When your data platform becomes a barrier to scale and innovation
Legacy ETL, fragmented tools, and inconsistent pipelines all create the same problem — your AI ambitions are ahead of your data. Azure Databricks is the fix.
Large-Scale Pipelines
Batch and streaming data engineering at enterprise scale, without the performance headaches.
Modern Lakehouse
Delta-based architecture that unifies your raw, transformed, and trusted data layers.
Governed by Design
Unity Catalog gives you centralised access control, lineage, and auditability across your data estate.
AI-Ready Data
Structure and govern your data so your AI agents and GenAI tools actually have something to work with.
Azure-Native
Integrates across your existing Azure estate, storage, identity, monitoring, and analytics.
One Platform, All Teams
Engineering, analytics, and data science, working from the same foundation, not separate silos.
End-to-end Databricks Delivery
From architecture and migration to governance and AI enablement, we cover the full lifecycle.
01
Platform Architecture & Setup
Enterprise-ready Databricks environments from day one — workspace design, network and security, environment separation, CI/CD, monitoring, and Azure integration.
02
Migration & Modernisation
Move workloads from legacy data warehouses, custom ETL, and fragmented Azure stacks into a cleaner modern architecture. Not just migration — simplification.
03
Data Engineering & Pipelines
Robust batch and streaming pipelines using medallion architecture — from raw to trusted data layers, improving quality, reusability, and reporting reliability.
04
Lakehouse & Analytics Foundations
Delta-based lakehouse structures, Power BI integration, and trusted semantic layers that support reporting, advanced analytics, and self-service business use.
05
Governance, Security & Cost
Unity Catalog, access design, lineage, auditing, tagging, cost visibility, and performance tuning. Governance built in — not bolted on.
We build the data foundation first. Then your AI actually works.
Get your data structured ready for AI agents.
AI copilots and GenAI tools are only as powerful as the data underneath them.
Most organisations want to move into AI, but their data isn’t clean, governed, or production-ready yet.
Clean & Structure Your Data
Raw to trusted layers via medallion architecture.
Govern & Catalogue
Unity Catalog — lineage, access, and discoverability.
Make it Accessible
Semantic layers, APIs, and data products your AI can consume.
Enable AI on Solid Ground
GenAI, agents, and analytics built on data you trust.
The Solution
Problems We Solve Every Day
If any of these sound familiar, we can help.
Legacy Platforms Holding You Back
Older ETL and warehouse platforms become expensive, rigid, and hard to evolve. We modernise those estates into scalable Azure-native architectures.
Fragmented Data Estate
Duplicated pipelines, inconsistent logic, disconnected tools. We unify engineering and analytics in a coherent, manageable platform.
Governance Not Keeping Up
Access control, lineage, and auditing falling behind as your data grows. We implement Unity Catalog properly — from the start.
AI Ahead of your Data
Older ETL and warehouse platforms become expensive, rigid, and hard to evolve. We modernise those estates into scalable Azure-native architectures.
Costs Growing Without Visibility
Databricks needs good architecture and cost governance to stay efficient. We tune for both value and scale.
No Clear Path Forward
Not sure where to start, what to migrate, or how to phase the work? We provide a practical roadmap grounded in your constraints.
We build the data foundation first. Then your AI actually works.
Databricks at Cuscal
Cuscal is one of Australia’s leading providers of payments infrastructure and services, operating in a fast-moving and highly regulated financial services industry. Cuscal’s data platform had evolved into a decentralised, multi-platform environment. Data assets were spread across disparate tools, creating inconsistency, duplication, and significant operational risk. Conflicting internal tools made it difficult to integrate, deploy, and consume analytics in a coherent way. The absence of a unified platform meant there was no consistent audit trail a critical gap in a regulated financial environment. Pipeline deployment was slow, manual, and error prone. Each new workflow required bespoke effort, limiting the team’s ability to scale. Cuscal needed a fundamentally different approach. Learn more in our case study.
Process
How We Work With You
A structured, low-risk path from assessment to scale.
1
Discovery & Assessment
Review your current estate, workloads, pain points, and target use cases.
2
Architecture & Roadmap
Design the target state, migration sequence, security model, and governance patterns.
3
Foundation & MVP
Build the core platform and deliver an initial use case to validate architecture early.
4
Scale & Optimise
Expand pipelines, onboard domains, automate deployments, tighten governance.
5
Enablement & Support
Upskill your team, document standards, and provide ongoing operational support.
It's Not Aways Either/Or
For many organisations, Databricks and Fabric coexist within the same Azure architecture. We help you make the right call – not just push one tool.
Advanced Engineering & Control
Choose Databricks when you need large-scale transformation, deep platform control, and a serious data engineering foundation.
Integrated Microsoft Analystics
Choose Fabric when you want a more seamless Microsoft analytics experience for broader business adoption across your organisation.
A Hybrid Enterprise Model
Use both when your organisation needs different tools for engineering, analytics, and AI. Agile Insights has depth across both — so we advise in context.
We build the data foundation first. Then your AI actually works.
Databricks at Yarra Valley Water
Yarra Valley Water (YVW) is one of Australia’s largest water corporations, providing essential water and sewerage services to more than 2 million people across Melbourne’s northern and eastern suburbs. Yarra Valley Water was concerned that its Azure Databricks spend was exceeding the budgeted plan while implementing its Azure Advanced Analytics Platform. As costs escalated, YVW needed expert guidance to understand the drivers of overspend and identify a path to greater efficiency. The organisation sought an independent assessment from Agile Insights. Real the full case study to learn more.
Common Questions
What is Azure Databricks best suited for?
Large-scale data engineering, analytics, and AI workloads. It’s built for data teams that need to collaborate on engineering, analytics, and advanced data use cases at scale — across batch and streaming.
What is Unity Catalog and why does it matter?
Unity Catalog is Databricks’ unified governance layer. It provides centralised access control, discovery, lineage, and auditing across all data and AI assets — giving you visibility and control as your platform scales.
Can Databricks work with our existing Azure environment?
Yes. It’s designed to integrate within Azure architectures alongside services like Azure Data Lake Storage, Azure Data Factory, and Microsoft Fabric. We design those integrations as part of every engagement.
How do you handle secure enterprise setup?
We help with workspace design, identity, storage access, governance, logging, cost controls, and secure access patterns — aligned to Microsoft’s well-architected guidance for Databricks.
What does a first engagement typically look like?
Usually a discovery workshop or platform assessment. That gives you a clear view of architecture options, migration priorities, governance needs, quick wins, and a phased roadmap — before committing to a full build.
Ready to build on Azure Databricks?
Talk to Agile Insights about your current platform, migration plans, AI goals, or governance requirements. We’ll design a practical path forward.