Auth0 to BigQuery: How to Move Your Data
Move Auth0 into BigQuery with Airbyte. Why the connector carries your directory rather than login activity, and how appended snapshots reveal access changes.
Moving Auth0 into BigQuery puts your identity directory beside the commercial data that gives it meaning. Which accounts exist, which organisations they belong to and what roles they hold are questions worth answering next to contract value and support history.
This guide covers the managed path with Airbyte. Two things shape the build, and the first decides whether this is the project anybody asked for: the connector carries your directory rather than your login activity.
Auth0 to BigQuery at a glance:
Why move data from Auth0 to BigQuery?
Two situations account for most of these pipelines.
The first is understanding your user base commercially, which means joining accounts and organisations to revenue, support load and product usage. None of that lives in an identity provider, so the join can only happen somewhere both sides exist.
The second is access review, since who belongs to which organisation with which role is exactly what a periodic review asks. If your work is heavy transformation over that data rather than SQL reporting, Auth0 to Databricks suits that shape better.
What do you need before you start?
Four things, and the first is a conversation rather than a credential:
Agreement that sign-in activity is out of scope. The available streams describe users, clients, organizations and organisation memberships. Login events are not among them, and anybody expecting them should hear so now. The Auth0 source documentation lists what exists.
A machine to machine application, authorised against the Management API. The documentation is explicit that scheduled production calls want an integration the connector can refresh itself rather than a token you pasted in.
A BigQuery dataset in the right location. Location is fixed at creation and BigQuery will not join across locations, so put this where your customer and product data already sit.
A decision about keeping history. The directory describes the present, so whether you can answer questions about how access changed is something you choose at setup.
If your network restricts traffic by IP, add the Airbyte Cloud IP addresses to the relevant allow list before you begin.
How do you build an Auth0 to BigQuery pipeline in Airbyte?
Step 1: Establish what nobody is getting
Ask what questions this data is meant to answer and check them against a stream list covering users, clients, organisations and memberships. Questions about who exists and what they can access are well served. Questions about who signed in last Tuesday, from where, and whether it failed are not, and those are exactly what people mean when they ask for Auth0 data in the warehouse.
Step 2: Configure the Auth0 source
Click Sources in the left navigation, then New Source, and select Auth0, following adding a source. Supply your domain and the credentials of your machine to machine application, granting read scopes only. Nothing this pipeline does requires the ability to change anything in your identity provider.
Step 3: Configure the BigQuery destination
Click Destinations, then New Destination, and select BigQuery, following adding a destination. Supply the project identifier, dataset and service account credentials. Give this its own dataset with narrow access, since a table of every account in your product is not something to leave in a general reporting area.
Step 4: Create the connection and choose append
Click Connections, then New connection, select your streams and a sync mode. Daily is ample for a directory. Prefer accumulating over overwriting, because the difference decides whether you can answer the access questions covered below.
This connector is community maintained rather than certified, so run it for a fortnight before anything depends on it.
What does this connector actually carry?
Your directory: the users in your tenant, the applications registered against it, the organisations you have defined, and which users belong to those organisations with which roles. That is a coherent and genuinely useful picture of who exists and what they are entitled to.
What it does not carry is activity. Auth0 records sign-ins, failures and administrative actions as log events, and those are not among the connector's streams, so this pipeline cannot tell you who authenticated, when, from which address or how often. That is the single most common expectation people bring to an identity pipeline.
Say so early, because the distinction is easy to miss and expensive to discover. A security team asking for Auth0 in the warehouse usually wants the activity; a commercial team usually wants the directory. Handing the second to the first is a technically correct project that satisfies nobody, and the conversation is much cheaper before the pipeline exists.
How do you see access change over time?
By accumulating snapshots, because the directory only ever describes now. An organisation membership table tells you who holds which role today and nothing about who held it in March, which is unfortunate given that the useful access question is almost always about change rather than state.
Appending rather than overwriting fixes that cheaply. The directory is small, so a daily copy of users, organisations, members and member roles costs almost nothing, and because tables arrive partitioned on the extraction timestamp those copies become dated observations without anybody designing it.
Then build the views that make it answerable. Comparing consecutive snapshots shows who gained a role, who lost one and which accounts appeared or vanished, which is what an access review actually needs and what no single snapshot can provide. Join the three membership streams once in a view rather than in every query, and filter on the partitioning column so the comparison stays cheap.
Frequently asked questions
Can I get login events?
Not through this connector. The streams cover users, clients, organisations and memberships, so sign-in activity needs a different route entirely.
How do I see who gained a role?
By comparing appended snapshots using the extraction timestamp partitioning. Overwriting discards exactly the evidence an access review asks for.
Which credentials should I use?
A machine to machine application with read scopes, so the connector obtains tokens itself. A manually copied token suits a test rather than a scheduled pipeline.
Why are memberships spread across streams?
Organisations, their members and those members' roles arrive separately, mirroring the API. Join them once in a view rather than repeating it in every query.
Can I do this without writing code?
The pipeline, yes. The view joining memberships and the comparison across snapshots are SQL, and they are what turns a directory copy into an access review.
Get your Auth0 data into BigQuery
Establish first that this is the directory rather than the activity, because that is the assumption people arrive with and the one that ends projects badly. Use a machine to machine application with read scopes. Then append rather than overwrite, join the three membership streams once in a view, and compare consecutive snapshots to answer the questions an access review actually asks.
Airbyte's connector catalog includes 600+ pre-built connectors, so identity data can sit beside the commercial reality. For the same source into a warehouse with fine-grained controls, see Auth0 to Snowflake, and for the same source into a lakehouse, Auth0 to Databricks.
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