catalog Governance
Close with shared workspace naming conventions, Unity Catalog governance configuration, deployment pipeline standards, and team runbooks for sustained platform operation.
Capability Programs
Programs for this area
Proof & Perspectives
Implementation evidence and strategic context
Representative Enterprise Banking Team
Data teams were moving from legacy reporting platforms to Azure-centric analytics environments, with uneven readiness across architecture, engineering, and reporting roles.
Azure Data ServicesDatabricksMicrosoft Fabric
Read engagement detailsAI & Data8 min read
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Retrieval-Augmented Generation (RAG) has become the dominant pattern for grounding enterprise LLM applications in proprietary data. Yet most organisations underestimate the architecture decisions required to move from a working demo to a production system that is accurate, cost-controlled, and auditable.
RAGAzure OpenAIAzure AI SearchLLM
Read insightReady to Begin
Start your Databricks Modernization program
Work with our team to design an enablement program matched to your team's readiness, platform priorities, and delivery timeline.
DP-750 Certification Readiness
MCT-Led Delivery
Delta Lake & Unity Catalog
Databricks ML & MLflow
