BigQuery, Unified on Matia: Expanded Observability, Formulas Plus a New Reverse ETL Source

BigQuery is where many enterprise data teams run their warehouse. Matia is the control plane around it: ingesting data into BigQuery, observing every table, cataloging the estate, and activating warehouse-modeled data out to the operational tools your teams rely on. Today's release deepens three of those layers in direct response to enterprise customer demand. Observability coverage expands across BigQuery workloads, spanning auto-generated monitors, ML Monitors, custom monitors, custom SQL, and schema change tracking. Salesforce Formulas, a big pain point many teams face when ETLing data, now can move seamlessly into BigQuery. And BigQuery is now available as a Reverse ETL source. Both land on the same shift-left foundation: catch data and pipeline issues at the BigQuery workload itself, before they cascade into broken dashboards, bad ML inputs, or silent downstream failures. Here's what's new.

Expanded Observability for BigQuery

BigQuery customers now get the full set of Matia monitor types, covering data quality, profiling, freshness, schema, and query performance in one place. Every monitor runs against the BigQuery workload directly, keeping data quality checks close to the source.

Auto-Generated Monitors

Matia automatically generates monitors on your BigQuery tables as they're discovered, measuring freshness to surface delays and gaps, and tracking row counts to catch unexpected swings. No manual setup required.

ML Monitors

Profile your BigQuery data on a schedule to track trends and flag anomalies automatically. ML Monitors baseline normal behavior for a metric and alert when values drift outside the expected range, catching data quality issues that fixed thresholds would miss.

Custom Monitors

Configure monitors on BigQuery tables to match the accuracy and reliability standards that matter most to your business. Set your own thresholds, cadences, and alert rules on the metrics your team cares about.

Custom SQL Monitors

For checks auto-generated and custom monitors can't express, write your own in SQL. Custom SQL monitors run on your schedule against BigQuery and alert when the query returns unexpected results, ideal for business logic validation and bespoke data quality rules.

Schema Change Monitoring

Detect column additions, deletions, and type changes on BigQuery tables so your team can react before upstream schema drift breaks downstream pipelines, dashboards, and ML models.

BigQuery Now Supports Salesforce Formulas

BigQuery's new Salesforce formula support works by retrieving formula expressions directly from Salesforce object metadata via the Describe API, parsing each expression, and translating it into equivalent BigQuery SQL. 

The result is a generated table with formulas in native SQL - the base table with the translated formula expressions, giving you the ability to calculate the fields as a view alongside your raw data. This means formulas involving logic, text manipulation, math, date operations, and even cross-object references through relationship traversals can be evaluated natively in BigQuery, keeping your analytics layer in sync with how your team has modeled the data in Salesforce.

BigQuery Is Now a Reverse ETL Source

BigQuery is now a native source for Matia's Reverse ETL capabilities.. Activate your warehouse-modeled data in the operational tools your GTM, product, and support teams live in, without routing through another system first. It's the same control plane that handles ingestion, observability, and catalog, now closing the loop back out to your stack.

Incremental Sync Support

Move only what changed. Incremental sync watches your BigQuery tables for new and updated rows by reading the journal of row-level changes - inserts, updates, and deletes - that have occurred since the last sync. Only those changes are fetched and pushed to your downstream destinations, giving you faster syncs, lower compute spend, and fresher data in the tools that matter.

Ready to set it up? Check out the BigQuery as a source documentation, or see the full 

list of Reverse ETL capabilities here.

Better Together: BigQuery + Matia

BigQuery customers no longer have to stitch together a separate tool for each layer of the lifecycle. Matia ingests into BigQuery, observes every table, catalogs the estate, and now activates warehouse-modeled data out to your operational stack, on one platform with one SLA. And because every layer sits close to the BigQuery workload, data quality, freshness, schema, and performance issues get caught at the source, before they reach Looker dashboards, ML models, and GTM tools that depend on them.