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Begin by logging into your AppsFlyer account. Navigate to the dashboard where your desired data resides. Use the AppsFlyer API to export the data. You can utilize the Pull API to extract raw data reports. Ensure you have the necessary API permissions and follow the API documentation to structure your request correctly. Save the extracted data locally in a CSV or JSON format.
Once you have exported the data, inspect it for any necessary transformations or cleaning. Check for duplicates, null values, or data types that may require conversion. Use Python or another scripting language to process and clean the data if necessary. Ensure the data is structured in a way that is compatible with Databricks Lakehouse schemas.
Access your Databricks account and create a new cluster if one isn’t already available. Ensure the cluster is running and has the necessary configurations to handle the incoming data. Install any required libraries or dependencies that might be necessary for loading data into Databricks from your local environment.
Before importing data into Databricks, upload the cleaned and prepared data to a cloud storage service that's accessible by Databricks, such as AWS S3, Azure Blob Storage, or Google Cloud Storage. Use the command-line interface or relevant SDKs of the cloud provider to upload your files securely.
In Databricks, mount the cloud storage location containing your data files. Use Databricks' built-in support for cloud storage services to establish a connection. This typically involves configuring a secure access path and using the appropriate credentials or access keys to authenticate the connection.
With the data accessible from your cloud storage, use Databricks' data import features to load the data into your Lakehouse environment. Use Spark SQL or Databricks’ DataFrame API to read the data from the cloud storage path. Perform any additional data transformations or validations as required during the loading process.
After loading the data, conduct a thorough verification to ensure that the data in Databricks Lakehouse matches the original data extracted from AppsFlyer. Run queries to check for data completeness, consistency, and integrity. Verify that all fields are properly mapped and that the data types are correct. Make any necessary adjustments or re-transformations if discrepancies are found.
By following these steps, you can effectively move your data from AppsFlyer to Databricks Lakehouse 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.
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:
