How to load data from Strava to Kafka

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

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Set up a Strava connector in Airbyte

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

Set up Kafka for your extracted Strava data

Select Kafka where you want to import data from your Strava 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 Kafka 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.

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TL;DR

set up Strava as a source connector (using Auth, or usually an API key)
set up Kafka as a destination connector
define which data you want to transfer and how frequently

You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

This tutorial’s purpose is to show you how.

What is Strava

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.

What is Kafka

A communication solutions agency, Kafka is a cloud-based / on-prem distributed system offering social media services, public relations, and events. For event streaming, three main functionalities are available: the ability to (1) subscribe to (read) and publish (write) streams of events, (2) store streams of events indefinitely, durably, and reliably, and (3) process streams of events in either real-time or retrospectively. Kafka offers these capabilities in a secure, highly scalable, and elastic manner.

Integrate Strava with Kafka in minutes

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Prerequisites

A Strava account to transfer your customer data automatically from.
A Kafka account.
An active Airbyte Cloud account, or you can also choose to use Airbyte Open Source locally. You can follow the instructions to set up Airbyte on your system using docker-compose.

Airbyte is an open-source data integration platform that consolidates and streamlines the process of extracting and loading data from multiple data sources to data warehouses. It offers pre-built connectors, including Strava and Kafka, for seamless data migration.

When using Airbyte to move data from Strava to Kafka, it extracts data from Strava using the source connector, converts it into a format Kafka can ingest using the provided schema, and then loads it into Kafka via the destination connector. This allows businesses to leverage their Strava data for advanced analytics and insights within Kafka, simplifying the ETL process and saving significant time and resources.

Step 1: Set up Strava as a source connector

1. Go to the Airbyte website and navigate to the "Sources" tab.
2. Find the Strava source connector and click on it.
3. Click on the "Create new connection" button.
4. Enter a name for your connection and click on "Next".
5. Enter your Strava credentials (email and password) and click on "Authorize".
6. Select the data you want to sync from Strava (activities, athletes, etc.) and click on "Next".
7. Choose the frequency of the sync (hourly, daily, weekly, etc.) and click on "Next".
8. Review your connection settings and click on "Create connection".
9. Wait for the sync to complete and check the status of your connection on the Airbyte dashboard.

Note: It is important to ensure that your Strava account has the necessary permissions to allow Airbyte to access your data. You may need to adjust your Strava privacy settings to enable this. Additionally, it is recommended to review the Airbyte documentation for more detailed instructions and troubleshooting tips.

Step 2: Set up Kafka as a destination connector

1. First, you need to have an Apache Kafka destination connector installed on your system. If you don't have it, you can download it from the Apache Kafka website.
2. Once you have the Apache Kafka destination connector installed, you need to create a new connection in Airbyte. To do this, go to the Connections tab and click on the "New Connection" button. 3. In the "New Connection" window, select "Apache Kafka" as the destination connector and enter the required connection details, such as the Kafka broker URL, topic name, and authentication credentials.
4. After entering the connection details, click on the "Test Connection" button to ensure that the connection is working properly.
5. If the connection test is successful, click on the "Save" button to save the connection.
6. Once the connection is saved, you can create a new pipeline in Airbyte and select the Apache Kafka destination connector as the destination for your data.
7. In the pipeline configuration, select the connection you created in step 3 as the destination connection.
8. Configure the pipeline to map the source data to the appropriate Kafka topic and fields.
9. Once the pipeline is configured, you can run it to start sending data to your Apache Kafka destination.

Step 3: Set up a connection to sync your Strava data to Kafka

Once you've successfully connected Strava as a data source and Kafka as a destination in Airbyte, you can set up a data pipeline between them with the following steps:

Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
Choose your source: Select Strava from the dropdown list of your configured sources.
Select your destination: Choose Kafka from the dropdown list of your configured destinations.
Configure your sync: Define the frequency of your data syncs based on your business needs. Airbyte allows both manual and automatic scheduling for your data refreshes.
Select the data to sync: Choose the specific Strava objects you want to import data from towards Kafka. You can sync all data or select specific tables and fields.
Select the sync mode for your streams: Choose between full refreshes or incremental syncs (with deduplication if you want), and this for all streams or at the stream level. Incremental is only available for streams that have a primary cursor.
Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from Strava to Kafka according to your settings.

Remember, Airbyte keeps your data in sync at the frequency you determine, ensuring your Kafka data warehouse is always up-to-date with your Strava data.

Use Cases to transfer your Strava data to Kafka

Integrating data from Strava to Kafka provides several benefits. Here are a few use cases:

Advanced Analytics: Kafka’s powerful data processing capabilities enable you to perform complex queries and data analysis on your Strava data, extracting insights that wouldn't be possible within Strava alone.
Data Consolidation: If you're using multiple other sources along with Strava, syncing to Kafka allows you to centralize your data for a holistic view of your operations, and to set up a change data capture process so you never have any discrepancies in your data again.
Historical Data Analysis: Strava has limits on historical data. Syncing data to Kafka allows for long-term data retention and analysis of historical trends over time.
Data Security and Compliance: Kafka provides robust data security features. Syncing Strava data to Kafka ensures your data is secured and allows for advanced data governance and compliance management.
Scalability: Kafka can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding Strava data.
Data Science and Machine Learning: By having Strava data in Kafka, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
Reporting and Visualization: While Strava provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to Kafka, providing more advanced business intelligence options. If you have a Strava table that needs to be converted to a Kafka table, Airbyte can do that automatically.

Wrapping Up

To summarize, this tutorial has shown you how to:

Configure a Strava account as an Airbyte data source connector.
Configure Kafka as a data destination connector.
Create an Airbyte data pipeline that will automatically be moving data directly from Strava to Kafka after you set a schedule

With Airbyte, creating data pipelines take minutes, and the data integration possibilities are endless. Airbyte supports the largest catalog of API tools, databases, and files, among other sources. Airbyte's connectors are open-source, so you can add any custom objects to the connector, or even build a new connector from scratch without any local dev environment or any data engineer within 10 minutes with the no-code connector builder.

We look forward to seeing you make use of it! We invite you to join the conversation on our community Slack Channel, or sign up for our newsletter. You should also check out other Airbyte tutorials, and Airbyte’s content hub!

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How to Sync Strava to Kafka Manually

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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.

What is Strava?

What data can you extract from Strava?

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.

How do I transfer data from Strava?

This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps:
1. Set up Strava to Kafka as a source connector (using Auth, or usually an API key)
2. Choose a destination (more than 50 available destination databases, data warehouses or lakes) to sync data too and set it up as a destination connector
3. Define which data you want to transfer from Strava to Kafka and how frequently
You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

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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How to load data from Strava to Kafka

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

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Sync data between Strava and Kafka

Get your data syncing in minutes

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TL;DR

set up Strava as a source connector (using Auth, or usually an API key)
set up Kafka as a destination connector
define which data you want to transfer and how frequently

You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

This tutorial’s purpose is to show you how.

What is Strava