01 / Inside the engagement
Situation
High-volume operational telemetry landed in an on-premises Hadoop data lake that was hard to scale, hard to access and kept the data away from the Databricks tools the data scientists wanted to use.
Before
- Telemetry ingestion
- Hadoop data lake
- Three destinations
After
- Telemetry ingestion
- Validated Azure path
- Databricks access
Simplified workflow. The case text describes the contribution, delivery stage and evidence.
Explore the full conceptual diagram
02 / Inside the engagement
What UTIS did
- Owned the migration end to end, with a zero-loss validation framework as the gate for decommissioning.
- Planned and pre-communicated the cutover; when the production transfer path failed mid-flight, redesigned it from Spark to per-file-trackable AzCopy.
- Collapsed a three-destination legacy fan-out into a single Azure path, then built the native acquisition and ingestion pipelines that replaced the old feed.
03 / Inside the engagement
Results
- Thirteen datasets migrated on schedule.
- Legacy system decommissioned: hardware, licensing and support retired.
- Raw-log ingestion 35% faster.
Why it matters to you
Moving the data is the easy half. The saving arrives only when the old platform is switched off, and that takes a validation gate strong enough to sign the decommission order.
Stack
How we measured it: validation-framework reconciliation counts; ingestion throughput before and after.