Map the existing data systems
Identify data models, loading processes and reports in scope, including dependencies that affect the migration order.
Azure Databricks / Focused engagement
Plan your SQL Server to Databricks migration.
UTIS helps teams assess SQL Server and SSIS workloads for Azure Databricks modernization. We map business rules, data models, processing jobs and reporting dependencies before defining the target design. Migration scope includes reconciliation, acceptance checks, transition planning and support responsibilities; the right approach depends on your systems and the reason for the change.
Proposed migration approach
Plan the transition before the cutover.
- Map dependencies
SQL Server models, SSIS jobs and reports.
- Plan the target
Select models and Databricks workflows.
- Validate and transition
Agree comparisons, sequence and ownership.
A practical starting scope
What the project can include
We agree the priorities, dependencies and acceptance criteria before delivery begins.
Rebuild the selected models and pipelines
Plan the target data structure and convert the selected data-processing workflows for Azure Databricks.
Check the results and switch systems
Agree output comparisons, acceptance criteria, migration sequence and responsibilities for the transition.
Prepare the team to support the platform
Provide documentation and knowledge transfer for the people who will run and extend the new system.
What we agree together
Deliverables we agree with you
The related cases demonstrate SQL Server engineering and separate Azure Databricks delivery. Migration scope, schedule and acceptance criteria depend on your systems and are agreed before implementation.
- A scoped assessment of sources and dependencies
- A data-model and pipeline migration plan
- Agreed validation and transition criteria
- Documentation and knowledge transfer
Case studies
Relevant case studies
These examples show the related work and how each result was assessed.
Conceptual summary
ContextGeographic scope
UTIS contributionSource-data checks
Production checks confirmed
Property data
Property reporting that stands up to a source-data check
Client-validated
Reporting changes checked against source data after production release.
Evidence basis: client production checks and completed work records; full scope in the case.
Conceptual summary
BeforeFull-table reloads
AfterIncremental PySpark jobs
Semiconductor
Cut Databricks compute cost by ~57% across a fleet of machine-log pipelines
Standardized deployment with Databricks Asset Bundles and a Terraform-based pipeline, so every pipeline ships the same way.
~57%
lower average daily compute cost across the eleven pipelines (first days after the final migration vs. a five-month baseline).
How we measured it: platform-reported daily compute cost, averaged over the baseline period and the initial post-migration window; the same datasets continued to reach users throughout.
Conceptual summary
BeforeChange freeze
AfterDatabricks daily refresh
Semiconductor
Thirty business-critical reporting models kept changeable through an ERP change freeze
Before the freeze, identified the 30 business-critical HANA models out of 50.
30 of 50
models live in Databricks and refreshing daily.
How we measured it: model count and refresh status on the platform.
Results are specific to the engagements described. Client references are subject to client permission and confidentiality requirements.
Explore all case studiesChoose how you want to work with us.
Add an engineer to your team, commission a defined project or discuss ongoing support.
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Discuss a SQL Server migration.
Choose a preferred time to discuss which systems need to change and why. UTIS will confirm the meeting by email.
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Book a meetingUTIS will review your preferred time and confirm the meeting by email.