Infrastructure design

We design and implement robust Azure cloud infrastructures tailored to your business needs. Our approach ensures scalability, security, and cost efficiency, while aligning with Microsoft best practices. From networking and storage to compute and identity management, we build a strong foundation that supports long-term growth and reliability.

Solution reference architecture design

Our team develops Azure solution reference architectures that provide clear, proven blueprints for complex data and analytics initiatives. These frameworks accelerate delivery, reduce risk, and ensure consistency across projects. By leveraging industry standards and Azure-native services, we help clients achieve faster time-to-value with reliable, enterprise-ready solutions.

Data architecture and models

We create modern data architectures and models on Azure that transform raw data into structured, actionable insights. This includes designing pipelines, warehouses, and semantic models optimised for performance and scalability. Our focus is on enabling organisations to unlock the full potential of their data for decision-making and innovation.

Metadata-driven pipelines

We design and implement metadata-driven pipelines on Azure that adapt dynamically to business rules and data requirements. This approach reduces repetitive coding, increases maintainability, and accelerates delivery. By embedding intelligence into the pipelines, we enable greater flexibility, scalability, and governance across diverse data sources and transformation processes.

{
    "framework": {
        "version": "2.4",
        "pipeline": "wdm_factories_pipeline",
        "active": 1,
        "path": "data_warehouse/config/01_delta/tms/01_site/pipelines"
    },
    "annotations": {
        "zone": "bronze",
        "schema": "delta",
        "table": "tms_01_site_consignment",
        ...

Load-balance orchestration

Our load-balance orchestration solutions ensure that Azure data workflows run efficiently and reliably, even at enterprise scale. By distributing workloads intelligently across resources, we minimise bottlenecks, optimise performance, and reduce downtime. This results in resilient data operations that can seamlessly support business-critical analytics and machine learning initiatives.

Development accelerators

We leverage development accelerators, prebuilt frameworks, reusable components, and proven methodologies, to speed up delivery on Azure projects. These accelerators reduce repetitive effort, improve quality, and ensure consistency across solutions. This results in faster time-to-value, lower costs, and scalable solutions that evolve seamlessly with business requirements.

from pyspark.sql import functions as F
df = (spark.table("maintenance.performance_indicators")
        .where(F.col("indicator") == "build_time_hours")
        .select("baseline","with_accelerator","abs","improve_percentage","p90_percentage","projects")
)

// build_time_hours
// baseline - 40
// with_accelerator - 24
// Δ (abs) - 16
// improve_percentage - 40%
// p90_percentage - 55%
// projects - 12

from pyspark.sql import functions as F
df = (spark.table("maintenance.performance_indicators")
        .where(F.col("indicator") == "manual_coding_hrs_wk")
        .select("baseline","with_accelerator","abs","improve_percentage","p90_percentage","projects")
)

// manual_coding_hrs_wk
// baseline - 20
// with_accelerator - 8
// Δ (abs) - 12
// improve_percentage - 60%
// p90_percentage - 70%
// projects - 10

from pyspark.sql import functions as F
df = (spark.table("maintenance.performance_indicators")
        .where(F.col("indicator") == "pipeline_success")
        .select("baseline","with_accelerator","abs","improve_percentage","p90_percentage","projects")
)

// pipeline_success
// baseline - 92
// with_accelerator - 98
// Δ (abs) - 6
// improve_percentage - 6.52%
// p90_percentage - 8%
// projects - 15

from pyspark.sql import functions as F
df = (spark.table("maintenance.performance_indicators")
        .where(F.col("indicator") == "pipeline_defects")
        .select("baseline","with_accelerator","abs","improve_percentage","p90_percentage","projects")
)

// pipeline_defects
// baseline - 10
// with_accelerator - 6
// Δ (abs) - 4
// improve_percentage - 40%
// p90_percentage - 50%
// projects - 9

from pyspark.sql import functions as F
df = (spark.table("maintenance.performance_indicators")
        .where(F.col("indicator") == "infra_cost")
        .select("baseline","with_accelerator","abs","improve_percentage","p90_percentage","projects")
)

// infra_cost
// baseline - 8000
// with_accelerator - 6500
// Δ (abs) - 1500
// improve_percentage - 18.75%
// p90_percentage - 25%
// projects - 7

Microsoft Fabric

We deliver solutions on Microsoft Fabric that unify data integration, engineering, and analytics into a single platform. By leveraging its end-to-end capabilities, we help organisations simplify complex data landscapes, accelerate insight generation, and improve governance, all within a scalable, cloud-native ecosystem built for enterprise performance.

Confident with PySpark?

from pyspark.sql.functions import col, current_timestamp, when
bronze = spark.read.format("delta").load("Files/lakehouse/bronze/tms/site/consignment")

silver = (
    bronze
    .withColumn("ingested_at", current_timestamp())
    .withColumn("status", when(col("delivered") == True, "Delivered").otherwise("InTransit"))
)

(silver.write
    .format("delta")
    .mode("overwrite")
    .option("overwriteSchema", "true")
    .save("Tables/silver/tms/site/consignment_curated"))

Prefer SQL?

With Fabric, use the skills you know to build the data solutions you need

SELECT
  site_id,
  COUNT(*) as total_consignments,
  SUM(CASE WHEN status = 'Delivered' THEN 1 ELSE 0 END) AS delivered,
  CAST(SUM(CASE WHEN status = 'Delivered' THEN 1 ELSE 0 END) * 1.0 / COUNT(*) AS DECIMAL(5,2)) AS delivery_rate
FROM OPENROWSET(
  BULK 'tables/silver/tms/site/consignment_curated',
  DATA_SOURCE = 'OneLake',
  FORMAT = 'DELTA'
) WITH (
  site_id INT,
  status NVARCHAR(32)
) AS d
GROUP BY site_id;

Power BI

Our team designs and develops Power BI solutions that transform data into clear, actionable insights. We build intuitive dashboards, interactive reports, and advanced analytics that empower decision-makers at every level. With a focus on usability and scalability, we ensure Power BI becomes a true driver of business intelligence.

Power Automate

We implement Power Automate workflows that streamline business processes and eliminate repetitive tasks. By connecting Azure data services with everyday applications, we create automation solutions that improve efficiency, reduce human error, and free up teams to focus on higher-value work. The result is a more agile, productive organisation.

Governance framework

We establish governance frameworks that define clear policies, roles, and controls for managing enterprise data on Azure. Our approach balances agility with compliance, ensuring data is accessible yet secure. By embedding governance into workflows, we help organisations build trust, maintain regulatory alignment, and maximise the value of their data assets.

Microsoft Purview

We implement Microsoft Purview to provide end-to-end visibility and control over organisational data. From discovery and classification to lineage and access management, Purview enables consistent governance across hybrid and multi-cloud environments. Our solutions ensure that data is not only compliant but also discoverable, trustworthy, and ready for business use.

Migration to Synapse (Serverless or Dedicated)

We guide organisations through seamless migrations to Azure Synapse, whether serverless for flexibility or dedicated for enterprise-scale performance. Our approach ensures data is moved securely, optimised for cost and speed, and structured for analytics readiness, delivering a modern data warehouse that scales with your business needs.

Migration to Fabric

We help organisations migrate to Microsoft Fabric, consolidating data integration, engineering, and analytics into a unified platform. Our expertise ensures smooth transition with minimal disruption, while optimising for governance, performance, and scalability. The result is a modern, end-to-end data estate built to accelerate insight and innovation.

Future-proof design

Our solutions are architected with the future in mind, scalable, adaptable, and ready to embrace new technologies. By combining best practices with modern cloud-native approaches, we design systems that evolve with business needs. This ensures long-term value, resilience, and the ability to leverage emerging innovations without costly redesigns.