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Begin by exporting your data from Strava. Log into your Strava account on the web, go to your account settings, and select the option to download your data archive. This will typically be a ZIP file containing various CSV or JSON files with your activity data.
Once you have downloaded your ZIP file, extract it to a local directory on your computer. Review the contents to understand the structure of the data. Organize the relevant files, such as activity data, into a format that aligns with your intended schema in Firebolt.
Open the extracted data files using a tool such as Excel or a text editor. Examine the data format and clean any inconsistencies or unnecessary data. Rename columns or adjust data types as necessary to match the schema you plan to implement in Firebolt.
To interact with Firebolt directly, download and install the Firebolt Command Line Interface (CLI) on your machine. Follow the Firebolt documentation for installation instructions, ensuring you have the necessary authentication credentials set up.
Access Firebolt using the CLI or web interface and create the appropriate tables to store your Strava data. Use SQL commands to define the schema, ensuring the data types and structures match those of your cleaned and prepared Strava data.
Use the Firebolt CLI to load your prepared data files into the newly created tables. You can use the `COPY` command in Firebolt to specify the file location and format, ensuring that data is correctly inserted into the respective tables.
After loading the data, run queries in Firebolt to verify that your data has been transferred correctly. Check for any discrepancies or errors in the data and make necessary adjustments by reloading or updating records to ensure data integrity.
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:
