Integrate with third-party tools

This document describes initial configuration steps you might need to take tomanage the connection between BigQuery and your third-partybusiness intelligence (BI) solutions. If you need assistance with a solution,consider contacting aGoogle Cloud Ready - BigQuerypartner. Third-party software is notsupported by Cloud Customer Care when it has been identified thatBigQuery is working as intended.

Network connectivity

All BI and data analytics solutions that are deployed on hosts and serviceswith external IP addresses can access BigQuery through the publicBigQuery REST API and the RPC-basedBigQuery Storage API over the internet.

Third-party BI and data analytics solutions that are deployed onCompute Engine VM instances only with internal IP addresses (no external IP addresses) can usePrivate Google Access to reach Google APIs and services like BigQuery. You enablePrivate Google Access on a subnet-by-subnet basis; it's a setting for subnetsin a VPC network. To enable a subnet for Private Google Access and to view therequirements, seeConfiguring Private Google Access.

Third-party BI and data analytics solutions that are deployed on on-premiseshosts can usePrivate Google Access for on-premises hosts to reach Google APIs and services like BigQuery. This serviceestablishes a private connection over aCloud VPN orCloud Interconnect from your data center to Google Cloud. On-premises hosts don'tneed external IP addresses; instead, they use internalRFC 1918 IP addresses. To enable Private Google Access for on-premises hosts, youmust configure DNS, firewall rules, and routes in your on-premises and VPCnetworks. For more details on Private Google Access for on-premiseshosts, seeConfiguring Private Google Access for on-premises hosts.

If you opt to manage your own instance of a third-party BI solution, considerdeploying it onCompute Engine to take advantage of Google's network backbone and minimize latency between yourinstance and BigQuery.

If your BI solution supports it, you might consider settingfilters in report or dashboard queries whenever possible.This step pushes the filters asWHERE clauses to BigQuery. Although setting these filtersdoesn't reduce the amount of data that BigQuery scans, it doesreduce the amount of data that comes back over the network.

For more information on network and query optimizations, seeMigrating data warehouses to BigQuery: performance optimization and theIntroduction to optimizing query performance.

API and ODBC/JDBC integrations

Google's BI and data analytics products likeLooker Studio,Looker,Dataproc,andVertex AI Workbench instances,and third-party solutions likeTableau,offer direct BigQuery integration using theBigQuery API.

For other third-party solutions and custom applications, Google has collaborated withMagnitude Simba to provideODBC andJDBC drivers.The intent of these drivers is to help you leverage the power ofBigQuery with existing tooling and infrastructure that doesn'tintegrate with theBigQuery API.

For more details, see the Google Cloud documentation onODBC and JDBC Drivers for BigQuery.

Authentication

The BigQuery API usesOAuth 2.0 access tokens to authenticate requests. An OAuth 2.0 access token is a stringthat grants temporary access to an API.Google's OAuth 2.0 server grants access tokens for all Google APIs. Access tokens are associated with ascope,which limits the token's access. For scopes associated with theBigQuery API, see the completelist of Google API scopes.

BI and data analytics solutions that offer native BigQueryintegration can automatically generate access tokens for BigQueryeither by usingOAuth 2.0 protocols or customer-suppliedservice account private keys.Similarly, solutions that rely on Simba ODBC/JDBC drivers can also obtain accesstokensfor a Google user account orfor a Google service account.