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To move data from Adjust to Google Sheets, you first need to access your data from Adjust using their API. Log into your Adjust account and navigate to the API section to obtain your API token and endpoint details needed for authentication and data retrieval.
Familiarize yourself with Adjust's API documentation. Identify the appropriate API endpoints that contain the data you want to export. Note the required parameters and response formats. This will help in crafting the correct API calls for your data.
Construct the API request URL using the endpoint and parameters identified in the previous step. Include your API token in the request headers or URL for authentication. Use a tool like Postman or a simple browser query to test your API request and ensure it returns the expected data.
Use a script to automate data extraction. You can write a script in Python, JavaScript, or any language that supports HTTP requests. The script should send a GET request to the Adjust API URL and capture the response data, which is typically in JSON format.
Once you have the JSON data from Adjust, parse it to extract relevant fields and format it into a tabular structure. This may involve converting JSON objects into arrays or lists that match the row and column format used in spreadsheets.
Open Google Sheets and use Google Apps Script to automate the import process. Go to "Extensions" > "Apps Script" and create a new project. Write a script that fetches data from your script's output (Step 4) and inserts it into the spreadsheet. Use the `SpreadsheetApp` service to manipulate sheets and cells.
To keep your Google Sheets updated with the latest data from Adjust, set up a time-driven trigger in Google Apps Script. Go to the Apps Script editor, click on the clock icon, and set a time-based trigger to run your data import function at regular intervals (e.g., daily or weekly).
By following these steps, you'll be able to move data from Adjust to Google Sheets effectively 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:
