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Log in to your Strava account and navigate to the settings page. From there, click on “My Account”� and then select “Download Your Data.”� Strava will prepare your data and send you an email with a link to download a ZIP file containing all your activity data in various formats, including GPX, TCX, or CSV files.
Once you have the ZIP file, extract its contents to a local directory on your computer. Organize the data files in a way that makes them easily accessible for transformation and loading processes. Ensure you have all necessary files, especially those in CSV format for easier handling.
Review the extracted files to understand the structure and content. Open the CSV files using a tool like Excel or a text editor and assess the columns and data types. Prepare the data by cleaning and transforming it if necessary, ensuring it matches the schema requirements of Starburst Galaxy. You may need to script this transformation using Python or another programming language to automate column name adjustments or data type conversions.
Access your Starburst Galaxy account and navigate to the workspace where you intend to load the data. Ensure you have the necessary permissions to create tables and insert data. Familiarize yourself with the platform"s data loading requirements and SQL syntax.
Using Starburst Galaxy"s SQL editor, create the tables that will store your Strava data. Define the schema based on the transformed data, specifying appropriate data types for each column. For example, use `CREATE TABLE` statements to set up tables with columns that match your CSV file structure.
Use SQL `COPY` commands or `INSERT` statements to load the transformed CSV data into the tables you created in Starburst Galaxy. This may involve uploading the CSV files to a location accessible by Starburst Galaxy (e.g., an S3 bucket) if direct file uploads are not supported. Ensure the data is correctly mapped to the table columns during this process.
After loading the data, run queries to verify that the data has been accurately transferred and corresponds to the original data from Strava. Check for any discrepancies or errors in the data types, values, or completeness. Rectify any issues by re-transforming and reloading the data as necessary. This ensures the integrity and usability of your data within Starburst Galaxy.
By following these steps, you can effectively move data from Strava to Starburst Galaxy without using third-party connectors or integrations, while maintaining control over the data transformation and loading process.
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.
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