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Data and AI foundations

Build an Azure data and AI foundation around governed data products.

We select and integrate Microsoft Fabric, Azure Databricks, Azure data services, and Microsoft Foundry according to workload, governance, networking, lifecycle, and team requirements. This service establishes the Azure platform and data foundation; Sense AI covers business-workflow design, copilots, agents, and product experience built on top.

Scope this Azure workSee technical scope

Data and AI platform

Data and AI foundations technical model
  1. Experiences and decisionsBI · applications · governed AI
  2. Semantic and model layerMetrics · features · models
  3. Curated data productsQuality · contracts · ownership
  4. Lakehouse and operational storesOpen formats · serving stores
  5. Ingestion and integrationBatch · stream · API
SaaSOperational systemsFilesEvents
Reference pattern, adapted during design.
Selection
Workload-ledFabric, Databricks, Azure services, and Foundry are compared by need.
Data layer
ADLS and OneLakeStorage boundaries follow the selected architecture.
Lifecycle
DataOps to LLMOpsRelease, evaluation, telemetry, and rollback are designed in.

Foundation scope

The foundation connects ingestion, storage, processing, governance, and AI delivery.

The target avoids forcing every workload into one analytics product.

01

Ingestion and storage

Design batch, streaming, CDC, file, and API ingestion into ADLS Gen2 or OneLake with partitioning, retention, encryption, and data-quality controls.

02

Transformation and serving

Select lakehouse, warehouse, stream processing, database, and semantic approaches for workload latency, scale, concurrency, and skills.

03

Governance

Define catalogue, lineage, classification, access, stewardship, and retention integration using Microsoft Purview and platform-native controls.

04

AI engineering

Establish model endpoints, retrieval infrastructure, evaluation services, deployment controls, observability, and human-oversight foundations. Workflow-specific copilots and agents are scoped through Sense AI.

Platform selection

Data and AI services are selected by workload characteristics.

A solution can combine platforms where ownership and data movement remain clear.

Microsoft Fabric

  • OneLake
  • Data Factory
  • Lakehouse
  • Warehouse
  • Real-Time Intelligence
  • Power BI

Azure Databricks

  • Lakehouse
  • Spark
  • Delta Lake
  • Unity Catalog
  • MLflow
  • Model Serving

Azure data services

  • ADLS Gen2
  • Azure SQL
  • Cosmos DB
  • Event Hubs
  • Stream Analytics
  • Azure Data Factory

Microsoft Foundry

  • Model catalogue
  • Prompt flow
  • Evaluations
  • Agents
  • Content Safety
  • Azure AI Search

Product lifecycle

Data products and AI workloads share controlled delivery foundations.

Quality, access, telemetry, and lifecycle requirements are established before production use.

  1. 01

    Profile and classify

    Confirm sources, owners, sensitivity, quality, volume, velocity, retention, and approved uses.

  2. 02

    Engineer

    Build ingestion, transformation, tests, lineage, access, network controls, and environment promotion.

  3. 03

    Validate

    Test data quality, performance, cost, model or prompt behavior, security, and business acceptance.

  4. 04

    Run and improve

    Monitor pipelines, freshness, quality, drift, model behavior, token use, latency, cost, and incidents through DataOps, MLOps, or LLMOps routines.

Foundation assets

The output joins architecture with deployable data and AI controls.

Artifacts reflect the selected platforms and included use cases.

  1. 01

    Reference architecture

    Source flows, storage zones, processing, serving, AI services, identities, private networking, telemetry, and ownership.

  2. 02

    Platform baseline

    Infrastructure code, workspace configuration, policies, private endpoints, diagnostic settings, and CI/CD integration.

  3. 03

    Data product template

    Contract, schema, quality checks, lineage, access, deployment stages, service measures, and support ownership.

  4. 04

    AI lifecycle template

    Model and prompt versions, evaluations, approvals, deployment, telemetry, rollback, and human escalation.

Foundation fit

A data and AI foundation starts with named products and access constraints.

Platform selection is completed before build commitments are made.

Questions to answer before scoping

  1. Which workloads favor Fabric, Databricks, or Azure-native services?
  2. What data may cross network or region boundaries?
  3. How will AI behavior be evaluated in production?
Bring us the current estate

Best suited to

  • New analytics foundations
  • Fabric or Databricks selection
  • Production foundations for generative AI use cases

Needed to begin

  • Data owners and use cases identified
  • Source access and sensitivity known
  • Identity, network, and region constraints available

Customer responsibilities

  • Approve data use and retention
  • Supply domain rules and acceptance data
  • Own model risk, human oversight, and business adoption

Not included by default

  • Unrestricted use of sensitive data
  • Model outcome guarantees
  • Data cleansing or labeling beyond named datasets