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First, explore Appfollow’s documentation to understand the available methods for exporting data. Appfollow typically allows data export via APIs, which will be crucial for direct data extraction. Ensure you have access to the Appfollow API and obtain necessary credentials such as API keys.
If you haven't already, set up a Google Cloud Platform account. Navigate to the Google Cloud Console and create a new project. Ensure that you have enabled billing, as certain functionalities of Google Pub/Sub require it.
Within your GCP project, go to the Pub/Sub section and create a new topic. This topic will serve as the endpoint for the data you will be sending from Appfollow. Make note of the topic name as it will be needed later for publishing messages.
Using a programming language of your choice (such as Python), write a script that utilizes the Appfollow API to extract data. This script should authenticate with the Appfollow API using your credentials and specify the data you wish to retrieve. Make sure the script can handle API response parsing effectively.
Within the same script or a separate one, set up authentication for Google Pub/Sub. You will need to create a service account in GCP with Pub/Sub Publisher role, download its key file in JSON format, and use it to authenticate your script. Use Google Cloud Client Libraries to configure a Pub/Sub client within your script.
Enhance your script to take the data extracted from Appfollow and publish it to the Google Pub/Sub topic. Convert the data into a format suitable for Pub/Sub messages (typically JSON) and use the Pub/Sub client to send it to the topic you created earlier. Handle any exceptions or errors in the publishing process.
Run the script manually to ensure it works as expected by successfully transferring data from Appfollow to Google Pub/Sub. Once verified, set up a cron job or a similar scheduling mechanism to automate the execution of the script at regular intervals, ensuring continuous data flow from Appfollow to Google Pub/Sub.
By following these steps, you can effectively transfer data from Appfollow to Google Pub/Sub without relying on third-party connectors or integrations.
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 should you do next?
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