Take a break and join us for a live demo of Imply Lumi — the observability data layer built to help you store more, search faster, and reduce cost without changing your existing tools.
In this 30-minute session, you’ll see exactly how teams are using Lumi to do more with Splunk and modern observability stacks — without replatforming. We’ll also give you a first look at Lumi Loglake — our newest feature — which queries unstructured logs directly where they already live — in S3, Delta Lake, Apache Iceberg, and other open storage — bringing a true lakehouse (separated compute and storage) architecture to your observability and SIEM data.
We’ll walk through real workflows and answer your questions live.
What you’ll see:
- Lower observability costs Reduce platform spend by up to 70% with smarter storage and compression — and cut infrastructure and hardware costs by 40%+ with separated compute and storage. With Loglake, you only pay for what you actually query.
- Query your logs where they live Point Lumi at logs already sitting in S3, Delta Lake, or Apache Iceberg and start querying — no pipelines to build, no data to move or duplicate, no rigid schemas to define upfront.
- Faster investigations across all data Query large historical datasets in seconds — no rehydration, no archive recovery, no cold storage delays.
Schema-on-read, taken further Query unstructured and semi-structured logs in whatever shape they’re already in. Query first, optimize later — no schema-on-write pre-processing required. - Keep your tools, no disruption Works with Splunk and your existing workflows — no migration or retraining. Query the same datasets across Splunk (SPL), Grafana (LogQL), Databricks (Spark SQL), and standard SQL/BI tools, all from one shared telemetry foundation.
- Retain more data without trade-offs Eliminate sampling and filtering — keep full-fidelity observability data and scale data volumes independent of your SIEM/observability license costs.