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Begin by using AppsFlyer's data export capabilities. Navigate to the AppsFlyer dashboard and use the Data Locker or Reports API to manually extract the desired dataset in a CSV or JSON format. Ensure you have the necessary permissions and API access to perform this operation.
After extracting the data, inspect it for any inconsistencies or unwanted information. Use a tool like Python, Excel, or any preferred data cleaning software to transform and clean the data. This may involve formatting dates, handling missing values, or normalizing fields to ensure compatibility with Starburst Galaxy.
Define the schema of your data for Starburst Galaxy. This involves outlining how your data will be structured, including data types for each field and any necessary indexing. Create a mapping document that details how each field in the AppsFlyer data corresponds to the fields in Starburst Galaxy.
Access Starburst Galaxy by logging into your account. Ensure you have the necessary permissions to create tables and load data. Familiarize yourself with the SQL interface of Starburst Galaxy, as you will use it to create tables and import the data.
Using the schema defined in Step 3, write SQL commands in Starburst Galaxy to create the appropriate tables. Ensure that the column names and data types match those of your cleaned data. Use the Starburst Galaxy console to execute these SQL commands and verify that tables are created successfully.
Manually load the cleaned data files into Starburst Galaxy. You can do this by using the SQL `COPY` command or by uploading files directly through the interface, depending on the size and format of your data. Ensure that the data aligns with the table structure created in the previous step.
After loading the data, perform a series of checks to verify that the data has been accurately transferred. Run sample queries to ensure data integrity and consistency. Verify that there are no discrepancies between the data in AppsFlyer and Starburst Galaxy. Adjust any anomalies by revisiting previous steps if necessary.
Following these steps will enable you to manually transfer data from AppsFlyer to Starburst Galaxy without the use of 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:
