Features, pricing, ratings, and pros & cons — compared head-to-head.
Databricks Lakewatch is a commercial security information and event management tool by Databricks. Elastic Kibana is a commercial security information and event management tool by Elastic. Compare features, ratings, integrations, and community reviews side by side to find the best security information and event management fit for your security stack.
Based on our analysis of NIST CSF 2.0 coverage, core features, integrations, company size fit, here is our conclusion:
Enterprise SOCs drowning in petabyte-scale security data will find real value in Databricks Lakewatch because it actually stores and analyzes that volume without forcing you into expensive data movement or retention tradeoffs. The platform covers DE.CM and DE.AE strongly through agentic threat detection, though incident response automation (RS.MA and RS.AN) remains lighter than dedicated SOAR platforms. Skip this if your team needs out-of-the-box playbooks and tight third-party tool orchestration; Lakewatch assumes you can architect workflows on an open lakehouse foundation.
Security teams managing large volumes of log and event data will get the most from Elastic Kibana because its natural language query engine and no-code anomaly detection eliminate the need for deep Elasticsearch expertise to hunt threats. The tool maps directly to NIST DE.CM and DE.AE, meaning you get continuous monitoring and fast incident characterization without building custom detection logic. Skip this if your team needs turnkey incident response automation; Kibana is a search and analysis platform that requires you to own your alert tuning and escalation workflows.
Open agentic SIEM on Databricks lakehouse for petabyte-scale SOC ops.
Open source interface for querying, analyzing, and visualizing Elasticsearch data
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Common questions about comparing Databricks Lakewatch vs Elastic Kibana for your security information and event management needs.
Databricks Lakewatch: Open agentic SIEM on Databricks lakehouse for petabyte-scale SOC ops. built by Databricks. Core capabilities include Agentic AI-driven threat detection and response, Petabyte-scale security data ingestion and storage, Unified security data lakehouse architecture..
Elastic Kibana: Open source interface for querying, analyzing, and visualizing Elasticsearch data. built by Elastic. Core capabilities include Real-time data search and exploration with natural language input, Interactive dashboard creation with charts, graphs, maps, and tables, No-code machine learning for anomaly detection and rare event identification..
Both serve the Security Information and Event Management market but differ in approach, feature depth, and target audience.
Databricks Lakewatch differentiates with Agentic AI-driven threat detection and response, Petabyte-scale security data ingestion and storage, Unified security data lakehouse architecture. Elastic Kibana differentiates with Real-time data search and exploration with natural language input, Interactive dashboard creation with charts, graphs, maps, and tables, No-code machine learning for anomaly detection and rare event identification.
Databricks Lakewatch is developed by Databricks. Elastic Kibana is developed by Elastic. Vendor maturity, funding stage, and team size can be important factors when evaluating long-term viability and support quality.
Databricks Lakewatch integrates with Databricks Unity Catalog, Databricks Delta Lake, Databricks Delta Sharing, AWS, Azure and 1 more. Elastic Kibana integrates with Slack, PagerDuty, ServiceNow, AWS, Google Cloud and 3 more. Check integration compatibility with your existing security stack before deciding.
Databricks Lakewatch and Elastic Kibana serve similar Security Information and Event Management use cases: both are Security Information and Event Management tools, both cover Log Management. Review the feature comparison above to determine which fits your requirements.
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