Marketing data connectors forGoogle BigQuery

Google BigQuery is a fully managed, serverless data warehouse built on Google Cloud Platform that enables fast SQL analytics over petabyte-scale datasets using a pay-per-query pricing model. Launched in 2011, it was among the first enterprise data warehouses to separate storage from compute, allowing organizations to scale each independently without provisioning servers or managing infrastructure.

BigQuery stores data in columnar format and executes queries through a distributed architecture that can scan terabytes in seconds. It supports standard SQL, nested and repeated fields, and integrates natively with the Google Cloud ecosystem including Google Ads, Google Analytics 4, and Looker Studio (formerly Data Studio). Data teams use it as a central repository for structured and semi-structured data, running ad-hoc analysis, scheduled reporting, and machine learning workflows through BigQuery ML. For marketing teams specifically, BigQuery solves the problem of data fragmentation: instead of pulling reports from individual platforms, teams can load all marketing data into one warehouse and query it with SQL.

Marketing teams adopt BigQuery to solve three recurring problems: fragmented data silos, slow manual reporting, and the inability to run cross-channel attribution at scale.

First, **unified cross-channel analysis**. BigQuery allows marketers to consolidate data from advertising platforms, CRMs, web analytics, and offline sources into a single schema. This makes it possible to calculate true customer acquisition cost across channels, identify overlapping audiences, and build custom attribution models that no individual platform provides.

Second, **automated reporting at scale**. Instead of exporting CSVs or relying on spreadsheet-only workflows, teams can schedule SQL queries to refresh dashboards hourly or daily. This eliminates version-control issues and reduces the time spent reconciling numbers between platforms.

Third, **machine-learning-ready data infrastructure**. BigQuery ML lets teams build predictive models—such as churn probability or lifetime value estimates—directly on warehouse data without moving it to separate tools. Teams typically choose BigQuery over general-purpose BI tools when their data volume exceeds what in-memory or local databases can handle, when they need to join large datasets from multiple sources, or when they want to reduce infrastructure overhead by using a fully managed service.

A BigQuery connector is a piece of software that brings data from a third-party tool (like Meta Ads or Shopify) into a BigQuery report, so you can build dashboards on top of it. Porter provides managed connectors that handle authentication, schema and rate limits for you.

Porter connectors are partner connectors — they appear inside the BigQuery connector gallery automatically once you sign up. You don't need to install anything manually; just search "Porter" when adding a new data source in BigQuery.

Yes. Porter's free plan gives you 3 data source accounts across any connectors with a 30-day data history. You can upgrade anytime without reconnecting accounts. BigQuery itself is also free.

Porter refreshes data on a scheduled basis (hourly or daily depending on the source) and caches it for fast BigQuery loading. You can also force a refresh manually from your Porter dashboard.

No. Porter connectors are available inside the BigQuery partner connector gallery as soon as you create a Porter account. Just add a new data source inside BigQuery and search for the connector you need.

Yes. Porter pre-standardizes field names across sources, so you can blend Meta Ads + Google Ads + GA4 (and more) in a single BigQuery report with matching campaign, date and UTM fields.

Porter stores a BigQuery copy of your data as a safety net. If you hit Meta's, Google's or TikTok's API rate limits, Porter automatically falls back to the stored version so your dashboards keep working.