Cloud Data Platforms
Architecture and delivery of lakehouse, warehouse and streaming platforms on Azure, AWS and Databricks.
Modern data foundations for AI-ready enterprises
Practice
Data Engineering
01 — Overview
Most AI initiatives fail not because of the model, but because of the data underneath. AIVION's Data Engineering practice helps organisations design, build and modernise the cloud data platforms that power analytics, machine learning and operational decision-making at scale. From greenfield lakehouse architectures on Databricks and Microsoft Fabric to migrations off legacy warehouses, we deliver production-grade pipelines, governance and observability — so your teams can trust every number, every model and every decision.
02 — Capabilities
Architecture and delivery of lakehouse, warehouse and streaming platforms on Azure, AWS and Databricks.
Resilient batch and real-time pipelines using Spark, Delta Lake, Kafka and dbt.
Dimensional, vault and medallion models that balance flexibility with performance.
Catalogues, lineage, contracts and automated quality testing built into every pipeline.
CI/CD, infrastructure-as-code, monitoring and SLOs for data products.
Pragmatic migrations from on-premise warehouses, SAS and Hadoop into modern cloud platforms.
03 — Approach
Audit current data estate, business priorities and target operating model.
Define target architecture, governance model and a phased delivery roadmap.
Deliver platform, pipelines and data products in agile increments.
Hand over with runbooks, SRE practices and an enablement plan for your team.
04 — Outcomes
Ready when you are
A 30-minute working session with a senior practitioner — no pitch, just a clear view of feasibility, value and the right next step.