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Familiarize yourself with Adjust's data export features. Adjust allows you to export raw data through their Data Export API. Review the API documentation provided by Adjust to understand the endpoints, required parameters, and authentication methods.
Log in to your AWS Management Console and create an S3 bucket where your data will be stored. Take note of the bucket name and configure access permissions to ensure that you can upload files. Ensure the bucket is in the desired AWS region and set appropriate security policies for data access.
To interact with Adjust's API, you'll need an API token. Log in to Adjust, navigate to the API section, and generate an API token if you haven't already. Ensure the token has the necessary permissions to access the required data.
Develop a script using a programming language of your choice (e.g., Python, Node.js) to call the Adjust Data Export API. Use the API token for authentication. Specify the data range and types of data you want to export. The script should handle pagination if Adjust's API returns data in batches.
Once you retrieve the data from Adjust, process it as needed. This might involve transforming the data format (e.g. JSON to CSV) or filtering certain fields. Ensure the data is in the desired format before uploading it to S3.
Use AWS SDK for the programming language you are using to upload the processed data to your S3 bucket. The SDK provides functions to authenticate and interact with S3. Ensure that your AWS credentials have the necessary permissions to upload files to the S3 bucket. Handle any errors that might occur during the upload process.
To regularly move data from Adjust to S3, automate the script execution using a scheduler. For example, use cron jobs on Linux or Task Scheduler on Windows to run the script at desired intervals. Monitor the logs to ensure the process runs smoothly and troubleshoot any issues promptly.
By following these steps, you can effectively transfer data from Adjust to Amazon S3 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.
Adjust is a favorite mobile attribution and deep-linking platform that makes mobile marketing easy. It is a mobile marketing analytics platform trusted by marketers around the world. This permits you to understand your users through attribution, giving you detailed insights into their journey and overall product experience. With a special focus on fraud prevention and data protection, Adjust also provides sophisticated app analytics capabilities to drive your project strategy and optimize your customer experience.
Adjust'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 Adjust's 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 related to user actions within the app, such as purchases, registrations, and other custom events.
3. User engagement data: This includes data related to user behavior within the app, such as session length, retention rate, and user churn.
4. Ad performance data: This includes data related to the performance of ad campaigns, such as impressions, clicks, and conversions.
5. Audience data: This includes data related to the demographics and behavior of app users, such as age, gender, location, and interests.
6. Fraud prevention data: This includes data related to the detection and prevention of fraudulent activity within the app, such as click spamming and install fraud.Overall, Adjust's API provides a comprehensive set of data that can be used to optimize mobile app marketing campaigns and improve 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?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
