BigQuery Overview

Prizm connects to Google BigQuery to catalog your projects, datasets, tables, and columns, bring in tags and policy tags, build lineage, and report usage and performance. Once connected, that metadata appears in the Prizm catalog alongside your other sources.

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Why Connect BigQuery to Prizm?

Catalog & Context Prizm discovers projects, datasets, tables, and columns, including nested fields, and adds them to the Prizm catalog. Tags & Classification Labels, policy tags (with their column mappings), resource tags, and Data Catalog tags and aspects are collected so governance context follows the data. Lineage Prizm builds lineage from either the Google Data Lineage API (Dataplex) or BigQuery job logs, depending on the lineage mode you choose when you connect. Usage & Performance Job history gives you who is querying what, how often, and how those queries perform.

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What Prizm Extracts

See What We Collect for the source of each item.

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Authentication

Prizm supports two ways to authenticate to Google Cloud:

• Service account key. A JSON key file for a service account that has the roles Prizm needs. You upload it on the connection form, or retrieve it from a vault.
• Workload Identity. Keyless authentication using Google’s identity federation. It appears on the connection form, but service account key is the method to use today. Workload Identity hasn’t been verified end to end.

See Setup for the APIs to enable and the roles to grant.

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Lineage Sources

When you connect, you turn Extract Lineage on or off and choose a Lineage Mode:

• Via DataPlex API (default). Uses the Google Data Lineage API. It covers query, load, and copy jobs that ran after the API was enabled.
• Job Logs. Derives lineage from BigQuery job history. Use it if you can’t enable the Data Lineage API, or you need history from before it was enabled. Source and destination tables are read from each job, so lineage from DML targets and from views is less complete.

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Next Steps