Appfollow is a platform for app review management, ASO, and customer support. It helps businesses track, analyze, and respond to user feedback, boosting app visibility and user satisfaction.
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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
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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.
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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.
Appfollow is a one-stop platform for app analytics, app reviews management, and app store optimization. Get reviews from the App Store, Google Play to monitor and analyse them. AppFollow is on a mission to help teams working on mobile apps to turn insights from reviews into new product experiences that users love. Mobile teams are responding to feedback in a timely manner, building products they know users will love, and optimizing their performance in the app stores with AppFollow.
Appfollow's API provides access to a wide range of data related to mobile apps and their performance. The following are the categories of data that can be accessed through Appfollow's API:
1. App Store Optimization (ASO) data: This includes data related to app store rankings, keyword rankings, and user reviews.
2. Competitor analysis data: This includes data related to competitor app rankings, keyword rankings, and user reviews.
3. User acquisition data: This includes data related to app installs, uninstall rates, and user retention rates.
4. App performance data: This includes data related to app crashes, bugs, and other performance issues.
5. Social media data: This includes data related to social media mentions and sentiment analysis.
6. Analytics data: This includes data related to app usage, user engagement, and user behavior.
7. Advertising data: This includes data related to app advertising campaigns, ad performance, and ad spend.
Overall, Appfollow's API provides a comprehensive set of data that can help app developers and marketers make informed decisions about their app's performance and user engagement.
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.
Appfollow is a one-stop platform for app analytics, app reviews management, and app store optimization. Get reviews from the App Store, Google Play to monitor and analyse them. AppFollow is on a mission to help teams working on mobile apps to turn insights from reviews into new product experiences that users love. Mobile teams are responding to feedback in a timely manner, building products they know users will love, and optimizing their performance in the app stores with AppFollow.
Appfollow's API provides access to a wide range of data related to mobile apps and their performance. The following are the categories of data that can be accessed through Appfollow's API:
1. App Store Optimization (ASO) data: This includes data related to app store rankings, keyword rankings, and user reviews.
2. Competitor analysis data: This includes data related to competitor app rankings, keyword rankings, and user reviews.
3. User acquisition data: This includes data related to app installs, uninstall rates, and user retention rates.
4. App performance data: This includes data related to app crashes, bugs, and other performance issues.
5. Social media data: This includes data related to social media mentions and sentiment analysis.
6. Analytics data: This includes data related to app usage, user engagement, and user behavior.
7. Advertising data: This includes data related to app advertising campaigns, ad performance, and ad spend.
Overall, Appfollow's API provides a comprehensive set of data that can help app developers and marketers make informed decisions about their app's performance and user engagement.
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.
Appfollow is a one-stop platform for app analytics, app reviews management, and app store optimization. Get reviews from the App Store, Google Play to monitor and analyse them. AppFollow is on a mission to help teams working on mobile apps to turn insights from reviews into new product experiences that users love. Mobile teams are responding to feedback in a timely manner, building products they know users will love, and optimizing their performance in the app stores with AppFollow.
Appfollow's API provides access to a wide range of data related to mobile apps and their performance. The following are the categories of data that can be accessed through Appfollow's API:
1. App Store Optimization (ASO) data: This includes data related to app store rankings, keyword rankings, and user reviews.
2. Competitor analysis data: This includes data related to competitor app rankings, keyword rankings, and user reviews.
3. User acquisition data: This includes data related to app installs, uninstall rates, and user retention rates.
4. App performance data: This includes data related to app crashes, bugs, and other performance issues.
5. Social media data: This includes data related to social media mentions and sentiment analysis.
6. Analytics data: This includes data related to app usage, user engagement, and user behavior.
7. Advertising data: This includes data related to app advertising campaigns, ad performance, and ad spend.
Overall, Appfollow's API provides a comprehensive set of data that can help app developers and marketers make informed decisions about their app's performance and user engagement.
1. Open the Appfollow website and log in to your account.
2. Navigate to the API section of your account settings.
3. Generate an API key by clicking on the "Generate new API key" button.
4. Copy the API key to your clipboard.
5. Open the Airbyte website and navigate to the "Sources" tab.
6. Click on the "Create new source" button.
7. Select "Appfollow" from the list of available connectors.
8. Enter a name for your source and click on the "Next" button.
9. Paste the API key you copied earlier into the "API Key" field.
10. Enter the name of the Appfollow account you want to connect to.
11. Click on the "Test" button to verify that the connection is working.
12. If the test is successful, click on the "Create" button to save your source.
13. You can now use your Appfollow source connector to extract data from your Appfollow account and integrate it with other tools and services.
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.