Azure Services

Azure is broad enough that the platform is rarely the constraint. What decides whether a migration lands is the order things are done in: identity, network and guardrails before workloads, and a per-application decision about what deserves rewriting and what can move as it is.

Iseyon designs the landing zone, moves the workloads, builds the data platform on top, and hands the environment to the team that will operate it.

Foundation and migration

Landing zone first

We set up management groups, subscriptions, network topology, identity and policy before the first workload arrives. Naming, tagging, segmentation and guardrails then belong to the environment, and every project inherits them. We define the landing zone as code, so a new subscription arrives with the same controls already in place.

Migration approach

We start from a dependency map, because the thing that breaks a cutover is usually an integration nobody documented. A server list misses it. We group applications into waves, and each one gets its own decision: rehost where the workload is fine as it is, or replatform onto managed database or app services where operations are the real cost. Where the application itself is the constraint, we re-architect it. What nobody uses, we retire. Every wave has a tested rollback. We rehearse each cutover before we perform it.

Compute, storage and networking

We choose the core services for each workload: virtual machines and scale sets, App Service or containers, managed disks and Blob Storage, virtual networks with private endpoints, and load balancing. We agree availability zone strategy, backup, and recovery time and recovery point targets with the business before the design is fixed. Then we test them. An untested recovery target is a guess.

Data platform

Data engineering

We build ingestion and transformation in three layers: raw landing, a cleaned and conformed layer, and models published for consumption. The layers sit on Data Lake Storage, built with Azure Data Factory, Databricks or Fabric pipelines. The pipelines are parameterized and incremental, so your team adds a new source by changing configuration.

Business intelligence

We connect Power BI to governed models, so a KPI has one definition and access rules live in one place. We design refresh schedules and gateway paths together with the data pipelines.

Machine learning

We use Azure Machine Learning for the parts of the lifecycle that need discipline: versioned data and models, reproducible training, and deployment with monitoring for drift and cost. Anything we put into production has an owner and a rollback path.

Security, governance and operations

Identity and access

We build on Microsoft Entra ID with role-based access granted to groups, conditional access, and managed identities so applications stop holding credentials. Secrets go into Key Vault. Access reviews run as a recurring process with an owner.

Governance and compliance

We apply Azure Policy, resource tagging and diagnostic settings from the landing zone, so configuration drift raises an alert the day it happens. Defender for Cloud and Log Analytics give one place to see posture and produce evidence, which is what an audit actually asks for.

Cost management

We tag for chargeback, set budgets and alerts per subscription, rightsize after observing real load, and apply reservations or savings plans only where usage has proven steady. We leave the client with cost reporting they can read by team and by application.

DevOps and automation

Infrastructure ships as code through Azure DevOps or GitHub Actions, with environment promotion, approvals, and the same pipeline for every environment. Nothing that matters gets configured by hand in the portal.

Integration and modernization

We integrate Azure with the systems that stay: on-premises databases, third party SaaS, and line of business applications, using API Management, Service Bus and Event Grid where messaging beats point-to-point coupling. Sequencing keeps the business running while the estate changes underneath it.

What we hand over

We build so the client's own team can run it. Where Iseyon stays involved afterwards, the client chose that, with a team that already knows how the environment works.