Case study
Splunk to Elastic Migration Stabilization
How SquareShift resolved post-migration data-loss and lag issues for a US IT services provider moving from Splunk to Elastic.
Most reported data loss after a migration is duplicate suppression from a leftover filter, not a missing record — diagnose the difference before building pipelines to fix it.
Platform & Software Engineering Practice Lead, SquareShift
A US IT services provider managing a global infrastructure and log management footprint.
The company migrated its log management platform from Splunk to Elastic and needed to stabilize the environment afterward.
- Dead Letter Queue pipeline — Isolates failed records instead of silently dropping them.
- Root-cause diagnosis — Traced data lag to local-agent-to-cluster synchronization delays.
- Multi-line event enrichment — Propagates headers across records for full traceability.
- Dashboard realignment — Custom transformation configs restore consistent business reporting.
How SquareShift delivered it.
The challenge
The client, a US IT services provider, had migrated its log management platform from Splunk to Elastic — but the move left behind synchronization delays between local agents and the central cluster, plus configuration gaps that put data integrity at risk. Some records reported as lost were tangled up with duplicates from leftover filter configurations, and no one could tell the two apart with confidence.
Business reporting depended on legacy dashboard visualizations that the new pipeline hadn’t fully replicated, and the system had no real-time way to catch and isolate ingestion failures as they happened.
What we delivered
SquareShift ran diagnostic analysis on the system logs to separate genuine data loss from duplicate records caused by filter configuration, then built Dead Letter Queues to isolate failed records going forward instead of dropping them silently.
The team developed enrichment logic to propagate event headers across multi-line documents for full traceability, and delivered custom transformation configurations to realign the complex data consolidation the legacy dashboards depended on.
The payoff
The client’s log management environment now runs on a stabilized, Elastic-based pipeline with failed records isolated through Dead Letter Queues rather than lost. Root-cause diagnosis resolved the local-agent-to-cluster lag, and realigned dashboard transformations restored consistent business reporting on the new stack.
Where this work sits