Imply is attempting to resolve the growing tension between exploding security telemetry volumes and tightening enterprise budgets by enabling direct, schema-less searches within data lakes. The company announced the general availability of Lumi Loglake, a solution designed to allow security teams to query unstructured machine data stored in object storage. This move addresses the operational friction typically required to make historical data usable for investigations and automated analysis.
Eliminating Data Rehydration and Schema Requirements
Lumi Loglake targets the technical hurdles that prevent organizations from effectively utilizing cost-efficient object storage for security telemetry. Currently, many teams must move, prepare, or rehydrate data to make it searchable, often creating duplicate pipelines and additional costs. Imply is positioning this tool to search data where it resides, including S3, supporting open formats such as Apache Iceberg, Delta Lake, Parquet, JSON, and GZIP. By removing the need for predefined schemas or data catalogs, the company aims to let security teams retain larger volumes of historical telemetry without the traditional overhead of data preparation or specialized search environments.
Unifying Real-Time and Historical Security Workloads
The technology functions as a unified search and access layer that bridges the gap between hot, real-time workloads and historical logs. This architecture allows organizations to decouple compute from storage, utilizing always-on compute for active monitoring while reserving on-demand compute for deep investigative workloads. Crucially, Imply is designing this to serve both human analysts and automated systems; security tools and AI agents can access the same underlying telemetry. This approach intends to preserve existing workflows, allowing analysts to use their current query languages, dashboards, and detections across both real-time and historical datasets.
Key Takeaways
- Lumi Loglake enables searching unstructured machine data directly in object storage without requiring schemas, catalogs, or data rehydration.
- The solution supports open data lake formats, including Apache Iceberg, Delta Lake, Parquet, JSON, and GZIP.
- The architecture decouples compute from storage, allowing for separate compute models for real-time monitoring and historical investigations.
TechInsyte's Take
In our view, Imply is making a calculated bet on the increasing necessity of "AI-ready" security data. As generative AI and automated agents require vast amounts of historical context to be effective, the ability to query massive, low-cost data lakes without expensive ETL processes becomes a strategic advantage. By targeting the "rehydration" bottleneck, Imply is attempting to turn passive, cold storage into an active asset for both human analysts and the next generation of autonomous security tools.
Questions & Answers
How does Lumi Loglake impact existing security analyst workflows?
The tool is designed to preserve existing workflows by allowing analysts to continue using their current query languages, dashboards, and detection processes across both hot and historical data.
What specific data formats are supported by the new service?
Lumi Loglake can search data in open formats including Apache Iceberg, Delta Lake, Parquet, JSON, and GZIP within object storage like S3.
In what way does this solution address the rising cost of security telemetry?
It allows organizations to retain large volumes of telemetry in cost-efficient object storage and search it in place, avoiding the need for expensive data movement or duplicate pipelines.
Can AI agents utilize the data provided by Lumi Loglake?
Yes, the company states that Lumi Loglake provides a single access layer that allows both traditional security tools and AI models or agents to access the same underlying telemetry.
Source: Businesswire
