Overview

A leading pharmaceutical company using Splunk was running Cribl Stream for log routing and transformations, handling nearly 5 TB of data per day. The Cribl licensing model and operational overhead were pushing costs up, so the customer wanted to move to Splunk Ingest Processor while keeping the same optimization logic and without introducing risk or downtime.

The goal was simple and painful at the same time: replicate mature Cribl pipelines inside Splunk Ingest Processor, keep ingestion optimized, and cut out an expensive dependency without breaking anything.

Challenges

• High licensing costs - Cribl pricing on 5 TB per day of traffic had become a significant operational expense.
• Complex data pipelines - Existing Cribl pipelines implemented critical transformations and data reduction logic that preserved data value while cutting volume.
• Limited Ingest Processor expertise - The customer team had little hands-on experience with Splunk's Ingest Processor, which is relatively new.
• Zero downtime requirement - Migration needed to be invisible to downstream users, with no data loss or outage during the cutover.

Our Streamlined Migration Approach

Assessment and planning

We started by dissecting the existing Cribl deployment: routes, pipelines, functions, and transforms. This analysis allowed us to design a 1 to 1 mapping into Splunk Ingest Processor so that the behavior of each pipeline could be replicated, not just roughly approximated.

Dual feed pipeline implementation

To avoid surprises, we implemented a dual feed model:

• Feed 1 - Raw, unmodified data sent through Cribl into Splunk as before.
• Feed 2 - The same data sent into Splunk Ingest Processor, where equivalent transformations and optimization logic were applied.

Testing and validation

We ran both feeds in parallel and validated:

• Data integrity and event completeness.
• Correct application of field extractions and data reduction rules.
• Performance characteristics that matched or exceeded the Cribl pipelines.

Seamless cutover with no downtime

After the side by side comparison met acceptance criteria, we switched traffic fully to the Ingest Processor pipelines and decommissioned the Cribl path. From the perspective of users and dashboards, nothing broke and nothing went dark.

Results and Benefits

• Material cost savings - Moving 5 TB per day into Splunk's native Ingest Processor significantly reduced Cribl related licensing and infrastructure costs.
• Simplified stack - One less moving part in the logging pipeline, fewer things to upgrade, monitor, and troubleshoot.
• Zero disruption - Migration completed with no downtime and no impact to SOC workflows or reporting.
• Upskilled internal team - We delivered hands on enablement sessions so the customer's engineers could confidently own and extend Ingest Processor pipelines going forward.

Conclusion

By migrating from Cribl Stream to Splunk Ingest Processor, this pharmaceutical customer cut recurring costs, removed complexity from their data pipeline, and kept their ingestion optimization intact. The combination of structured discovery, dual feed testing and guided enablement meant the move was both low risk and high impact.

If you are looking to rationalize ingest tooling, reduce spend, and lean into Splunk's native capabilities, our approach gives you a proven path without gambling with your production data.