Orbit is a community growth platform that helps you manage, measure, and engage your community. It centralizes data, tracks member activity, and provides insights to foster engagement.
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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.
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.
Set up a source connector to extract data from in Airbyte
Choose from one of 400 sources where you want to import data from. This can be any API tool, cloud data warehouse, database, data lake, files, among other source types. You can even build your own source connector in minutes with our no-code no-code connector builder.
Configure the connection in Airbyte
The Airbyte Open Data Movement Platform
The only open solution empowering data teams to meet growing business demands in the new AI era.
Leverage the largest catalog of connectors
Cover your custom needs with our extensibility
Free your time from maintaining connectors, with automation
- Automated schema change handling, data normalization and more
- Automated data transformation orchestration with our dbt integration
- Automated workflow with our Airflow, Dagster and Prefect integration
Reliability at every level
Ship more quickly with the only solution that fits ALL your needs.
As your tools and edge cases grow, you deserve an extensible and open ELT solution that eliminates the time you spend on building and maintaining data pipelines
Leverage the largest catalog of connectors
Cover your custom needs with our extensibility
Free your time from maintaining connectors, with automation
- Automated schema change handling, data normalization and more
- Automated data transformation orchestration with our dbt integration
- Automated workflow with our Airflow, Dagster and Prefect integration
Reliability at every level
Ship more quickly with the only solution that fits ALL your needs.
As your tools and edge cases grow, you deserve an extensible and open ELT solution that eliminates the time you spend on building and maintaining data pipelines
Leverage the largest catalog of connectors
Cover your custom needs with our extensibility
Free your time from maintaining connectors, with automation
- Automated schema change handling, data normalization and more
- Automated data transformation orchestration with our dbt integration
- Automated workflow with our Airflow, Dagster and Prefect integration
Reliability at every level
Move large volumes, fast.
Change Data Capture.
Security from source to destination.
We support the CDC methods your company needs
Log-based CDC
Timestamp-based CDC
Airbyte Open Source
Airbyte Cloud
Airbyte Enterprise
Why choose Airbyte as the backbone of your data infrastructure?
Keep your data engineering costs in check
Get Airbyte hosted where you need it to be
- Airbyte Cloud: Have it hosted by us, with all the security you need (SOC2, ISO, GDPR, HIPAA Conduit).
- Airbyte Enterprise: Have it hosted within your own infrastructure, so your data and secrets never leave it.
White-glove enterprise-level support
Including for your Airbyte Open Source instance with our premium support.
Airbyte supports a growing list of destinations, including cloud data warehouses, lakes, and databases.
Airbyte supports a growing list of destinations, including cloud data warehouses, lakes, and databases.
Airbyte supports a growing list of sources, including API tools, cloud data warehouses, lakes, databases, and files, or even custom sources you can build.
Fnatic, based out of London, is the world's leading esports organization, with a winning legacy of 16 years and counting in over 28 different titles, generating over 13m USD in prize money. Fnatic has an engaged follower base of 14m across their social media platforms and hundreds of millions of people watch their teams compete in League of Legends, CS:GO, Dota 2, Rainbow Six Siege, and many more titles every year.
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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.
Orbit is the leading community growth platform. Orbit is made by community builders, who understand the power of community. They want to help you deliver a stellar member experience, quantify your business impact, and become community-driven.
Orbit.love's API provides access to a variety of data related to social media and influencer marketing. The following are the categories of data that can be accessed through the API:
1. Social media data: This includes data related to social media platforms such as Instagram, Twitter, and YouTube. It includes information such as follower count, engagement rate, and post frequency.
2. Influencer data: This includes data related to influencers such as their name, handle, and bio. It also includes information about their audience demographics and interests.
3. Campaign data: This includes data related to influencer marketing campaigns such as campaign goals, budget, and performance metrics.
4. Brand data: This includes data related to brands such as their name, industry, and target audience. It also includes information about their marketing goals and strategies.
5. Performance data: This includes data related to the performance of influencer marketing campaigns such as engagement rate, reach, and conversion rate.
Overall, Orbit.love's API provides a comprehensive set of data that can be used to analyze and optimize influencer marketing campaigns.
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 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.
Orbit is the leading community growth platform. Orbit is made by community builders, who understand the power of community. They want to help you deliver a stellar member experience, quantify your business impact, and become community-driven.
Orbit.love's API provides access to a variety of data related to social media and influencer marketing. The following are the categories of data that can be accessed through the API:
1. Social media data: This includes data related to social media platforms such as Instagram, Twitter, and YouTube. It includes information such as follower count, engagement rate, and post frequency.
2. Influencer data: This includes data related to influencers such as their name, handle, and bio. It also includes information about their audience demographics and interests.
3. Campaign data: This includes data related to influencer marketing campaigns such as campaign goals, budget, and performance metrics.
4. Brand data: This includes data related to brands such as their name, industry, and target audience. It also includes information about their marketing goals and strategies.
5. Performance data: This includes data related to the performance of influencer marketing campaigns such as engagement rate, reach, and conversion rate.
Overall, Orbit.love's API provides a comprehensive set of data that can be used to analyze and optimize influencer marketing campaigns.
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 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.
Orbit is the leading community growth platform. Orbit is made by community builders, who understand the power of community. They want to help you deliver a stellar member experience, quantify your business impact, and become community-driven.
Orbit.love's API provides access to a variety of data related to social media and influencer marketing. The following are the categories of data that can be accessed through the API:
1. Social media data: This includes data related to social media platforms such as Instagram, Twitter, and YouTube. It includes information such as follower count, engagement rate, and post frequency.
2. Influencer data: This includes data related to influencers such as their name, handle, and bio. It also includes information about their audience demographics and interests.
3. Campaign data: This includes data related to influencer marketing campaigns such as campaign goals, budget, and performance metrics.
4. Brand data: This includes data related to brands such as their name, industry, and target audience. It also includes information about their marketing goals and strategies.
5. Performance data: This includes data related to the performance of influencer marketing campaigns such as engagement rate, reach, and conversion rate.
Overall, Orbit.love's API provides a comprehensive set of data that can be used to analyze and optimize influencer marketing campaigns.
1. First, navigate to the Orbit.love source connector page on Airbyte.com.
2. Click on the "Create new connection" button.
3. In the "Configure your Orbit.love source" section, enter a name for your connection.
4. In the "Connection Configuration" section, enter your Orbit.love API key.
5. In the "Advanced" section, you can configure additional settings such as the number of days to sync data for.
6. Click on the "Check connection" button to ensure that your credentials are correct and the connection is successful.
7. Once the connection is successful, click on the "Create connection" button to save your configuration.
8. You can now use this connection to sync data from your Orbit.love account to your destination of choice.
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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