How to load data from Strava to Clickhouse

Learn how to use Airbyte to synchronize your Strava data into Clickhouse within minutes.

Try Airbyte Cloud Manual method (the hard way)

Building your pipeline or Using Airbyte

Airbyte is the only open source solution empowering data teams to meet all their growing custom business demands in the new AI era.

Building in-house pipelines

Bespoke pipelines are:

Inconsistent and inaccurate data
Laborious and expensive
Brittle and inflexible

Furthermore, you will need to build and maintain Y x Z pipelines with Y sources and Z destinations to cover all your needs.

After Airbyte

Airbyte connections are:

Reliable and accurate
Extensible and scalable for all your needs
Deployed and governed your way

All your pipelines in minutes, however custom they are, thanks to Airbyte’s connector marketplace and AI Connector Builder.

Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Strava connector in Airbyte

Connect to or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up Clickhouse for your extracted Strava data

Select where you want to import data from your source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the Strava to Clickhouse in Airbyte

This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.

Try Airbyte Cloud Manual method (the hard way)

Take a virtual tour

Check out our interactive demo and our how-to videos to learn how you can sync data from any source to any destination.

Demo video of Airbyte Cloud

Demo video of AI Connector Builder

Talk to our team

Setup Complexities simplified!

You don’t need to put hours into figuring out how to use Airbyte to achieve your Data Engineering goals.

Simple & Easy to use Interface

Airbyte is built to get out of your way. Our clean, modern interface walks you through setup, so you can go from zero to sync in minutes—without deep technical expertise.

Guided Tour: Assisting you in building connections

Whether you’re setting up your first connection or managing complex syncs, Airbyte’s UI and documentation help you move with confidence. No guesswork. Just clarity.

Airbyte AI Assistant that will act as your sidekick in building your data pipelines in Minutes

Airbyte’s built-in assistant helps you choose sources, set destinations, and configure syncs quickly. It’s like having a data engineer on call—without the overhead.

Try Airbyte Cloud Manual method (the hard way)

What sets Airbyte Apart

Modern GenAI Workflows

Streamline AI workflows with Airbyte: load unstructured data into vector stores like Pinecone, Weaviate, and Milvus. Supports RAG transformations with LangChain chunking and embeddings from OpenAI, Cohere, etc., all in one operation.

Move Large Volumes, Fast

Quickly get up and running with a 5-minute setup that enables both incremental and full refreshes for databases of any size, seamlessly scaling to handle large data volumes. Our optimized architecture overcomes performance bottlenecks, ensuring efficient data synchronization even as your datasets grow from gigabytes to petabytes.

An Extensible Open-Source Standard

More than 1,000 developers contribute to Airbyte’s connectors, different interfaces (UI, API, Terraform Provider, Python Library), and integrations with the rest of the stack. Airbyte’s AI Connector Builder lets you edit or add new connectors in minutes.

Full Control & Security

Airbyte secures your data with cloud-hosted, self-hosted or hybrid deployment options. Single Sign-On (SSO) and Role-Based Access Control (RBAC) ensure only authorized users have access with the right permissions. Airbyte acts as a HIPAA conduit and supports compliance with CCPA, GDPR, and SOC2.

Fully Featured & Integrated

Airbyte automates schema evolution for seamless data flow, and utilizes efficient Change Data Capture (CDC) for real-time updates. Select only the columns you need, and leverage our dbt integration for powerful data transformations.

Enterprise Support with SLAs

Airbyte Self-Managed Enterprise comes with dedicated support and guaranteed service level agreements (SLAs), ensuring that your data movement infrastructure remains reliable and performant, and expert assistance is available when needed.

What our users say

Raman Singh

Tech Lead at Symend

Predictable, straightforward pricing model that simplified budgeting and significantly reduced overall spend

Learn more

Chase Zieman

Chief Data Officer

“Airbyte helped us accelerate our progress by years, compared to our competitors. We don’t need to worry about connectors and focus on creating value for our users instead of building infrastructure. That’s priceless. The time and energy saved allows us to disrupt and grow faster.”

Learn more

Rupak Patel

Operational Intelligence Manager

"With Airbyte, we could just push a few buttons, allow API access, and bring all the data into Google BigQuery. By blending all the different marketing data sources, we can gain valuable insights."

Learn more

How to Sync to Manually

Step 1: Register a Strava Developer Account

To access Strava's API, you first need to create a developer account on Strava. Once registered, create an application in the Strava developer portal. This will provide you with a `Client ID` and `Client Secret`, which are essential for authenticating API requests.

Step 2: Authenticate with Strava API

Use the `Client ID` and `Client Secret` to obtain an access token from Strava. Start by directing your browser to request user authorization (e.g., `https://www.strava.com/oauth/authorize...`). Once authorized, exchange the authorization code for an access token by making a POST request to `https://www.strava.com/api/v3/oauth/token`.

Step 3: Extract Data from Strava

With the access token, you can now make API requests to extract data. Use Python's `requests` library to send GET requests to endpoints like `https://www.strava.com/api/v3/athlete/activities` to retrieve activity data. Parse the JSON response to extract relevant fields such as activity name, type, distance, and time.

Step 4: Prepare Data for Clickhouse

Transform the JSON response into a format suitable for Clickhouse insertion. Convert the data into a CSV format or prepare a list of dictionaries with keys corresponding to Clickhouse table columns. Ensure data types are compatible with your Clickhouse schema.

Step 5: Set Up Clickhouse Environment

Ensure that Clickhouse is installed and running on your server. Use the Clickhouse client or Clickhouse HTTP interface for data insertion. Create a table schema in Clickhouse that matches the data format extracted from Strava.

Step 6: Insert Data into Clickhouse

Use Python's `pandas` library to convert your data into a DataFrame if necessary. Write the data to a CSV file or use the Clickhouse `INSERT` command directly. For CSV insertion, use the Clickhouse `clickhouse-client` with the `--query` option to insert data from a CSV file: `clickhouse-client --query="INSERT INTO your_table FORMAT CSV" < data.csv`.

Step 7: Automate the Data Transfer Process

To regularly update your Clickhouse database with new Strava data, automate the process using a cron job or a scheduled task. Write a script that performs the above steps and schedule it to run at desired intervals. Ensure proper error handling and logging to monitor the data transfer process.

By following these steps, you can successfully move data from Strava to Clickhouse without relying on third-party connectors or integrations, while maintaining control over the data flow and ensuring data accuracy.