Azure Data Platform Managed Services Across Databricks and AKS
Challenges
As the Azure data platform scaled, growing complexity across multiple integrated services increased operational overhead and dependency challenges. The lack of centralised ownership led to reactive firefighting, frequent failures, and risks to business-critical reporting. At the same time, rising Azure costs and continuous monitoring demands pressured teams to balance platform stability, performance, and cost efficiency.
Outcome
The engagement delivered measurable impact, achieving 98%+ SLA compliance and maintaining over 95% platform uptime for critical workloads. Proactive monitoring reduced MTTR significantly, while continuous optimisation improved resource utilisation and lowered overall cloud consumption.
Solution
Managed Services
Challenges
Solution
Technology Stack
Outcomes
For large retail enterprises, data platforms are no longer just supporting systems. They are critical operational foundations that power reporting, analytics, supply chain visibility and business decision-making at scale. But as these ecosystems grow across cloud services, users and workloads, maintaining platform reliability, operational governance and cost efficiency becomes increasingly difficult.
This case study highlights how Cloudaeon partnered with a leading UK retailer through a fully managed services model to stabilise and optimise its data platform on Azure. By taking end-to-end operational ownership across the platform ecosystem, Cloudaeon enabled 95%+ uptime, 98%+ SLA adherence with zero escalations, faster issue resolution and continuous cloud cost optimisation. It has transformed the retailer's data platform into a highly reliable and operationally mature data platform.
Client Problem
The organisation operated a large-scale cloud-native Azure data platform. It was used by data engineers and analytics teams across multiple locations. It supported business-critical reporting and daily data processing workloads, making operational reliability a top priority. However, as adoption increased, the platform, once relatively simple, became significantly more complex to manage.
Along with this platform, they used many integrated services like Apache Airflow, Azure Data Factory (ADF), Databricks, Azure Kubernetes Service (AKS), Alation, Prophecy, Soda, and Logic Apps, all running on the Azure infrastructure. Each of these services introduced its operational dependencies, upgrade cycles, monitoring requirements, and support challenges. As engineering demands grew, platform availability became a critical priority for the organisation. The platform was heavily relied upon for pipeline execution, workflow orchestration and analytics delivery.
The organisation also faced mounting pressure to optimise Azure consumption to ensure cloud costs remained controlled. Platform complexity began to impact both engineering teams and business users. Frequent interventions were required to maintain platform stability, resolve workflow failures and coordinate maintenance across services. Since the platform supported business-critical workloads, even minor disruptions created reporting risks and delayed decision-making. Centralised operational ownership was lacking, forcing teams into reactive firefighting rather than enabling proactive platform improvement. Simultaneously, Databricks and AKS environments required continuous monitoring and optimisation to control cloud costs and improve resource efficiency.
Root Cause Analysis
Cloudaeon’s platform experts conducted a thorough root cause analysis. It was then concluded that the challenges were not caused by one single technology or limitation, but by the absence of an integrated operational governance framework across the platform ecosystem.
Fragmented Monitoring Across Services: Monitoring was performed at the individual service level, with limited end-to-end visibility on the platform level. This delayed issue detection and increased mean time to resolution (MTTR).
Reactive Maintenance Processes: Upgrades, patching activities and certificate renewals were handled manually. This required significant coordination effort, thereby increasing maintenance overhead and operational risks.
Lack of Standardised Incident & Change Governance: The platform lacked a unified operational framework for incident response and SLA tracking. The system also lacked controlled change management, which led to operational consistency being difficult at scale.
Unoptimised Resource Consumption: The Cloud infrastructure usage patterns were not being continuously reviewed against actual workload demand.
Solution Architecture
Cloudaeon implemented a comprehensive operational management framework designed to deliver reliability, governance, scalability and cost efficiency across the data platform ecosystem. Cloudaeon covered full operational ownership of the Azure-based platform, including:
Apache Airflow
Azure Data Factory (ADF)
Databricks
Azure Kubernetes Service (AKS)
Alation
Azure Logic Apps
Soda
Prophecy
The solution was structured around five core pillars: Proactive Monitoring & Reliability Engineering: End-to-end monitoring was established across platform services to improve operational visibility and accelerate issue detection. This resulted in:
Faster incident identification
Early detection of performance bottlenecks
Reduced downtime risks
Improved MTTR
It also strengthened platform reliability through proactive operational management rather than reactive troubleshooting.
Platform Lifecycle Management: A structured lifecycle management process was implemented to manage:
Platform upgrades
Security patching
Certificate renewals
Environment maintenance
Cloudaeon ensured that all the activities were executed with zero disruption to active business workloads.
Incident & Change Management: Cloudaeon introduced enterprise-grade governance processes aligned with SLA-driven operations. This enabled structured incident response workflows and established controlled change implementation processes. Moreover, audit-ready operational tracking was implemented with risk-management execution.
Cloudaeon’s approach significantly improved operational predictability and platform governance.
End-User Support Enablement: Dedicated L2 and L3 operational support was provided to assist the client’s data engineering community. The support model focused on:
Faster issue resolution
Workflow debugging assistance
Pipeline troubleshooting
Improved user productivity
Continuous Cost Optimisation: Cloudaeon implemented quarterly platform audits focused on Azure consumption analysis and infrastructure efficiency. The optimisation program included:
Databricks compute optimisation
AKS resource right-sizing
Identification of underutilised services
Workload efficiency improvements
Continuous cloud spend governance
How We Delivered
Cloudaeon deployed a dedicated team of engineers with expertise:
Azure Architecture
DevOps
Databricks
Kubernetes
Cloud Operations
The team operated as an extended platform operations unit, delivering 24×7 support coverage while maintaining strict SLA adherence.
Operational delivery combined proactive monitoring, structured governance, continuous optimisation and user support into a single managed services framework. This enabled the client to shift from reactive platform management to a stable, scalable and operationally mature data platform model.
Technology Stack
Microsoft Azure
Databricks, Azure Data Factory
Apache Airflow
Azure Kubernetes Service (AKS)
Alation
Azure Logic Apps
Soda
Prophecy
Outcomes
This managed service collaboration with Cloudaeon had a very powerful impact on the enterprise:
Achieved 98%+ SLA compliance within defined timelines
Maintained more than 95% platform uptime across services
Reduced mean time to resolution (MTTR) through proactive monitoring
Successfully handled 100+ user requests per month
Delivered 20–30 implementation and enhancement tickets monthly
Achieved zero escalations in platform support operations
Enabled continuous cost optimisation through quarterly audits
Improved infrastructure utilisation across Databricks and AKS
Reduced unnecessary cloud resource consumption
Conclusion
Through this managed services partnership with the leading UK retailer, Cloudaeon helped transform its data platform from a reactive operational environment into a stable and governance-driven cloud data platform. That included proactive monitoring, lifecycle management, structured incident governance, continuous optimisation, and 24×7 operational support. Cloudaeon established a highly reliable and cost-efficient operational framework for the platform. Today, the platform delivers consistent uptime, strong SLA adherence, improved engineering productivity, and optimised Azure resource utilisation. The enterprise can now focus on business innovation while Cloudaeon ensures uninterrupted and efficient data operations at scale.