May 2026 Summaries

Imply Lumi Loglake is introduced as an innovative solution for querying unstructured logs directly where they reside, without the need for preprocessing or rigid schema definition. This approach addresses the challenge in modern observability architectures where teams must make premature decisions about telemetry data retention and indexing due to infrastructure costs. By utilizing a decoupled architecture that combines real-time indexing, elastic compute, and in-place querying, Lumi Loglake enables immediate querying of telemetry data across multiple observability tools and lakehouse ecosystems without the need for duplicating storage or rebuilding pipelines. This shift from a pipeline-first to a query-first model allows organizations to retain larger datasets, conduct investigations without archive recovery, and leverage open storage environments for scalable observability, ultimately reducing operational costs and complexity.

Imply Lumi is set to introduce a major expansion to its observability warehouse, focusing on decoupled observability/SIEM architectures that separate storage, compute, and access. This includes the introduction of Imply Lumi Log Lake, which allows querying unstructured logs directly in object storage without predefined schemas, eliminating the need for complex data pipelines. The expansion aims to integrate seamlessly with existing platforms like Splunk, Databricks, and Grafana, enabling organizations to access observability data without replacing current tools. New infrastructure management approaches, such as Virtual Tier and Elastic Compute, are designed to optimize cost and efficiency by aligning resources according to workload demands, allowing independent scaling of investigative and real-time workloads. These advancements are part of a broader shift toward more flexible and scalable observability architectures, with further insights to be shared at the upcoming Databricks Data + AI Summit.

Imply Lumi's integration with Grafana Loki, now in Private Preview, aims to eliminate the need for separate ingestion pipelines and duplicate data storage by enabling native querying of logs loaded into Lumi through Grafana using LogQL. This integration allows users of both Splunk and Grafana to manage their data more efficiently, as it unifies the dataset, reducing the complexity and cost associated with managing two distinct platforms. Lumi acts as a Loki backend, meaning existing Grafana workflows and integrations remain unaffected, and users can seamlessly transition to using this new setup without additional custom data source plugins. The setup process is straightforward, involving a few configuration steps within the Lumi Integrations page and Grafana, and detailed instructions are available in the Lumi documentation. This development signifies a step towards more streamlined observability by facilitating one ingestion point and dataset, accessible through multiple ecosystems, thereby offering a more flexible and cost-effective solution for organizations managing complex data environments.