Data Platform Modernisation
Platform modernisation projects fail in a predictable way: the new platform is built, the old one cannot be switched off because nobody knows what still depends on it, and the organisation runs both for years at double the cost.
We plan for the cutover from the first week — usage analysis, dependency mapping, parallel running with reconciliation, and an actual decommissioning schedule with owners against each item.
Platforms we typically deliver this on
Scope of the service
Lakehouse implementation
Medallion architecture on Databricks or equivalent, with governed tables, time travel and workload isolation.
Cloud data warehouse build
Snowflake, Microsoft Fabric, BigQuery or Synapse implementations sized against real workloads and real budgets.
Legacy warehouse migration
Moving from on-premises Oracle, SQL Server, Teradata or Netezza with logic translation and reconciliation testing.
Semantic layer
One governed definition of each business measure, consumed identically by BI tools, notebooks and applications.
Cost and performance engineering
Partitioning, clustering, caching, warehouse sizing and workload management, with spend attributed to teams.
Cutover and decommissioning
Parallel running, reconciliation, user migration and a decommissioning plan with named owners and dates.
Where this is used
Representative engagements, described at the level our clients permit. Sector and shape are accurate; identifying detail is withheld.
From an unsupported on-premises warehouse to a governed lakehouse
An ageing warehouse could not support new analytics or AI. We built a lakehouse with in-region storage, migrated the reporting layer, and ran both in parallel with automated reconciliation until sign-off.
Outcome — Cutover completed with reconciled numbers rather than a leap of faith.
Consolidating three warehouses after acquisitions
Three platforms with conflicting product and customer definitions. We built a single lakehouse with a conformed semantic layer and migrated in sequence.
Outcome — One version of the product and customer master across the merged business.
Meeting residency requirements during modernisation
Data could not leave the region. We designed the platform within in-region cloud services, with an explicit data-flow map for the privacy office.
Outcome — Modern platform capability without moving regulated data out of region.
Before you get in touch
Lakehouse or data warehouse?
It depends on workload mix. If you have substantial unstructured data, data science or AI ambitions, a lakehouse usually wins. For structured BI at moderate scale, a cloud warehouse is often simpler and cheaper.
How do you avoid running two platforms forever?
Usage analysis and dependency mapping at the start, a named owner for each object to be retired, and decommissioning treated as a deliverable with a date rather than an aspiration.
Is Workiy a Databricks partner?
Yes — Workiy works with Databricks through its Consulting & System Integrator partner programme, alongside partnerships with Microsoft, Oracle, AWS and Acquia.
Often engaged alongside this
Data Engineering & Pipelines
Ingestion, transformation and orchestration built with tests, lineage and alerting — so a broken feed is an alert, not a discovery three weeks later.
Read moreData & AnalyticsData Migration & Integration
Moving data between platforms with the testing that lets you sign off — row counts, control totals, business-rule validation and a documented rollback.
Read moreAIAI Architecture & Platform Engineering
Model choice is the easy part. We design and build the platform layer — data access, identity, evaluation, observability, cost control — that everything else depends on.
Read moreStart with three weeks and a straight answer
The AI Readiness Assessment is fixed in scope, fixed in price and produces four deliverables you own — whether or not you continue with us.