Why Cloudaeon for Databricks?
Most organisations struggle to get value from Databricks because engineering falls short. Cloudaeon removes those barriers.
Stabilise & Modernise Databricks Platforms
We fix failing performance, governance and operational foundations to restore trust and scale.
Deliver Production Grade AI
on Databricks
RAG+, agent workflows, evaluation and AI Ops are engineered to run reliably in production.
Scale Delivery with Databricks Engineering PODs
Governed, high velocity teams built for execution, not staff augmentation.
Our Databricks
Engineering
Expertise
Build a Databricks platform that’s governed, audit-ready and cost-controlled — and ready for AI in weeks.
Databricks Platform
Modernisation
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Unity Catalog migrations built on UC-first architecture patterns
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Workspace design and permission models that support enterprise governance
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End-to-end lineage, auditability and compliance readiness
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Cost and performance optimisation across clusters and workloads
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Reliable job orchestration for production grade data and AI pipelines
Production Grade AI on
Databricks
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RAG+ pipelines with vector search, built for enterprise scale
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Retrieval optimisation and automated evaluation to improve accuracy and reduce hallucinations
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Agent workflows designed for real operational use cases
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Model governance registry and deployment aligned with enterprise controls
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AI Ops including monitoring, guardrails and drift detection
Lakehouse Engineering
& Delivery
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Delta Lake patterns for better performance
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Scalable batch and streaming ingestion (ETL/ELT)
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CI/CD pipelines purpose built for Databricks
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Observability and data quality across Lakehouse workloads
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Architectural refactoring for scale and resilience
Engineering led
Databricks Solutions
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UC Migration Toolkit for structured, low risk Unity Catalog migrations
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RAG+ Starter Framework for production ready retrieval and evaluation pipelines
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AI Ops Evaluation Engine for model evaluation, monitoring and operational guardrails
How We Deliver Databricks Success
We don’t provide ad-hoc contractors. We embed Databricks Engineering PODs backed by repeatable patterns, solutions and delivery principles.
What a Databricks POD Include
• 1 Lead data / AI engineer for technical ownership and architecture
• 2 to 3 Senior or mid level Databricks engineers for platform and AI delivery
• 1 to 2 Data or analytics engineers for ingestion, modelling and analytics
How PODs Are Run
• Governed by Cloudaeon engineering principles
• Powered by proven solutions and Databricks best practices
• Designed for continuous improvement, not one-off delivery
• Aligned to outcomes, velocity and platform stability
Databricks Results in Production
How We Help Databricks
AEs Win
We help Databricks teams address stalled accounts, accelerate adoption and stabilise customer platforms.
• Rapid assessments for at-risk or stalled customer environments
• Execution support for AI use cases, including RAG, agents and evaluation
• Fast deployment of Databricks Engineering PODs
• Clear co-sell alignment and delivery ownership
• Weekly or bi-weekly progress updates
• Structured handover and documentation
Databricks Certifications & Expertise
102+
Databricks Certified Engineers
35+
Databricks Gen AI
Certified Engineers
1+
Databricks Champion
100+
Successful Engagements Globally
FAQs
Yes. Cloudaeon is an official Databricks partner with deep engineering-led expertise across Lakehouse architecture, Unity Catalog, and AI on Databricks. Beyond partnership status, Cloudaeon works closely with Databricks teams to stabilise platforms, accelerate adoption and move customers from experimentation into reliable production environments.
Cloudaeon provides end-to-end Databricks engineering services, covering platform modernisation, Lakehouse delivery and AI implementation. This includes Unity Catalog migrations, workspace and permission design, cost and performance optimisation, scalable ETL and streaming pipelines, CI/CD for Databricks and production-grade AI use cases such as RAG+, agent workflows, evaluation and AI Ops.
Yes. Cloudaeon specialises in structured, low-risk migrations from legacy data platforms to Databricks. This includes assessment, architectural refactoring, Unity Catalog-first migrations, dependency mapping and performance optimisation to ensure platforms are not just migrated, but modernised and ready for scale, governance and AI workloads.
Yes. Cloudaeon provides ongoing managed services and AI Ops for Databricks environments running in production. This includes platform monitoring, cost and performance optimisation, data quality and pipeline reliability, model monitoring, evaluation, governance and continuous improvement. The focus is long-term platform stability, predictable operations and trusted AI at scale.
Getting started typically begins with a Databricks platform assessment or a conversation with a Databricks architect. From there, Cloudaeon defines a clear path, whether that’s stabilising an existing platform, migrating to Unity Catalog, delivering AI use cases or embedding a Databricks Engineering POD to accelerate execution and adoption.