Your data & AI platform, deployed with no security trade-offs
The accelerator that guarantees your project's success. We choose, design and deploy your Data Platform on Azure Databricks, Azure Data Factory, Microsoft Fabric and Azure Machine Learning Workspace — directly inside your tenant, built to best practice.
Your data & AI initiatives are only as strong as the foundations beneath them
Deploying a modern data platform inside your tenant — as an extension of their existing infrastructure — demands rare mastery of Cloud security, networking and governance. Very few firms can deploy Azure Databricks in a fully secure way.
Security first
Private connectivity, network isolation, no public endpoints: a best-practice deployment tolerates no shortcuts.
Varying maturity
Partial Landing Zone, incomplete on-premise Azure connectivity, nascent governance: every client starts from a different point.
Data & AI, one platform
A Data Platform today carries both analytical and AI workloads. The right technology choice drives both.
Visium Data Platform Foundation
An end-to-end accelerator: from assessing your infrastructure to a running, secure and governed data & AI platform — ready to deliver value.
Choose the right platform
We help you arbitrate between Azure Databricks, Microsoft Fabric, Azure Machine Learning Workspace, a combination of these services, and the rest of the Azure ecosystem — based on your goals, priorities and requirements.
Design the target architecture
Design of a reference architecture: networking, security, governance, ingestion, transformation and exposure — built to last.
Deploy in your tenant
Infrastructure-as-Code, multi-environment deployment pipelines and Git integration: a reliable extension of your IT.
Technologies at the heart of the offer
Azure Databricks
Orchestration and ingestion of your on-premise or cloud data through a wide range of connectors
Azure ML Workspace
Train, track and deploy your models: a governed, secure Azure Machine Learning workspace to industrialise your AI and MLOps workloads
Microsoft Fabric
A unified Data & AI data platform, integrated optimally by our teams inside your Azure tenant for stronger data security
Azure Databricks
Orchestration and ingestion of your on-premise or cloud data through a wide range of connectors
Everything an enterprise ready platform demands
.webp)
CI/CD pipelines for deployment across environments
Integration with your Git repositories & DBT Cloud
Secret-rotation pipelines & Key Vault
Monitoring, alerting & observability
Access controls, RBAC & identity governance
Data and AI workloads on a single foundation
An accelerator in four steps
A proven method that de-risks every decision and speeds up go-live, without sacrificing best practice.
Assessment
Maturity assessment: on-premise infrastructure, Azure connectivity, and the state and readiness of the Landing Zone for a best-practice Databricks deployment.
Design
Target data & AI architecture: technology choices, network topology, security model, governance and deployment plan.
Deploy
Deployment in your tenant as Infrastructure-as-Code, Git integration, CI/CD pipelines across environments, connectors (including DBT Cloud).
Operate
Monitoring, access controls, secret rotation and skills transfer for a durable, self-sufficient platform.
Not just Data & AI — infrastructure too
Our real differentiator: we combineData & AIexpertise with deep mastery ofinfrastructure— networking, cloud and security. Deploying Azure Databricks or connecting Microsoft Fabric in a truly secure way demands both at once, and very few firms bring these skills together.
For every brick, we choose WITH YOU the optimal solution -based on your context, priorities and requirements.
Network connectivity
ExpressRoute
VPN site-to-site
Peering
Perimeter & isolation
Hub-Spoke
Firewall
Private DNS
Private Endpoints
Managed Private Endpoints
Data & Al compute
Databricks
Fabric
Azure Data Factory
Azure ML Workspace
Data storage
OneLake (private)
Redundancy: ZRS / GRS
ADLS Gen2
Performance: tiers & cache
Secrets & encryption
Key Vault
CMK
Secret Rotation
Key Rotation
Network connectivity
Microsoft Entra ID
RBAC
Unity Catalog
Azure Policy
Monitoring & logs
What a production platform demands
Beyond infrastructure, we deliver the automation and guardrails that make the difference between a POC and an enterprise platform.
Fully secured Databricks
VNet injection, removal of public IPs, Private Link, data-plane control.
Governed Unity Catalog
Unity Catalog configured to your governance rules, with traceability of configuration changes.
Fabric network connectivity
Secure connection of Fabric (SaaS) to data sources at the network level.
Access controls & monitoring
RBAC, identity governance, logging and end-to-end monitoring.
Private OneLake & ADLS Gen2
OneLake shortcuts to storage with no public endpoint, strictly private access.
Managed private endpoints
Creation of managed private endpoints for Fabric on Azure resources.
Industrialised Fabric workspaces
Industrialised Fabric workspaces
Industrialised workspace creation, naming conventions, access management, and organisation by business domain and environment.
Key Vault, CMK & rotation
Secure connection to Key Vault, automated secret-rotation pipelines, and customer-managed keys (CMK) on storage and Databricks services — with key rotation.
From idea to AI impact
Build custom AI solutions that fit your data, workflows, and business goals.
The accelerator, concretely
Behind the offer sits areal, production-provenDatabricks accelerator. Three layers — infrastructure, Unity Catalog governance, ADF / DBT / DABs deployment pipelines — delivered as Infrastructure-as-Code and CI/CD. A new project starts from a working, secured, multi-environment platform instead of a blank slate.
› visium-dp.infra
Infrastructure as Code (IaC) in Terraform: networking, storage, Key Vault, ADF, Databricks workspace, API
› visium-dp.databricks-uc
Unity Catalog configuration as code: credentials, external locations, catalogs / schemas / volumes, grants
› visium-dp.databricks-code
DBT project code, DABs project code, and deployment pipelines for the DBT and DABs projects
› visium-dp.adf
Azure Data Factory pipeline code and its deployment
What is actually implemented
Empower organizations to innovate through AI & Data solutions
Networking, storage, Key Vault, ADF, Databricks workspace, API, Entra ID — built on Azure Verified Modules.
- Networking (subnets, NSGs, route tables), storage accounts + RBAC, Key Vaults, Azure Data Factory, Databricks workspace, an Azure Function-based API, and Microsoft Entra ID groups & role assignments.
- Built on Azure Verified Modules (AVM) — storage, Key Vault, workspace, landing-zone — not bespoke, unmaintained modules.
- One resource group per functional domain (secrets · storage · management · ingestion · processing · api · iac), consistent naming <type>-<product>-<env>.
- Diagnostic settings wired by convention to a shared Log Analytics workspace for every supported resource — observability is not an afterthought.
Unity Catalog provisioned as code
Credentials, external locations, catalogs, schemas, volumes and grants declared in tfvars — not clicked in the UI.
- Everything declared intfvars: a reproducible, auditable catalog structure across every environment.
- Guaranteed dependency chain: storage credential → external location → catalog → grants → schema → grants → volume → grants.
- Built-in Azure Monitor alert rules that page on Databricks job failures, configured per environment via tfvars.
Layered dbt transformation platform
Medallion 10_staging → 20_intermediate → 30_mart, deployed via Databricks Asset Bundles.
- Proven layering: staging / ODS (SCD Type 2 snapshots) → intermediate → mart optimised for the use case (DataHub or BI).
- Deployment via Databricks Asset Bundles (DABs) — not ad-hoc notebooks or hand-wired jobs.
- Shared, reusable logging package dbt_visium_utils (on-run-end hook) → central dbt_logs table: invocation ID, status, timing, row counts, test results. Cross-project observability with zero custom code.
CI/CD already built, not bolted on later
Azure DevOps pipelines, plan / apply split, approval gates per environment.
- Automaticplanarchived as an artifact; apply against an approval-gated Environment per target (dev-infra · qual-infra · prod-infra).
- Optional, parameter-gated security scanning: TFLint · TFSec · Trivy.
- Automatic git tagging and qual → prod promotion on the dbt / Databricks Asset Bundle projects.
- Ability to roll back to a previous version / earlier tag.
Multi-environment by construction
Dev / qual / prod: separate state, tfvars, clusters, catalogs and connections.
- Separate Terraform state per environment, separate tfvars, separate Databricks clusters & catalogs, separate service connections.
- Environment differences are isolated to configuration — never to code branches.
What makes this accelerator unique
End-to-end coverage, structural governance, shared conventions, audited modules.
- End-to-end coverage: from network to governed catalog to running transformation pipelines — the pieces fit together natively, not after the fact.
- Structural governance & observability: Unity Catalog grants, least-privilege RBAC, diagnostic settings and dbt run logging are part of the convention — no separate governance workstream.
- Structural governance & observability: Unity Catalog grants, least-privilege RBAC, diagnostic settings and dbt run logging are part of the convention — no separate governance workstream.
- Convention over reinvention: naming, {env} placeholders, schema macros and layering are consistent across all three repos.
- Audited, maintained modules(AVM) rather than custom Terraform — less maintenance, less long-term risk.
From weeks to days
A new project starts by copying the repository structure and filling in environment-specifictfvarsand profile values — architecture, naming, security posture, observability and CI/CD are already decided and battle-tested. The plumbing is solved; the team focuses immediately on business transformation logic.
From data & AI strategy through to implementation
We guide you from thestrategicframing of your data & AI initiatives through toimplementation,coaching your teamsto become self-sufficient — and we're just as comfortable speaking with yourinfrastructure and network teams.
Historically an AI-specialist company, it's this end-to-end command — business vision, technical depth and skills transfer — that turns your projects into real successes.
Data + AI
One foundation, two worlds addressed
100%
Deployed in your tenant
End-to-end
From strategy to implementation, your teams upskilled
Frequently asked questions
Everything you need to know about Visium..
How does this offer differ from Visium Data Foundation?
Visium Data Platform Foundation focuses on choosing, designing and securely deploying a data & AI platform inside your Azure tenant (Databricks, ADF, Microsoft Fabric, Azure ML Workspace) — an extension of your infrastructure, whereas the Data Foundation offer accelerates the delivery of data products.
Why is an assessment phase necessary?
Because everything depends on your starting point: do you have an Azure Landing Zone, is it fully operational, does your on-premise Azure connectivity allow a best-practice Databricks deployment? The assessment de-risks the next steps.
Do you work in our own tenant?
Yes. The offer is designed precisely to deploy infrastructure in the client's tenant, as an extension of the existing estate — with no security compromise whatsoever.
Microsoft Fabric is SaaS: is security really a concern?
Absolutely. Connecting Fabric to sources at the network level, accessing a Key Vault or an ADLS Gen2 with no public endpoint, creating managed private endpoints: all essential security skills, even in SaaS.
Do you also handle AI workloads?
Yes. A Data Platform carries both data and AI workloads. Visium addresses both and helps you choose the most suitable platforms given your goals, priorities and requirements.