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Services / Data platforms

Governed data platforms on Azure and Databricks.

Data platforms that carry an SLA: private, governed, the same code in every environment, handed over to your team.

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01 / Problems

Problems we solve

These are the situations that bring organisations to us.

  • Every team deploys differentlyProduct teams ship pipelines to one platform without a common deployment standard, so every release is a negotiation.
  • Sensitive data, strict rulesData classification, sovereignty and audit teams need continuous evidence that the platform follows policy.
  • Data split across worldsOperational data lives partly on premises and partly on Azure, and nothing joins it up.
  • A migration that cannot break reportingDecades of batch logic and regulatory reporting have to keep working while the platform underneath changes.

02 / Approach

How we deliver

The delivery standards we apply to every platform.

  • Secure and private by designPrivate endpoints, no public network paths, managed identities and secrets in Key Vault from the first resource.
  • Governed accessEntra ID groups and PIM for people. Unity Catalog reader and contributor groups for every schema, defined in Terraform.
  • DTAP as codeDev, test, acceptance and production run the same Terraform modules and Asset Bundles, deployed only by pipelines with build validation and code scanning.
  • HandoverA documented architecture and run book, plus knowledge transfer, so your team can own and evolve the platform.

Databricks

Databricks engineering, as a partner

Databricks is where most of our platform work runs. We are a Databricks partner, and the credentials are listed on our About page.

  • Databricks Asset Bundles as one deployment standard, released only by pipelines
  • Unity Catalog access per schema through Entra ID groups, with lineage
  • Delta Lake and Lakeflow pipelines, batch and streaming
  • Security with the Security Analysis Tool and a review of cost and usage

Already on Databricks? The health check shows where to improve first.

See the credentials

databricks.ymlIllustration
# One bundle: the same code in every environment
bundle:
  name: data-platform
targets:
  dev:
    mode: development
  test:
    mode: production
  acc:
    mode: production
  prod:
    mode: production
# Released only by the pipeline, after build validation
  1. Source systemsOn premises and Azure
  2. PipelinesBatch and streaming

  3. Governed by Unity Catalog

    1. LakehouseDelta Lake
    2. Access per schemaEntra ID groups
  4. Reports, AI and apps
  • Databricks
  • Unity Catalog
  • Delta Lake
  • Azure
  • Terraform
  • Microsoft Entra ID
  • Azure Key Vault

Governance for AI

One gateway for every model, agent and tool

  1. Teams, agents, coding toolsWhoever calls a model
  2. Unity AI GatewayPermissions, budgets, limits, policies, logging
  3. Models, MCP servers, toolsDatabricks-hosted and external
  • AccessUnity Catalog privileges decide who may use what.
  • Budgets and limitsSpend and request rates have a cap per use case.
  • PoliciesRules apply to requests and responses.
  • LoggingEvery call lands in Delta tables for audit.
  • RoutingTraffic splits across models, with failover.

How we set it up

  • The gateway and its policies are defined as code and deployed by pipeline, never clicked together.
  • One service per use case, each with its own budget and an owner who answers for it.
  • Inference tables with a retention period and access groups, so logs are useful and not a liability.
  • System-table dashboards for tokens, cost and latency per team.
  • Coding agents such as Claude Code are routed through the gateway, with repository rules and review gates on top.
  • Fallbacks are tested before go-live, by switching a destination off on purpose.
  • Smart Routing stays out of production while it is in Beta.

Generally available. Smart Routing is in Beta. Last verified: 10 November 2026. Unity AI Gateway documentation

  • Databricks
  • Unity Catalog
  • Unity AI Gateway
  • Delta Lake

Apps and analytics

OLTP, OLAP and Lakebase

Applications need a transactional database and analytics needs a lakehouse. We explain the difference, what Lakebase is and how we engineer one platform for both.

OLTP, OLAP and Lakebase in depth

Deliverables

What you get and how to start

Each starts with a conversation about your scope.

Typical duration: 3 days

Databricks health check

We check cost, security with the Security Analysis Tool, governance with Unity Catalog and delivery with Asset Bundles and CI/CD, and give you a prioritised fix list.

What you get

  • Cost and usage review
  • Security and governance findings
  • Delivery and CI/CD review
  • Prioritised fix list with effort
Ask for a quote

Typical duration: 4 to 8 weeks

Azure data platform foundation

Landing zone, Databricks, Unity Catalog, CI/CD and Terraform modules, delivered secure by design.

What you get

  • Landing zone and network with private endpoints
  • Workspace, catalogue and access groups
  • CI/CD with build validation
  • First pipelines in production
Ask for a quote

All engagement models

Proof

Related case studies

Outcomes are quoted from our case studies. Your result depends on your data, your teams and your starting point.

All projects

Knowledge

Related articles

Written by the engineers who build these systems.

All articles

Free check

Take the check

A score from 0 to 100 and a next step, in about three minutes.

All six checks

FAQ

Questions about this service

Which platform do you build on?

Databricks on Azure. One platform for data engineering, analytics, machine learning and AI, governed in Unity Catalog, with Power BI and the rest of your Microsoft estate on top. Our references and credentials are all on Databricks.

How long does a foundation take?

Typically 4 to 8 weeks to the first pipelines in production. The plan follows a first conversation about your scope.

We already have a platform. Where do we start?

With the Databricks health check: 3 days of review across cost, security, governance and delivery, ending in a prioritised fix list.

Who owns the platform afterwards?

You do. Infrastructure and pipelines are code in your repositories, documented and handed over with knowledge transfer.

Next step

Which service fits your problem?

Tell us what you run today and what you want to change. We will say which service line fits, or that none does.

Talk to an engineerSend a messageor email info@nexops.nl directly

Currently accepting projects for Q4 2026