Cloud · Since 2018 · Enterprise estates

Microsoft Azure

The right cloud when the organisation already runs on Microsoft.

Our position

Azure is the right answer for a specific and common situation: an organisation already running Microsoft 365, Active Directory and Power BI, where identity, licensing and support are already consolidated with Microsoft. In that context the integration advantages are real and the enterprise agreement pricing is frequently better than the alternatives.

Our Azure work concentrates on the data platform — Data Factory for orchestration and ingestion, Azure Databricks for Spark and lakehouse, Synapse where a managed warehouse suits, Cosmos DB for genuinely globally distributed workloads, and Data Explorer, formerly Kusto, for high-volume telemetry and log analytics.

On the application side we run App Service and Container Apps with Azure Database for PostgreSQL, with Entra ID handling identity — which is usually the deciding advantage, since it means staff sign in with the credentials they already have and access reviews happen where IT already runs them.

Where it fits

Data platforms for organisations standardised on Microsoft 365 and Power BI

Azure Data Factory pipelines ingesting from on-premise SQL Server and SAP

Azure Databricks lakehouse with Unity Catalog governance

Cosmos DB for globally distributed, low-latency workloads

Application hosting with Entra ID authentication

Why we choose it

What Microsoft Azure genuinely gives us

Identity that is already there

Entra ID single sign-on and conditional access apply to new applications without a separate identity project.

Power BI integration

For organisations standardised on Power BI, keeping the semantic and storage layers in Azure removes a category of connectivity and gateway friction.

Enterprise agreement economics

Existing Microsoft commitments frequently make Azure the cheapest option on paper and in practice.

Data Explorer for telemetry

Kusto is genuinely excellent for high-volume log and time-series analytics, and it has no close equivalent elsewhere.

Data Factory as the ingestion and orchestration layer

Data Factory earns its place through the self-hosted integration runtime, which reaches into on-premise SQL Server, SAP and file shares behind a firewall without exposing them. For organisations with substantial on-premise estate — which describes most Indian manufacturing groups — this removes a genuine obstacle.

We use it for ingestion and orchestration and generally keep transformation logic in Databricks or dbt rather than in Data Factory data flows, because code in version control with tests is easier to review and maintain than a visual mapping.

Self-hosted integration runtime for on-premise sources, with high availability.

Parameterised pipelines rather than one pipeline per table.

Managed identities throughout — no connection strings with embedded credentials.

Monitoring and alerting wired to a channel a human actually watches.

Governance from the first subscription

Management groups, subscriptions separated by environment, Azure Policy enforcing region and tagging constraints, and role assignments through Entra groups rather than individual users. Configured at the start this costs a few days; retrofitted across an established estate it is a project.

Cost management follows the same structure — spend attributed by subscription, resource group and tag, with budgets and alerts rather than a monthly surprise.

Honest warnings

How Microsoft Azure projects usually go wrong

These are the failure modes we look for first when we are called in to rescue somebody else's implementation.

Transformation logic in visual data flowsHard to review, hard to test, hard to diff. Keep logic in code and use Data Factory for movement and orchestration.

Cosmos DB as a default databaseExcellent for genuinely global low-latency workloads, expensive and awkward as a general-purpose relational substitute.

Under-provisioned integration runtimeA single self-hosted node becomes a single point of failure for every on-premise ingestion.

Governance retrofittedManagement groups, policy and tagging are cheap at the start and a substantial project later.

Straight answers

Microsoft Azure questions we get asked

Including where we would recommend something other than Microsoft Azure. Call +91 70033 91355 and you will get the same answer from an engineer.

+91 70033 91355 WhatsApp

It is a genuine point in Azure's favour, mainly because of identity — Entra ID single sign-on and conditional access apply to new applications without a separate project, and access reviews happen where your IT already runs them. It is not decisive on its own. If your data team has deep AWS or GCP skills, that matters more. We weigh both rather than assuming.

Databricks for anything Spark-heavy, streaming or machine-learning adjacent, and for a genuine lakehouse with Unity Catalog governance. Synapse dedicated pools where a straightforward managed warehouse serving Power BI is the requirement and the team prefers pure SQL. Many estates use both, and we keep the boundary explicit rather than letting it blur.

Yes, through the self-hosted integration runtime, which runs inside your network and initiates outbound connections only — so no inbound firewall exposure is required. We deploy it with at least two nodes for availability, because a single node quietly becomes a single point of failure for every on-premise ingestion you have.

Services built on Microsoft Azure

Cloud, Hosting & DevOpsAWS, Azure and GCP infrastructure as code, domains, CDN, CI/CD and observability — built so a deployment is boring and an outage is short.Data EngineeringPetabyte-scale pipelines, lakehouses and warehouses — built by engineers who have run them in production for cement, steel, solar and commerce clients.Business Intelligence & AnalyticsDashboards people actually open, built on a modelled semantic layer so two departments never bring different numbers to the same meeting.ETL & Data PipelinesBatch and real-time pipelines that run every day without drama — orchestrated, monitored, idempotent, and honest about failure.

Technologies we pair it with

Databricks & Delta LakeLakehouse platform where Spark, streaming and machine learning share one governed layer.Apache SparkDistributed processing for the workloads that genuinely need it — batch and streaming.PostgreSQLThe default operational database for every business system we build.PythonThe language our data, scraping and AI work is written in.

Next step

Tell us what is slowing your business down.

A 30-minute call with a senior engineer — not a salesperson. You leave with an architecture sketch and an honest cost range, whether or not you hire us.

Book a discovery call