Integrate with third-party tools
This document describes initial configuration steps you might need to take to manage the connection between BigQuery and your third-party business intelligence (BI) solutions. If you need assistance with a solution, consider contacting a BigQuery Ready partner.
All BI and data analytics solutions that are deployed on hosts and services with external IP addresses can access BigQuery through the public BigQuery REST API and the RPC-based BigQuery Storage API over the internet.
Third-party BI and data analytics solutions that are deployed on Compute Engine VM instances only with internal IP addresses (no external IP addresses) can use Private Google Access to reach Google APIs and services like BigQuery. You enable Private Google Access on a subnet-by-subnet basis; it's a setting for subnets in a VPC network. To enable a subnet for Private Google Access and to view the requirements, see Configuring Private Google Access.
Third-party BI and data analytics solutions that are deployed on on-premises hosts can use Private Google Access for on-premises hosts to reach Google APIs and services like BigQuery. This service establishes a private connection over a Cloud VPN or Cloud Interconnect from your data center to Google Cloud. On-premises hosts don't need external IP addresses; instead, they use internal RFC 1918 IP addresses. To enable Private Google Access for on-premises hosts, you must configure DNS, firewall rules, and routes in your on-premises and VPC networks. For more details on Private Google Access for on-premises hosts, see Configuring Private Google Access for on-premises hosts.
If you opt to manage your own instance of a third-party BI solution, consider deploying it on Compute Engine to take advantage of Google's network backbone and minimize latency between your instance and BigQuery.
If your BI solution supports it, you might consider setting
filters in report or dashboard queries whenever possible.
This step pushes the filters as
WHERE clauses to BigQuery. Although setting these filters
doesn't reduce the amount of data that BigQuery scans, it does
reduce the amount of data that comes back over the network.
For more information on network and query optimizations, see Migrating data warehouses to BigQuery: performance optimization and the Introduction to optimizing query performance.
API and ODBC/JDBC integrations
Google's BI and data analytics products like Looker Studio, Looker, Dataproc, and Vertex AI Workbench user-managed notebooks, and third-party solutions like Tableau, offer direct BigQuery integration using the BigQuery API.
For other third-party solutions and custom applications, Google has collaborated with Magnitude Simba to provide ODBC and JDBC drivers. The intent of these drivers is to help you leverage the power of BigQuery with existing tooling and infrastructure that doesn't integrate with the BigQuery API.
For more details, see the Google documentation on ODBC and JDBC Drivers for Google BigQuery.
The BigQuery API uses OAuth 2.0 access tokens to authenticate requests. An OAuth 2.0 access token is a string that grants temporary access to an API. Google's OAuth 2.0 server grants access tokens for all Google APIs. Access tokens are associated with a scope, which limits the token's access. For scopes associated with the BigQuery API, see the complete list of Google API scopes.
BI and data analytics solutions that offer native BigQuery integration can automatically generate access tokens for BigQuery either by using OAuth 2.0 protocols or customer-supplied service account private keys. Similarly, solutions that rely on Simba ODBC/JDBC drivers can also obtain access tokens for a Google user account or for a Google service account.