Migration
Move off your current platform to Databricks. Without the risk.
Migration risk is real. Most of it comes from how the migration is run.
The Approach
Lift and shift or re-architect, depending on what you need.
Every Datapao migration starts with understanding your current setup, not with migrating code. We look at how your workloads are used, what's working well, and what you need from Databricks. Based on that, we recommend whether to move things across as they are, re-architect them, or combine both.
Architecture-first. Not code-first.
Target architecture, data flows, Unity Catalog structure, and governance model are designed and approved before migration begins. The design is based on what you need from Databricks, whether that means keeping existing patterns or re-architecting them.
Migrated in sequence. Not all at once.
Every migrated workload is tested against production data volumes and against your actual SLAs, not synthetic test data. Performance, cost, and correctness are verified at production scale before cutover. What goes live has already proven it can hold.
Owned by your team. Not dependent on us.
When we're done, your team can operate, extend, and troubleshoot the platform on their own. Knowledge transfer happens throughout the engagement, and the handover includes the runbooks and documentation they'll need.
From your current platform to Databricks
Structured to deliver.
Not to stall in discovery.
A thorough review of your current setup: schemas, pipelines, data volumes, usage patterns, and dependencies. The output is a migration roadmap with clear scope, sequencing, workload prioritisation, and risk assessment.
Target Lakehouse architecture design: ingestion patterns, data flows, Unity Catalog structure, governance model, and security controls. Reviewed and approved before migration begins.
Workloads move to Databricks in prioritised phases, following the approved architecture. Each phase covers pipelines, transformations, data, and access controls, and is signed off before the next begins.
Every migrated workload is tested end to end and signed off with your team before go-live. Anything that needs attention is resolved first, so cutover can go ahead with confidence.
Production cutover, carried out together with your team. Runbooks and operational documentation build on the knowledge transfer that's happened throughout the engagement. When we're done, your team can operate, extend, and troubleshoot the platform on their own.
What migrations deliver in practice
From snow guns to smart systems: TechnoAlpin’s data transformation with Datapao
Two focused migrations.
One standard of delivery.
Snowflake to Databricks Migration PoV
- Workload selection and assessment
- Architecture design for the migrated workload on Databricks
- Migration of selected pipelines, transformations, and data
- Performance and cost comparison against the Snowflake baseline
- Findings report with full migration roadmap
Unity Catalog Reference Migration
- Assessment of current metastore structure and governance model
- Unity Catalog architecture design (namespaces, access controls, lineage)
- Migration of existing tables, views, and permissions
- Cross-workspace configuration and identity federation setup
- Governance documentation and operational runbook
