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- Create a Google Cloud project if you don’t already have one.
- Enable billing for your project.
- Create a Google Cloud Storage bucket where you will store the exported data from AppsFlyer.
- Log in to your AppsFlyer account and navigate to the Data Locker setup.
- Configure Data Locker to export data to your Google Cloud Storage bucket. You will need to provide your bucket details and configure the data export schedule.
- Once Data Locker is configured, AppsFlyer will start exporting data to your Google Cloud Storage bucket according to your schedule.
- You will find the data in files, typically gzipped CSVs, in your bucket.
- Enable the Cloud Functions API for your project.
- Create a new Cloud Function that will trigger on the creation of new files in your Google Cloud Storage bucket.
- Write a function in your preferred runtime (Node.js, Python, etc.) that will:
- Trigger when new data is uploaded to your Cloud Storage bucket.
- Unzip the file if necessary.
- Parse the CSV data.
- Stream the data into BigQuery.
- Enable the BigQuery API for your project.
- Create a dataset in BigQuery where you will store your AppsFlyer data.
- Design your table schema based on the AppsFlyer data you will be importing.
- Implement the code to read the CSV file from the Cloud Storage bucket.
- Parse the CSV data into a format that BigQuery can ingest.
- Use the BigQuery client library to insert data into your BigQuery table.
- After writing and testing your function locally, deploy it to Google Cloud Functions.
- Ensure that the function is set to trigger when new files are added to your Cloud Storage bucket.
- Check the Cloud Function logs to make sure it’s being triggered correctly and that data is being processed without errors.
- Verify that data is appearing in your BigQuery table as expected.
If you need more control over when the data is transferred, you can set up a Cloud Scheduler job to trigger your Cloud Function at specific times.
- Monitor your setup to ensure it continues to work as expected.
- Optimize costs by managing the frequency of data transfers and the processing resources used.
- Update your Cloud Function and BigQuery schema as needed if your AppsFlyer data changes.
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.
AppsFlyer is a mobile attribution and marketing analytics platform that helps businesses measure and optimize their mobile app marketing campaigns. It provides real-time data and insights on user acquisition, engagement, retention, and revenue, allowing businesses to make data-driven decisions to improve their app performance and ROI. AppsFlyer's platform integrates with over 5,000 partners, including ad networks, social media platforms, and analytics tools, to provide a comprehensive view of the entire mobile app marketing ecosystem. With its advanced fraud protection and privacy compliance features, AppsFlyer ensures that businesses can trust their data and protect their users' privacy.
AppsFlyer's API provides access to a wide range of data related to mobile app marketing and user engagement. The following are the categories of data that can be accessed through the API:
1. Attribution data: This includes information about the source of app installs, such as the ad network, campaign, and creative.
2. In-app events data: This includes data about user actions within the app, such as purchases, registrations, and other custom events.
3. Retargeting data: This includes data about users who have engaged with the app in the past and can be targeted with specific campaigns.
4. Audience data: This includes data about the characteristics of app users, such as demographics, interests, and behaviors.
5. Ad revenue data: This includes data about the revenue generated by ads within the app, such as impressions, clicks, and conversions.
6. Fraud prevention data: This includes data about potential fraudulent activity, such as fake installs or clicks.
7. Raw data: This includes all of the above data in its raw form, allowing for custom analysis and reporting.
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?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
