Assess the current workflows
Map the selected data-processing jobs, sources, users and support requirements. Record output checks, processing time and compute cost.
Azure Databricks / Focused engagement
Manufacturing data engineering on Azure Databricks.
Improve the workflows that turn machine logs and source-system data into usable information. UTIS can assess selected workloads, modernize their processing and validate the results on Azure Databricks.
Databricks · Manufacturing
A scoped path for machine data.
- Select the workload
Machine logs, telemetry or reporting inputs.
- Improve processing
Assess incremental work and deployment.
- Validate the transition
Compare outputs and agree support.
A practical starting scope
What the project can include
We agree the priorities, dependencies and acceptance criteria before delivery begins.
Improve processing and deployment
Assess where PySpark, incremental Delta processing and repeatable deployment will benefit the selected workloads.
Validate and plan the transition
Compare outputs and job costs. Agree the switch to the new system and distinguish tested milestones from daily operational use.
Prepare the support team
Document the pipelines, responsibilities and steps needed to maintain and extend them.
What we agree together
Deliverables we agree with you
This is a scoped consulting engagement. Deliverables and acceptance criteria are agreed for your systems; published case results are not guaranteed outcomes.
- An inventory of agreed workloads and dependencies
- A scoped processing and deployment plan
- Output checks and cost-comparison results
- Documentation and agreed support responsibilities
Case studies
Relevant case studies
These examples show the related work and how each result was assessed.
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.
Compare engagement modelsMeet with UTIS
Discuss a manufacturing data challenge.
Choose a preferred time to discuss which systems need to change and why. UTIS will confirm the meeting by email.
Choose a preferred time
Book a meetingUTIS will review your preferred time and confirm the meeting by email.