Fairing vs BigQuery
Side-by-side trajectory, velocity, and editorial themes.
Fairing pushes its post-purchase survey data deeper into the analytics stacks ecommerce teams already live in.
Fairing is concentrating on making its survey responses (attribution, NPS, demographics) a first-class data source elsewhere — Shopify Analytics, Hazel, ESPs for NPS embeds. The in-app product is getting cleanup work too: bulk recategorization of write-ins, automated reclassification of exact matches, faster monthly reporting filters. The Shopify Checkout extension story has filled in with native preview tooling.
The product's bet is shifting from 'collect post-purchase survey data' to 'become the post-purchase data layer plugged into the rest of the ecommerce stack'. The Shopify Order Metafields sync removes a real friction point — analysts no longer need to export and join. Pairing with Hazel's AI analytics suggests Fairing wants to be the data source, not the analytics destination.
More integrations with ecommerce data warehouses and CDPs are likely next, since the metafield/sync pattern is repeatable. Expect attribution-specific functionality (multi-touch reconciliation, channel mapping helpers) to land soon — recategorization tooling is foundation work for it.
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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