Okta Data Integration with BigQuery

With Portable, integrate Okta data with your BigQuery warehouse in minutes. Access your identity management service data from BigQuery without having to manage cumbersome ETL scripts.

The Two Paths to Connect Okta to Google BigQuery

There are two ways to sync data from Okta into your data warehouse for analytics.

Method 1: Manually Developing a Custom Data Pipeline Yourself

Write code from scratch or use an open-source framework to build an integration between Okta and BigQuery.

Method 2: Automating the ETL Process with a No-Code Solution

Leverage a pre-built connector from a cloud-hosted solution like Portable.

How to Create Value with Okta Data

Teams connect Okta to their data warehouse to build dashboards and generate value for their business. Let’s dig into the capabilities Okta exposes via their API, outline insights you can build with the data, and summarize the most common analytics environments that teams are using to process their Okta data.

Extract: What Data Can You Extract from the Okta API?

Okta is a identity management service used for managing employee's authentication to any application or device.

To help clients power downstream analytics, Okta offers an application programming interface (API) for clients to extract data on business entities. Here are a few example entities you can extract from the API:

  • Brands
  • Devices
  • Domains
  • Dynamic Client Registration
  • Event Types
  • Factors
  • Features
  • Groups
  • Identity Sources
  • Key Management
  • Linked Objects
  • Log Streaming
  • Mappings
  • MyAccount
  • Org
  • Policy
  • Push Providers
  • Risk Events
  • Risk Providers
  • Schemas
  • Sessions
  • Subscriptions
  • System Log
  • Templates
  • ThreatInsight
  • Trusted Origins
  • UI Schema
  • User Types
  • Users
  • ASA Attributes
  • ASA Audits
  • ASA Clients
  • ASA Entitlements
  • ASA Groups
  • ASA Projects
  • ASA Service Users
  • ASA Teams
  • ASA Users

You can visit the Okta API Documentation to explore the entire catalog of available API resources and the complete schema definition for each.

As you think about the data you will need for analytics, don’t forget that Portable offers no-code integrations to other similar applications.

Regardless of the SaaS solution you use, it’s important to find a identity management service with robust data available for analytics.

Load: Which Destinations Are Best for Your Okta ETL Pipeline?

To turn raw data from Okta into dashboards, most companies centralize information into a data warehouse or data lake. For Portable clients, the most common ETL pipelines are:

  1. Okta to Snowflake Integration
  2. Okta to Google BigQuery Integration
  3. Okta to Amazon Redshift Integration
  4. Okta to PostgreSQL Integration
Common Data Warehouses
Common Data Warehouses

Once you have a destination to load the data, it’s common to combine Okta data with information from other enterprise applications like Jira, Mailchimp, HubSpot, Zendesk, and Klaviyo.

From there, you can build cross-functional dashboards in a visualization tool like Power BI, Tableau, Looker, or Retool.

Develop: Which Dashboards Should You Build with Okta Data?

Now that you have identified the data you want to extract, the next step is to plan out the dashboards you can build with the data.

As a process, you want to consume raw data, overlay SQL logic, and build a dashboard to either 1) increase revenue or 2) decrease costs.

Replicating Okta data into your cloud data warehouse can unlock a wide array of opportunities to power analytics, automate workflows, and develop products. The use cases are endless.

Now that we have a clear sense of the insights we can create, let’s compare the process of developing a custom Okta integration with the benefits of using a no-code ETL solution like Portable.

Method 1: Building a Custom Okta ETL Pipeline

To build your own Okta integration, there are three steps:

  1. Navigate the Okta API documentation
  2. Make your first API request
  3. Turn an API request into a complete data pipeline

Let’s walk through the process in more detail.

How to Interpret Okta’s API Documentation

When reading API documentation, there are a handful of key concepts to consider.


There are many common authentication mechanisms. OAuth 2.0 (Auth Code and Client Credentials), API Keys, JWT Tokens, Personal Access Tokens, Basic Authentication, etc. For Okta, it’s important to identify the authentication mechanism and how best to incorporate the necessary credentials into your API requests.

The Okta Authentication API provides operations to authenticate users, perform multifactor enrollment and verification, recover forgotten passwords, and unlock accounts. It can be used as a standalone API to provide the identity layer on top of your existing application, or it can be integrated with the Okta Sessions API to obtain an Okta session cookie and access apps within Okta.

The API is targeted for developers who want to build their own end-to-end login experience to replace the built-in Okta login experience and addresses the following key scenarios:

Primary authentication allows you to verify username and password credentials for a user.

Multifactor authentication (MFA) strengthens the security of password-based authentication by requiring additional verification of another Factor such as a temporary one-time password or an SMS passcode. The Authentication API supports user enrollment with MFA factors enabled by the administrator, as well as MFA challenges based on your global session policy. Recovery allows users to securely reset their password if they've forgotten it, or unlock their account if it has been locked out due to excessive failed login attempts. This functionality is subject to the security policy set by the administrator.


It’s important to identify the Okta API endpoints you want to use for analytics. Most APIs offer a combination of GET, POST, PUT, and DELETE request methods; however, for analytics, GET requests are typically the most useful. At times, POST requests can be used to extract data as well.

For Okta, the teams endpoint is a great place to get started.

Request Parameters

For each API endpoint you would like to use for analytics, you need to understand the method (GET, POST, PUT, or DELETE) and the URL, but there are other considerations to take into account as well. You should look out for pagination mechanics, query parameters, and parameters that are added to the request path.

Requests that return a list of objects may support pagination. Pagination is based on a cursor and not on page number. The cursor is opaque to the client and specified in either the before or after query parameter. For some objects, you can also set a custom page size with the limit parameter.

Some API endpoints require unique identifiers from a previous API response to be included in the URL path. For instance, to list audits for a team, you need a team_name that is returned from another endpoint.

How Do You Call the Okta API? (Tutorial)

  1. Follow the instructions above to read the Okta API documentation
  2. Identify and collect your credentials for authentication
  3. Pick the API resource you want to pull data from
  4. Configure the necessary parameters, method, and URL to make your first request (e.g. with curl or Postman)
  5. Add your credentials and make your first API call . Here is an example request using curl (without real credentials):
curl -i -X GET       
-H 'Authorization: Bearer <YOUR_JWT_HERE>'

How Do You Maintain a Custom Okta to BigQuery ETL Pipeline?

Making a call to the Okta API is just the beginning of maintaining a complete custom ETL pipeline.

Here is a getting-started guide to building a production-grade pipeline for Okta:

  • For each API endpoint, define schemas (which fields exist and the type for each)
  • Process the API response and parse the data (typically parsing JSON or XML)
  • Handle and replicate nested objects and custom fields
  • Identify which Okta fields are primary keys and which keys are required vs. optional
  • Version control your changes in a git-based workflow (using GitHub, GitLab, etc.)
  • Handle code dependencies in your toolchain and the upgrades that come with each
  • Monitor the health of the upstream API, and —when things go wrong— troubleshoot via the status page, reach out to support, and open tickets
  • Handle error codes (HTTP error codes like 400s, 500s, etc.)
  • Manage and respect rate limits imposed by the server

We won’t go into detail on all of the items above, but rate limits are a great example of the complexity found in a production-grade data pipeline.

To protect the service for all customers, Okta APIs are subject to rate limiting. These limits mitigate denial-of-service attacks and abusive actions such as rapidly updating configurations, aggressive polling and concurrency, or excessive API calls.

The Okta API rate limits are divided into three categories: authentication/end user, management, and other endpoints. Each category has APIs with rate limits that are enforced individually as well as a cumulative rate limit. The rate limits vary by service subscription

If you don’t respect rate limits, and if you can’t handle server responses (like 429 errors with a Retry-After header), your pipeline can break, and analytics can become out-of-date.

What Are the Drawbacks of Building the Okta ETL Pipeline Yourself?

You can probably tell at this point that there is a lot of work that goes into building and maintaining an ETL pipeline from Okta to your data warehouse.

If you want less development work, faster insights, and no ongoing responsibilities, you should consider a cloud-hosted ETL solution.

Let’s walk through the setup process for a no-code ETL solution and its benefits.

Method 2: Using a No-Code Okta ETL Solution

No-code ETL solutions are simple. Vendors specialize in building and maintaining data pipelines on your behalf. Instead of starting from scratch for each integration. Companies like Portable create connector templates that can be leveraged by hundreds or thousands of clients.

Step-By-Step Tutorial for Configuring Your Okta ETL Pipeline

Off-the-shelf ETL tools offer a no-code setup process. Here are the instructions to connect Okta to your cloud data warehouse with Portable.

  1. Create an account (no credit card required)
  2. Add a source —search for and select Okta
  3. Authenticate with Okta using the instructions in the Portable console
  4. Select BigQuery and authenticate
  5. Set up a flow connecting Okta to your analytics environment
  6. Run your flow to replicate data from Okta to your warehouse
  7. Use the dropdown to set your data flow to run on a cadence

What Are the Benefits of Using Portable for Okta ETL?

No-Code Simplicity

Start moving Okta data in minutes. Save yourself the headaches of reading API documentation, writing code, and worrying about maintenance. Leave the hassle to us.

Easy to Understand Pricing

With predictable, fixed-cost pricing per data flow, you know exactly how much your Okta integration will cost every month.

Fast Development Speeds

Access lightning-fast connector development. Portable can build new integrations on-demand in hours or days.

Hands-On Support

APIs change. Schemas evolve. Okta will have maintenance issues and errors. With Portable, we will do everything in our power to make your life easier.

Unlimited Data Volumes

You can move as much data from Okta to Google BigQuery as you want without worrying about usage credits or overages. Instead of analyzing your ETL costs, you should be analyzing your data.

Free to Get Started

Sign up and get started for free. You don’t need a credit card to manually trigger a data sync, so you can try all of our connectors before paying a dime.

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