Lightdash vs BigQuery
Side-by-side trajectory, velocity, and editorial themes.
Lightdash chips away at the SQL barrier with NL-to-formula table calcs and metric-tree visualization.
The release cadence is high and the work spans three areas: lowering the technical barrier (spreadsheet-style formulas in table calculations, plain references to grand totals), enriching what a chart and dashboard can express (color palettes at every scope, row/column limits, rich-text table cells), and self-serve operability (default user spaces, expiring preview projects, dashboard-version rollbacks that include chart configs). The Canvas now hosts persistent metric trees, hinting at a heavier semantic-layer story.
Lightdash is positioning between a dbt-native semantic layer (where SQL-fluent analysts live) and a self-serve BI tool (where business users live). The intent-driven formula editor and reference-total functions chip away at the SQL prerequisite for table calculations, while Saved Trees push the metric model into something visually editable. Underneath, the platform is doing the unglamorous self-serve work — personal spaces, palette hierarchies, preview hygiene — that BI products need to survive in larger orgs.
Expect the formula editor to grow into broader AI-assisted authoring (filters, joins, custom dimensions) and Saved Trees to evolve into a more general semantic-layer view that consumes from dbt and produces governance artifacts. Color and palette work suggests embedded/customer-facing BI ambitions next.
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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