Gong to BigQuery: How to Move Your Data

Moving Gong into BigQuery lets you connect what was said in sales conversations to what happened afterwards. Gong knows how calls went, who talked and for how long, and which topics came up. It does not know whether the deal closed, whether the customer renewed, or what they were worth.

This guide covers the managed path with Airbyte. Because the connector can sync call transcripts, this pipeline moves some of the most sensitive material your company holds, so the article treats the governance question as seriously as the configuration one.

Gong to BigQuery at a glance:

Why move data from Gong to BigQuery?

Two situations account for most of these pipelines.

The first is connecting conversations to outcomes. Joining call activity to your CRM and revenue data lets you ask whether the behaviours Gong measures actually correlate with winning, which is a question neither system can answer alone and the one sales leadership genuinely wants answered.

The second is coaching and enablement analysis over a long window. Gong's interface is built around recent calls and individual coaching, and questions about whether a training programme changed anything across two quarters need aggregation and history. If you only want to review last week's calls, Gong already does that well.

What do you need before you start?

One item needs a specific person and one needs a decision from outside the data team:

A Gong technical administrator. Both authentication routes require one. OAuth needs an administrator to authorise the connection, and API key credentials are generated under company settings, then ecosystem, then API, which only an administrator can reach. The Gong source documentation covers both.

The full scope list, granted in one pass. Basic and extensive call reading, transcript reading, user reading, scorecard and interaction statistics, and scorecard settings. Grant them together rather than incrementally, and if you use API key authentication copy the access key secret immediately, because it is displayed only once.

A decision about transcripts. Syncing them puts verbatim customer conversations in your warehouse. That is legitimate and valuable, and it is a decision for whoever owns privacy and customer commitments rather than one to make while configuring a connector.

A deliberate start date. Supplied in ISO-8601 format and applying to incremental streams. Set it explicitly rather than leaving it blank, because without one the first sync can reach a very long way back through your call history.

On the BigQuery side you need a project, a dataset and a service account with the BigQuery User and BigQuery Data Editor roles. Use a dataset dedicated to this source, because it will need its own access controls.

How do you build a Gong to BigQuery pipeline in Airbyte?

Step 1: Settle the transcript question first

Decide with your legal or privacy owner whether transcripts belong in the warehouse, and who will be able to read them once they are there. Most of the analytical value sits in the metadata and statistics rather than the words themselves, so if the answer is complicated, a pipeline without transcripts still answers most questions and is much easier to govern.

Step 2: Configure the Gong source

Click Sources in the left navigation, then New Source, and select Gong, following adding a source. Authenticate with OAuth, which handles token refresh automatically, or supply your access key and secret. Set the start date, and leave the concurrent threads setting alone unless you have a reason, for the reason below.

Step 3: Configure the BigQuery destination

Click Destinations, then New Destination, and select BigQuery, following adding a destination. Supply the project, dataset and service account key. Set the dataset location to match your CRM and revenue datasets, since BigQuery only joins datasets in the same physical location and that is the entire point of this pipeline.

Step 4: Select streams and schedule daily

Click Connections, then New connection, and select streams with a sync mode each. Users, calls and the activity and interaction statistics cover most analysis. Add transcripts only if step 1 concluded you should. Daily is the right cadence, since calls are recorded and processed rather than streamed.

Take the users and workspaces streams alongside whatever else you select, because call records reference people and workspaces by identifier and without them your dashboards show numbers against opaque IDs.

What is already protected, and what is not?

One protection is built in and worth knowing about. From version 1.1.0, the connector excludes calls marked as private in the Gong API from the calls and extensive call streams. So a conversation a salesperson deliberately marked private does not travel into your warehouse, which removes a category of accident.

Everything else is your responsibility. Call metadata identifies participants, including people outside your company, and transcripts contain whatever those people said. Once that is in BigQuery it is subject to your access controls rather than Gong's, and Gong's own permission model no longer applies.

So put this in its own dataset with deliberate grants, decide a retention period rather than accumulating indefinitely, and consider whether the analysis actually needs transcript text or only the statistics derived from it. A great deal of coaching analysis works on durations, talk ratios and scorecards, none of which reproduce what a customer said.

Why does raising the thread count not speed things up?

Because the ceiling is not concurrency, it is Gong's rate limit. The connector paces itself to three requests per second no matter how many threads you configure, so adding threads does not buy you more requests.

What it does help with is waiting. When streams are blocked on Gong taking time to respond rather than on the rate limit, running more of them in parallel uses that idle time. The default is four and the range is one to ten, so there is room to experiment where response latency rather than throughput is your problem.

The practical consequence is that a slow first sync is usually a start date problem rather than a tuning problem. Three requests per second is a firm ceiling, so a sync reaching back through years of calls will take as long as it takes. Narrowing the window is the lever that works.

Frequently asked questions

Can I sync call transcripts?

Yes, the connector supports a call transcripts stream, and the transcript read scope is part of the required permission set. Decide whether you should before deciding whether you can.

Are private calls included?

No. From version 1.1.0 the connector excludes calls marked as private in the Gong API from the calls and extensive call streams.

Who can create the credentials?

A Gong technical administrator. That applies to both OAuth authorisation and API key generation, so plan for that person's involvement.

Will more concurrent threads make my sync faster?

Not for throughput. The connector paces itself to Gong's three requests per second regardless of thread count, so raising it mainly helps when streams are waiting on Gong to respond.

Can I do this without writing code?

Yes. The setup above is entirely UI-driven. You will want BigQuery views joining call activity to your CRM data, which is where the useful analysis lives.

Get your Gong data into BigQuery

Settle the transcript question with your privacy owner before configuring anything, involve a Gong technical administrator early, grant the full scope list in one pass, and set a start date rather than leaving it open. Then keep the data in its own dataset with deliberate grants, because your warehouse controls replace Gong's once it lands.

Airbyte's connector catalog includes 600+ pre-built connectors, so conversation data can sit beside the CRM records that tell you how the deal ended. For that pairing, see Salesforce to Snowflake, and for another source carrying employee communications, Slack to Elasticsearch.

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