Strava connector

Set up the Strava connector in Kaivo: authentication, configuration, the 2 BigQuery tables it syncs, and answers to common questions.

Written By Lauri Raivio

Last updated About 4 hours ago

Kaivo is a fully managed data platform that syncs your Strava data into a Google BigQuery warehouse and keeps it up to date automatically. There is no pipeline to build and no infrastructure to run, so you can spend your time analysing your data from Strava instead of moving it.

What is the Strava connector

Sync your Strava activity data into BigQuery with Kaivo to analyse training volume, performance, and trends across rides, runs, and swims.

CategoryOther
StatusGenerally available
AuthenticationAPI key
SetupSelf-service

Getting started with the Strava connector

  1. Sign up for Kaivo and create a workspace.
  2. Connect your Strava account.
  3. Choose which tables to sync.
  4. Wait for the initial sync to finish.
  5. Query your data in BigQuery or your favourite AI or BI tool.

Authenticating Strava

Authenticate with your Strava credentials. You provide:

FieldDescription
Client Secret

The Client Secret of your Strava API application.

Refresh Token

The Refresh Token with activity:read_all permissions.

Prerequisites

Strava only supports OAuth, so you need your own Strava API application:

  1. In the Strava developer portal, create an API application and note your Client ID and Client Secret.
  2. Authorize the app to obtain a Refresh Token with read_all and activity:read_all scopes.
  3. Find your Athlete ID in your Strava profile URL.

Then enter those values below.

Need help, or want an easier way to connect? Reach our team via the chat in the bottom-right corner.

Configuring the Strava connector

When you set up the connector, you provide:

FieldDescription
Client ID

The Client ID of your Strava API application.

Athlete ID

Your Strava athlete ID.

Start Date

Any data before this date will not be fetched.

Tables and columns synced from Strava

Kaivo syncs 2 tables from Strava into a dedicated dataset in your BigQuery warehouse. Click any table to see its columns and types.

How the Strava sync works

After the first load, Kaivo keeps your BigQuery warehouse up to date for you. Where Strava supports it, each sync pulls only new and changed records so it stays fast; otherwise it refreshes the whole table. Every record keeps its original ID, so you won't get duplicate rows.

Frequently asked questions

How long does the initial sync take for Strava?

It depends on how much history is in your Strava account. Most initial syncs finish within minutes, while large accounts can take a few hours. After that, syncs only fetch new and changed records, so they're much faster.

Can I sync only some tables or columns?

Yes. You pick which tables to sync when you set up the connection and can change the selection later. Tables you don't select are never copied to your warehouse.

What happens when Strava's schema changes?

New fields are never added automatically. You choose which fields to sync, so data you haven't selected (sensitive personal data, for example) never lands in your warehouse. When a new field appears, it becomes available for you to add. What happens to removed or renamed fields depends on a table's sync mode: full-refresh tables always match what's currently in Strava, so dropped fields disappear, while incremental tables keep their existing columns and history, so an old field stays and newly added fields fill in over time.

How do I handle GDPR or data deletion requests?

Your data lives in your own Kaivo-managed BigQuery warehouse, so the most direct option is to delete or anonymise specific records right in BigQuery. If you delete data in Strava instead, full-refresh tables drop it on the next sync, while incremental tables keep it, so you would remove the row in BigQuery or ask us to run a full refresh. To remove everything, delete the Strava connector in Kaivo and all of its synced data is deleted with it.

Common use cases for Strava data

Training volume

Use activities to track distance, moving time, and elevation by activity type and week.

Follow average_speed, average_watts, and heart rate across activities to see how fitness changes over time.

Activity mix

Use athlete_stats to compare ride, run, and swim totals at a glance.

Combine with other data

Join activity data with your other sources for custom reporting.

Use Strava data in your AI and BI tools

Once Strava data lands in your Kaivo-managed BigQuery warehouse, you can explore it with AI tools or any BI tool that connects to BigQuery. Here's how the most common destinations work with Strava data.

Claude

Use Kaivo's MCP server to give Claude secure, workspace-scoped access to your data. Setup guide →

Power BI

Microsoft's BI tool with a native BigQuery connector. Supports direct query and scheduled refresh. Setup guide →

Data Studio

Free Google BI tool with native BigQuery support. One-click connection to your Kaivo warehouse; great for SMB teams on Google Workspace. Setup guide →

Tableau

The premium analytics standard, with native BigQuery integration. Setup guide →

Google Sheets

Use Connected Sheets to query BigQuery directly from a spreadsheet, with no SQL. Setup guide →

Excel

Connect via Power Query's BigQuery connector. Setup guide →

Metabase

Open-source BI tool with strong BigQuery support. Setup guide →

See our pricing page for Strava connector pricing and plan details.