Kubit vs BigQuery
Side-by-side trajectory, velocity, and editorial themes.
Kubit pivots from query builder to agentic analytics with the Lumos AI chat.
Kubit is a product-analytics platform that has spent the last quarter shifting from a manual report-builder model toward an agentic one. The headline move is Lumos Agentic AI Chat, which lets users describe reports in natural language instead of clicking through a builder. Alongside it, an AI Readiness framework continually scores how well a customer's metadata is prepared for that workflow.
Every directional release this quarter either ships agentic capability or removes blockers in front of it. AI Readiness keeps expanding its assessment surface (virtual events, breakdown fields) so customers can see exactly what gaps would limit Lumos. Enterprise-readiness work like granular Slack permissions and partial caching is clearing the path for production rollout rather than chasing new categories.
Expect Lumos to extend past chat into scheduled agent runs and proactive insights surfaced on dashboards, with a Slack or Teams entry point built on the new fine-grained permission model.
BigQuery doubles down on Iceberg, graph, and global data sharing as the lakehouse fight intensifies.
BigQuery's May 2026 ship list is dominated by three tracks: open-format lakehouse integration (Iceberg v3 with deletion vectors, REST catalog support in Conversational Analytics), graph capabilities maturing inside BigQuery Studio, and global data exchange via multi-region sharing listings reaching GA. Alongside the feature work, Google is tightening Data Transfer Service security (MFA on Google Ads transfers) and warning about Ads retention changes that will cap historical backfills from June 1. The release notes show a mature warehouse continuing to absorb adjacent workloads rather than reinventing itself.
BigQuery is positioning itself as the federated query and sharing fabric for a multi-format world, with Iceberg getting closer to first-class status and Conversational Analytics extending across external catalogs. The graph and notebook work signals a push to keep more analytical work inside Studio instead of bouncing to specialized tools. Expect continued layering of governance, AI-assisted query, and open-table support on top of the existing engine rather than core engine reinvention.
Next obvious step is GA for Iceberg v3 features and full conversational graph querying without Preview gating. Watch for additional first-party data sources getting MFA mandates, mirroring the Google Ads tightening.
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