M&S Cuts Migration Time 70% with Unity Catalog Accelerator
Challenges
The mandate was to migrate Databricks workloads from Hive Metastore to Unity Catalog to strengthen governance and access control. Despite being a platform upgrade, it impacted hundreds of pipelines and had to be executed without disrupting production.
Outcome
70%+ reduction in migration execution time across scanning, refactoring, and deployment phases.
Over 500 pipelines were transitioned through a structured and controlled migration program.
Solution
Unity Catalog Migration
Challenges
Solution
Technology Stack
Outcomes
A major retailer was operating Databricks, governance was taking a backseat. The business urgently needed to transition from legacy Hive Metastore to Databricks Unity Catalog. However, what looked like a straight platform upgrade, became deeper structural issues. Their teams had to perform manual notebook refactoring; there was also limited auditability and tightly coupled pipelines that developed downtime risk.
As migration friction started to rise, execution slowed and production stability became a concern. That’s when they engaged Cloudaeon. We looked at the whole situation differently. Instead of treating the move as a code clean-up exercise, we reframed it as an engineered platform transition. This introduced automation, deterministic deployment and embedded governance controls.
Client Problem
The mandate was clear: migrate a substantial Databricks estate from Hive Metastore to Databricks Unity Catalog to strengthen governance, standardise access control, which is scalable for retail data operations. It is technically a platform upgrade, but practically, it was a high-risk transition touching hundreds of interdependent pipelines.
Business Context
Governance maturity had become non-negotiable. Fine-grained access control and traceability with controlled data sharing were required. Remaining on legacy Hive Metastore meant governance gaps and operational friction would compound over time. The migration was inevitable, but it had to be done without destabilising production.
Technical Pain Points
Manual Notebook Refactoring
Teams were manually inspecting notebooks to determine which could migrate as-is and which required function updates. Deprecated functions had to be found and replaced by hand. At a wider scale, this introduced inconsistency.
Dev/Production Failures
Lower environments were not aligned with production. Changes tested in dev behaved differently when promoted. Moreover, migration fixes themselves caused breakage.
Limited Change Traceability
There was no robust and centralised audit trail showing what changed, by whom and why. Rollbacks were possible, but not cleanly controlled.
Downtime Amplification
Migration steps were executed sequentially, starting from scan, refactor, test, and deploy with tightly coupled notebook dependencies. One blocked asset could stall an entire migration wave.
Operational Impact
The result was predictable with inconsistencies, intermittent production failures, extended downtime windows and growing cross-team coordination overhead. The issue wasn’t Unity Catalog complexity; it was pure technical and strategic migration mechanics.
Root Cause Analysis
The reason why the migration was failing was because it was being executed like a manual code clean-up project and not like a controlled platform transition. Four structural issues were evident:
No automated inventory or static analysis layer
Deprecated patterns were discovered late and inconsistently. Without automated scanning, scaling beyond a handful of pipelines becomes fragile.
No deterministic promotion path
Deployment into environments lacked automation. Without CI/CD enforcement, drift becomes systemic and production effectively becomes the integration test bed.
Governance treated as post-migration
Auditability and control gates must operate during migration, not after it. A transition of this blast radius requires tight change control embedded in the delivery flow.
Serialised execution of correlated assets
Sequential execution across interdependent notebooks increases downtime and magnifies failure propagation.
The diagnosis was simple, the migration required an engineering control system, not more manual effort.
Solution Architecture
Cloudaeon reframed the migration as a repeatable pipeline:
Step 1: Scan
Step 2: Refactor
Step 3: Deploy
Step 4: Govern
Instead of treating notebooks as ad-hoc artefacts, they became version-controlled assets with deterministic promotion. Core architectural elements:
GitHub as source-of-truth for all notebooks and migration assets
Automated notebook scanning + refactoring workflows were used to detect deprecated functions, auto-replace where safe and flag exceptions
CI/CD-driven deployment across the process, starting from dev → test → production.
Pull request–based approval gates for review, traceability, and rollback
Batch and parallel migration waves to reduce serialised downtime
The transition was from migration as an activity to migration as an engineered system.
How We Delivered
Migration Planning at Estate Scale
The scope involved 500+ pipelines across multiple domains. Work was partitioned into phases, prioritised by complexity and dependency density. Critical business domains were sequenced to minimise operational risk.
Automated Notebook Scanning and Refactoring
Notebooks stored in GitHub were scanned programmatically to identify deprecated functions and UC-incompatible patterns.
Where possible:
Functions were automatically replaced.
Safe transformations were executed without manual touch.
Where ambiguity existed:
Exceptions were flagged for targeted manual intervention.
This removed the human-by-human review bottleneck.
CI/CD Integration for Environment Consistency
GitHub CI/CD pipelines enforced deterministic promotion. Every change moved through development, validation and production through the same automated mechanism. This eliminated dev and production drift and significantly reduced migration-induced failures.
Audit + Rollback via Pull Request Gating
Every change triggered a pull request. Every pull request requires review and approval. This created:
A durable audit trail
Clear change ownership
A clean rollback path
Governance was naturally embedded in the migration process and not layered on afterwards.
Parallel Batch Execution
Migration activities were decomposed into batches of loosely coupled assets. Rather than sequentially blocking large domains:
Refactoring and deployment were executed in parallel waves.
Downtime windows were reduced.
Throughput increased significantly.
Technology Stack
Databricks
Databricks Unity Catalog
GitHub repository scanning
GitHub CI/CD for notebook deployment
Pull request–based audit and rollback workflows
Outcomes
70%+ reduction in migration execution time across scanning, refactoring and deployment phases.
500+ pipelines transitioned through a structured, controlled migration programme.
Strengthened governance posture via Databricks Unity Catalog fine-grained access control.
Reduced production disruption risk through early detection of deprecated patterns.
Standardised deployment mechanics and environment consistency.
POD & Managed Ops Transition
The programme evolved naturally into Cloudaeon’s Solutions → POD → Ops model.
Solution
Establish automation, governance gates and deterministic deployment controls to complete the Hive Metastore to UC transition reliably.
POD (Governance & Release Ownership)
A dedicated POD can assume ownership of:
Unity Catalog policy evolution
Workspace standards
CI/CD guardrails
Remaining edge-case asset migration
This prevents regression into ad-hoc change.
Managed Ops (SLA-Backed Reliability)
Operational transition includes:
Pipeline health monitoring
Release validation
Audit readiness
Continuous optimisation
Governance and reliability remain stable as scale increases.
Conclusion
Unity Catalog migration succeeds when it is engineered as a controlled platform transition, not executed as a manual clean-up exercise. In this migration, automation, deterministic CI/CD promotion and embedded governance controls transformed a high-risk migration into a predictable and auditable rollout, delivering a 70%+ reduction in execution time while protecting production stability through Databricks Unity Catalog.
If you are planning a Hive Metastore to Unity Catalog move, or your current migration is taking longer than expected, speak to a Unity Catalog expert to design a controlled and accelerated path forward.