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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.
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.
CSV (Comma Separated Values) file is a tool used to store and exchange data in a simple and structured format. It is a plain text file that contains data separated by commas, where each line represents a record and each field is separated by a comma. CSV files are widely used in data analysis, data migration, and data exchange between different software applications. The CSV file format is easy to read and write, making it a popular choice for storing and exchanging data. It can be opened and edited using any text editor or spreadsheet software, such as Microsoft Excel or Google Sheets. CSV files can also be imported and exported from databases, making it a convenient tool for data management. CSV files are commonly used for storing large amounts of data, such as customer information, product catalogs, financial data, and scientific data. They are also used for data analysis and visualization, as they can be easily imported into statistical software and other data analysis tools. Overall, the CSV file is a simple and versatile tool that is widely used for storing, exchanging, and analyzing data.
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.
1. Open the Airbyte platform and navigate to the "Destinations" tab on the left-hand side of the screen.
2. Click on the "CSV File" destination connector.
3. Click on the "Create new connection" button.
4. Enter a name for your connection and select the workspace you want to use.
5. Enter the path where you want to save your CSV file.
6. Choose the delimiter you want to use for your CSV file.
7. Select the encoding you want to use for your CSV file.
8. Choose whether you want to append data to an existing file or create a new file each time the connector runs.
9. Enter any additional configuration settings you want to use for your CSV file.
10. Click on the "Test" button to ensure that your connection is working properly.
11. If the test is successful, click on the "Create" button to save your connection.
12. Your CSV File destination connector is now connected and ready to use.
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!
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
Ready to get started?
Frequently Asked Questions
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 should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey: