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Databricks Deployment Services

Stage: Services

Deploy Lakehouse, Unity Catalog, and MLflow the right way—secure, governable, and cost-aware from day one.

10 min readLast updated: Q3 2026

Key Takeaways

Databricks Ecosystem
Workspace & Terraform Foundations
Assess & Blueprint Architecture

§01Technology Stack

Databricks Ecosystem

Workspaces Unity Catalog Delta Live Tables MLflow Photon

Infrastructure / DevOps

Terraform GitHub Actions

Data Sharing

Delta Sharing

Data Governance / Privacy

Immuta Privacera

§02Databricks, Production-Grade

Workspace & Terraform Foundations

Tooling rail: workspaces • SCIM • Terraform modules Edge: Environment parity and repeatable provisioning.

Unity Catalog & Security

Tooling rail: UC • metastore • ABAC/RBAC • data masking Edge: Policy-as-code with auditable access tiers.

Delta Lake & DLT Pipelines

Tooling rail: Delta • DLT • expectations • medallion Edge: SLA’d bronze/silver/gold with observability hooks.

MLflow & Feature Stores

Tooling rail: MLflow registry • feature store • serving Edge: Qμβrix™ sets latency/cost/quality SLOs for LLM/ML.

Cost Governance & FinOps

Tooling rail: cluster policies • serverless • spot policies Edge: Budget caps, auto-shutdowns, and usage telemetry.

BI & Sharing Integration

Tooling rail: SQL endpoints • Delta Sharing • semantic layers Edge: Certified datasets for Power BI/Tableau/Looker.

§03Methodology

Assess & Blueprint Architecture

Target lakehouse in 2–3 weeks

Build & Secure Governance

UC + pipelines + policies live.

Operationalize FinOps

Runbooks, SLOs, cost dashboards

§04Lakehouse with guardrails

Unity Catalog and policy-as-code
DLT pipelines with expectations
MLflow registry & feature stores
Cost policies and budgets enforced
Certified BI integration

§05Assurance Strip

Assurance Strip

UC: Use Case documentation capturing objectives, requirements, and expected outcomes for AI initiatives
Lineage: Tracking the origin, transformations, and flow of data and models to ensure transparency and auditability
SLOs: Service-level objectives defining operational performance targets and reliability standards for AI systems
FinOps: Implementing financial operations practices to control costs, optimize resources, and maintain budget accountability

Boardroom Kit

UC Hardening Guide: Best practices for securing, optimizing, and validating AI use cases to ensure reliability and compliance
DLT Expectations Pack: Defined standards and guidelines for distributed ledger technology usage, performance, and governance
MLflow Registry SOP: Standard operating procedures for registering, versioning, and managing machine learning models in MLflow
FinOps Policy Set: Comprehensive policy framework to govern financial operations, cost optimization, and cloud resource usage

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