What is Lakehouse Modernisation Solution?
The Lakehouse modernisation solution helps enterprises recover, stabilise and modernise existing Databricks and Microsoft Fabric platforms that are failing to deliver reliable analytics or support AI initiatives. The Lakehouse modernisation solution offers:
Platform stability and reliability
Governance and compliance
Cost and performance optimisation
Making data usable for AI, RAG and advanced analytics
Data pipelines that fail frequently or require constant manual intervention
Rising platform costs with little transparency or accountability
Governance gaps due to incomplete or incorrect Unity Catalog or Purview implementations
Slow, inconsistent, or unreliable BI refresh cycles
Data quality issues across domains that undermine trust
AI initiatives stalled by inaccessible, poorly governed, or untrusted data
Common Challenges with Lakehouse Modernisation
Many organisations have invested in Lakehouse platforms, but poor implementation and weak operating models leave them unstable and costly.
Why Lakehouse Platforms Fail?
Without production grade architecture, governance, and DataOps, Lakehouse platforms cannot scale beyond initial success because:
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Architecture patterns borrowed from demos or reference builds, not production grade workloads
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Governance added as an afterthought instead of being foundational
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No clear separation between platform engineering (build) and platform operations (run)
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Weak workload isolation and cost management practices
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Absence of a DataOps operating model once the platform goes live
Enterprise Lakehouse Modernisation Blueprint
Lakehouse Modernisation Solution Capabilities
Cloudaeon’s Lakehouse modernisation solution is delivered through a set of proven, production grade capabilities designed to stabilise existing platforms, restore trust and enable scale.
Platform Stabilisation
We address the root causes of instability to restore reliability and operational confidence.
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Improve pipeline reliability and failure handling
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Remove fragile, duplicated or tightly coupled logic
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Establish clear separation of workloads to reduce contention and blast radius
Governance & Trust
We implement governance correctly foundational, consistent and enforceable.
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Production grade Unity Catalog or Purview implementations
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Consistent access control, metadata management and lineage
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An audit ready data estate aligned to enterprise compliance needs
Operational Efficiency
We make performance predictable and costs transparent.
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Compute and workload tuning based on real usage patterns
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Storage and query optimisation for scale and efficiency
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Cost visibility, guardrails and controls aligned to business accountability
Analytics Enablement
We enable analytics teams to move faster with confidence.
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Faster, more reliable BI refresh cycles
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Simplified and consistent semantic models
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Reduced query latency and improved user experience
AI Readiness
We prepare your lakehouse to support AI workloads at scale.
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Data structured and governed for RAG, ML, and agentic use cases
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Metadata and access controls aligned to AI consumption patterns
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A strong foundation for AI operations, monitoring and evaluation
Designed forthe Platforms You Already Run
This solution applies to enterprise Lakehouse environments, including:
License & Ownership Model
This solution is delivered as a fully client owned implementation, designed to maximise control, transparency and long term flexibility.
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No proprietary tooling or architectural lock in
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All architecture, pipelines, configurations and operational assets belong to you
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The platform remains fully operable by your internal teams or any future partner
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Cloudaeon intellectual property is used to accelerate delivery, not to restrict ownership or create dependency
Delivery & Commercial Model
One Time Modernisation Engagement
A structured engagement focused on fixing core platform issues and establishing a production-grade foundation.
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Platform assessment and stabilisation
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Governance and architecture remediation
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Performance and cost optimisation
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Knowledge transfer and operational handover
Ongoing Managed Support
For organisations that require continued operational assurance after modernisation.
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DataOps and Lakehouse platform operations
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Continuous monitoring, optimisation, and governance upkeep
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SLA backed reliability and performance
Optional Proof of Design (PoD)
For highly complex or high risk environments where targeted validation is required.
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Used to de-risk specific architectural or operational problem areas
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Focused, time bound, and outcome driven
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Not mandatory for standard modernisation engagements
What This Solution Is Not
To be clear, this is not a generic implementation or resourcing service.
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Not Databricks services
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Not Microsoft Fabric implementation services
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Not staff augmentation or body leasing
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Not a lift-and-shift or migration-only engagement
FAQs
It is a structured service to stabilise, govern and optimise existing Databricks or Microsoft Fabric platforms that are already live but underperforming. The goal is to restore trust in data, control costs and make the platform usable for analytics and AI at scale.
Most modernisation efforts stop at platform setup and ignore operability, governance and post–go-live discipline. Without proper DataOps, cost controls and governance-first design, platforms technically run but cannot scale reliably.
Costs increase due to inefficient compute usage, poorly tuned workloads, lack of isolation and missing cost guardrails. Performance remains flat because the underlying architecture and query patterns were never optimised for production scale.
No. The solution focuses on targeted remediation, fixing fragile pipelines, improving architecture patterns and introducing DataOps without re-platforming or rebuilding everything from scratch.
By enforcing trusted data zones, strong metadata, lineage, access control and data quality, the lakehouse becomes discoverable and safe for RAG, ML models and agentic workflows. AI readiness is treated as a data and governance problem, not just a tooling upgrade.
Governance tools are often partially implemented or bolted on late, leading to broken lineage, inconsistent permissions and poor auditability. This solution remediates governance with audit-first, AI-aware patterns that actually work in production.
Standard implementations focus on building or migrating a platform. Lakehouse Modernisation focuses on fixing what’s already live, stability, governance, cost efficiency and long-term operability are the primary outcomes.