Summarize this article with:
Trusted by data-driven companies
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.
- Inconsistent and inaccurate data
- Laborious and expensive
- Brittle and inflexible
- Reliable and accurate
- Extensible and scalable for all your needs
- Deployed and governed your way
Start syncing with Airbyte in 3 easy steps within 10 minutes
Take a virtual tour
Demo video of Airbyte Cloud
Demo video of AI Connector Builder
Setup Complexities simplified!
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.
What sets Airbyte Apart
Modern GenAI Workflows
Move Large Volumes, Fast
An Extensible Open-Source Standard
Full Control & Security
Fully Featured & Integrated
Enterprise Support with SLAs
What our users say
Andre Exner
"For TUI Musement, Airbyte cut development time in half and enabled dynamic customer experiences."
Chase Zieman
“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.”
Rupak Patel
"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."
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.
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`.
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.
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.
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.
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`.
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.
FAQs
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
Strava is a late-stage venture company and fitness-focused social app for tracking exercise. It is one of the most popular fitness apps for those with a competitive edge. Strava is an online network where runners and cyclists can record their activities, compare performance, and compete with their community. Strava is a worldwide community of millions of runners, cyclists and triathletes, united by the fellowship of sport. Strava is a free digital service available through both mobile applications and the web.
Strava's API provides access to a wide range of data related to user activities on the platform. The following are the categories of data that can be accessed through Strava's API:
1. Athlete data: This includes information about the user's profile, such as name, age, gender, weight, and location.
2. Activity data: This includes information about the user's activities, such as distance, duration, speed, elevation, and heart rate.
3. Segment data: This includes information about the user's performance on specific segments, such as the segment name, distance, elevation, and leaderboard rankings.
4. Route data: This includes information about the user's created routes, such as the route name, distance, elevation, and map coordinates.
5. Club data: This includes information about the user's clubs, such as the club name, description, and member list.
6. Gear data: This includes information about the user's gear, such as the gear name, type, and usage statistics.
7. Authorization data: This includes information about the user's authorization status, such as access tokens and refresh tokens.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
